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Matches in ScholarlyData for { ?s <https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstract> ?o. }

• paper-17 abstract "Ontology and other logical languages are built around the idea that axioms enable the inference of new facts about the available data. In some circumstances, however, the data is meant to be complete in certain ways, and deducing new facts may be undesirable. Previous approaches to this issue have relied on syntactically specifying certain axioms as constraints or adding in new constructs for constraints, and providing a different or extended meaning for constraints that reduces or eliminates their ability to infer new facts without requiring the data to be complete. We propose to instead directly state that the extension of certain concepts and roles are complete by making them DBox predicates, which eliminates the distinction between regular axioms and constraints for these concepts and roles. This proposal eliminates the need for special semantics and avoids problems of previous proposals.".
• paper-18 abstract "First order logic (FOL) rewritability is a desirable feature for query answering over geo-thematic ontologies because in most geoprocessing scenarios one has to cope with large data volumes. Hence, there is a need for combined geo-thematic logics that provide a sufficiently expressive query language allowing for FOL rewritability. The DL-Lite family of description logics is tailored towards FOL rewritability of query answering for unions of conjunctive queries, hence it is a suitable candidate for the thematic component of a combined geo-thematic logic. We show that a weak coupling of DL-Lite with the expressive region connection calculus RCC8 allows for FOL rewritability under a spatial completeness condition for the ABox. Stronger couplings allowing for FOL rewritability are possible only for spatial calculi as weak as the low-resolution calculus RCC2. Already a strong combination of DL-Lite with the low-resolution calculus RCC3 does not allow for FOL rewritability.".
• paper-19 abstract "Data provenance is the history of derivation of a data artifact from its original sources. As the real-life provenance records can likely cover thousands of data items and derivation steps, one of the pressing challenges becomes development of formal frameworks for their automated verification. In this paper, we consider data expressed in standard Semantic Web ontology languages, such as OWL, and define a novel verification formalism called provenance specification logic, building on dynamic logic. We validate our proposal by modeling the test queries presented in The First Provenance Challenge, and conclude that the logic core of such queries can be successfully captured in our formalism.".
• paper-20 abstract "Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models.".
• paper-21 abstract "One of the main tasks when creating and maintaining knowledge bases is to validate facts and provide sources for them in order to ensure correctness and traceability of the provided knowledge. So far, this task is often addressed by human curators in a three-step process: issuing appropriate keyword queries for the statement to check using standard search engines, retrieving potentially relevant documents and screening those documents for relevant content. The drawbacks of this process are manifold. Most importantly, it is very time-consuming as the experts have to carry out several search processes and must often read several documents. In this article, we present DeFacto (Deep Fact Validation) - an algorithm for validating facts by finding trustworthy sources for it on the Web. DeFacto aims to provide an effective way of validating facts by supplying the user with relevant excerpts of webpages as well as useful additional information including a score for the confidence DeFacto has in the correctness of the input fact.".
• paper-22 abstract "The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to the same real-world object. The problem of discovering owl:sameAs links between pairwise data sources is called instance matching. Most of the existing approaches addressing this problem rely on the quality of prior schema matching, which is not always good enough in the LOD scenario. In this paper, we propose a schema-independent instance-pair similarity metric based on several general descriptive features. We transform the instance matching problem to the binary classification problem and solve it by machine learning algorithms. Furthermore, we employ some transfer learning methods to utilize the existing owl:sameAs links in LOD to reduce the demand for labeled data. We carry out experiments on some datasets of OAEI2010. The results show that our method performs well on real-world LOD data and outperforms the participants of OAEI2010.".
• paper-23 abstract "In this paper, we describe a mechanism for ontology alignment using instance based matching of types (or classes). Instance-based matching is known to be a useful technique for matching ontologies that have different names and different structures. A key problem in instance matching of types, however, is scaling the matching algorithm to (a) handle types with a large number of instances, and (b) efficiently match a large number of type pairs. We propose the use of state-of-the art locality-sensitive hashing (LSH) techniques to vastly improve the scalability of instance matching across multiple types. We show the feasibility of our approach with DBpedia and Freebase, two different type systems with hundreds and thousands of types, respectively. We describe how these techniques can be used to estimate containment or equivalence relations between two type systems, and we compare two different LSH techniques for computing instance similarity.".
• paper-24 abstract "In RDF, a blank node (or anonymous resource or bnode) is a node in an RDF graph which is not identified by a URI and is not a literal. Several RDF/S Knowledge Bases (KBs) rely heavily on blank nodes as they are convenient for representing complex attributes or resources whose identity is unknown but their attributes (either literals or associations with other resources) are known. In this paper we show how we can exploit blank nodes anonymity in order to reduce the delta (diff) size when comparing such KBs. The main idea of the proposed method is to build a mapping between the bnodes of the compared KBs for reducing the delta size. We prove that finding the optimal mapping is NP-Hard in the general case, and polynomial in case there are not directly connected bnodes. Subsequently we present various polynomial algorithms returning approximate solutions for the general case. For making the application of our method feasible also to large KBs we present a signature-based mapping algorithm with n logn complexity. Finally, we report experimental results over real and synthetic datasets that demonstrate significant reductions in the sizes of the computed deltas. For the proposed algorithms we also provide comparative results regarding delta reduction, equivalence detection and time efficiency.".
• paper-25 abstract "We present Strabon, a new RDF store that supports the state of the art semantic geospatial query languages stSPARQL and GeoSPARQL. To illustrate the expressive power offered by these query languages and their implementation in Strabon, we concentrate on the new version of the data model stRDF and the query language stSPARQL that we have developed ourselves. Like GeoSPARQL, these new versions use OGC standards to represent geometries where the original versions used linear constraints. We study the performance of Strabon experimentally and show that it scales to very large data volumes and performs, most of the times, better than all other geospatial RDF stores it has been compared with.".
• paper-26 abstract "The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception - explanation and discrimination - and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale.".
• paper-27 abstract "We introduce SRBench, a general-purpose benchmark primarily designed for streaming RDF/SPARQL engines, completely based on real-world data sets from the Linked Open Data cloud. With the increasing problem of too much streaming data but not enough tools to gain knowledge from them, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for publishing, sharing, analysing and understanding streaming data. To help researchers and users comparing streaming RDF/SPARQL (strRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a strRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The data sets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a concise, yet comprehensive set of queries that cover the major aspects of strRS processing. Finally, our work is complemented with a functional evaluation on three representative strRS engines: SPARQLStream, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art.".
• paper-28 abstract "Despite the increase in the number of linked instances in the Linked Data Cloud in recent times, the absence of links at the concept level has resulted in heterogenous schemas, challenging the interoperability goal of the Semantic Web. In this paper, we address this problem by finding alignments between concepts from multiple Linked Data sources. Instead of only considering the existing concepts present in each ontology, we hypothesize new composite concepts defined as disjunctions of conjunctions of (RDF) types and value restrictions, which we call restriction classes, and generate alignments between these composite concepts. This extended concept language enables us to find more complete definitions and to even align sources that have rudimentary ontologies, such as those that are simple renderings of relational databases. Our concept alignment approach is based on analyzing the extensions of these concepts and their linked instances. Having explored the alignment of conjunctive concepts in our previous work, in this paper, we focus on concept coverings (disjunctions of restriction classes). We present an evaluation of this new algorithm to Geospatial, Biological Classification, and Genetics domains. The resulting alignments are useful for refining existing ontologies and determining the alignments between concepts in the ontologies, thus increasing the interoperability in the Linked Open Data Cloud.".
• paper-29 abstract "Ontology mappings are often assigned a weight or confidence factor by matchers. Nonetheless, few semantic accounts have been given so far for such weights. This paper presents a formal semantics for weighted mappings between different ontologies. It is based on a classificational interpretation of mappings: if O1 and O2 are two ontologies used to classify a common set X , then mappings between O1 and O2 are interpreted to encode how elements of X classified in the concepts of O1 are re-classified in the concepts of O2, and weights are interpreted to measure how precise and complete re-classifications are. This semantics is justifiable by extensional practice of ontology matching. It is a conservative extension of a semantics of crisp mappings. The paper also includes properties that relate mapping entailment with description logic constructors.".
• paper-30 abstract "The inherent heterogeneity of datasets on the Semantic Web has created a need to interlink them, and several tools have emerged that automate this task. In this paper we are interested in what happens if we enrich these matching tools with knowledge of the domain of the ontologies. We explore how to express the notion of a domain in terms usable for an ontology matching tool, and we examine various methods to decide what constitutes the domain of a given dataset. We show how we can use this in a matching tool, and study the effect of domain knowledge on the quality of the alignment. We perform evaluations for two scenarios: Last.fm artists and UMLS medical terms. To quantify the added value of domain knowledge, we compare our domain-aware matching approach to a standard approach based on a manually created reference alignment. The results indicate that the proposed domain-aware approach indeed outperforms the standard approach, with a large effect on ambiguous concepts but a much smaller effect on unambiguous concepts.".
• paper-31 abstract "We describe a system that incrementally translates SPARQL queries to Pig Latin and executes them on a Hadoop cluster. This system is designed to work efficiently on complex queries with many self-joins over huge datasets, avoiding job failures even in the case of joins with unexpected high-value skew. To be robust against cost estimation errors, our system interleaves query optimization with query execution, determining the next steps to take based on data samples and statistics gathered during the previous step. Furthermore, we have developed a novel skew-resistant join algorithm that replicates tuples corresponding to popular keys. We evaluate the effectiveness of our approach both on a synthetic benchmark known to generate complex queries (BSBM-BI) as well as on a Yahoo! case of data analysis using RDF data crawled from the web. Our results indicate that our system is indeed capable of processing huge datasets without pre-computed statistics while exhibiting good load-balancing properties.".
• paper-32 abstract "The distributed and heterogeneous nature of Linked Open Data requires flexible and federated techniques for query evaluation. In order to evaluate current federation querying approaches a general methodology for conducting benchmarks is mandatory. In this paper, we present a classification methodology for federated SPARQL queries. This methodology can be used by developers of federated querying approaches to compose a set of test benchmarks that cover diverse characteristics of different queries and allows for comparability. We further develop a heuristic called SPLODGE for automatic generation of benchmark queries that is based on this methodology and takes into account the number of sources to be queried and several complexity parameters. We evaluate the adequacy of our methodology and the query generation strategy by applying them on the 2011 billion triple challenge data set.".
• paper-33 abstract "Recent developments in hardware have shown an increase in parallelism as opposed to clock rates. In order to fully exploit these new avenues of performance improvement, computationally expensive workloads have to be expressed in a way that allows for fine-grained parallelism. In this paper, we address the problem of describing RDFS entailment in such a way. Different from previous work on parallel RDFS reasoning, we assume a shared memory architecture. We analyze the problem of duplicates that naturally occur in RDFS reasoning and develop strategies towards its mitigation, exploiting all levels of our architecture. We implement and evaluate our approach on two real-world datasets and study its performance characteristics on different levels of parallelization. We conclude that RDFS entailment lends itself well to parallelization but can benefit even more from careful optimizations that take into account intricacies of modern parallel hardware.".
• paper-34 abstract "Three conflicting requirements arise in the context of knowledge base (KB) extraction: the size of the extracted KB, the size of the corresponding signature and the syntactic similarity of the extracted KB with the original one. Minimal module extraction and uniform interpolation assign an absolute priority to one of these requirements, thereby limiting the possibilities to influence the other two. We propose a novel technique for EL that does not require such an extreme prioritization. We propose a tractable rewriting approach and empirically compare the technique with existing approaches with encouraging results.".
• paper-35 abstract "Due to the high worst case complexity of the core reasoning problem for the expressive profiles of OWL 2, ontology engineers are often surprised and confused by the performance behaviour of reasoners on their ontologies. Even very experienced modellers with a sophisticated grasp of reasoning algorithms do not have a good mental model of reasoner performance behaviour. Seemingly innocuous changes to an OWL ontology can degrade classification time from instantaneous to too long to wait for. Similarly, switching reasoners (e.g., to take advantage of specific features) can result in wildly different classification times. In this paper we investigate performance variability phenomena in OWL ontologies, and present methods to identify subsets of an ontology which are performance-degrading for a given reasoner. When such (ideally small) subsets are removed from an ontology, and the remainder is much easier for the given reasoner to reason over, we designate them “hot spotsâ€?. The identification of these hot spots allows users to isolate difficult portions of the ontology in a principled and systematic way. Moreover, we devise and compare various methods for approximate reasoning and knowledge compilation based on hot spots. We verify our techniques with a select set of varyingly difficult ontologies from the NCBO BioPortal, and were able to, firstly, successfully identify performance hot spots against the major freely available DL reasoners, and, secondly, significantly improve classification time using approximate reasoning based on hot spots.".
• paper-36 abstract "A key issue in semantic reasoning is the computational complexity of inference tasks on expressive ontology languages such as OWL DL and OWL 2 DL. Theoretical works have established worst-case complexity results for reasoning tasks for these languages. However, hardness of reasoning about individual ontologies has not been adequately characterised. In this paper, we conduct a systematic study to tackle this problem using machine learning techniques, covering over 350 real-world ontologies and four state-of-the-art, widely-used OWL 2 reasoners. Our main contributions are two-fold. Firstly, we learn various classifiers that accurately predict classification time for an ontology based on its metric values. Secondly, we identify a number of metrics that can be used to effectively predict reasoning performance. Our prediction models have been shown to be highly effective, achieving an accuracy of over 80%.".
• paper-37 abstract "This paper presents an approach to automatically extract entities and relationships from textual documents. The main goal is to populate a knowledge base that hosts this structured information about domain entities. The extracted entities and their expected relationships are verified using two evidence based techniques: classification and linking. This last process also enables the linking of our knowledge base to other sources which are part of the Linked Open Data cloud. We demonstrate the benefit of our approach through series of experiments with real-world datasets.".
• paper-38 abstract "We present a large-scale relation extraction (RE) system which learns grammar-based RE rules from the Web by utilizing large numbers of relation instances as seed. Our goal is to obtain rule sets large enough to cover the actual range of linguistic variation, thus tackling the long-tail problem of real-world applications. A variant of distant supervision learns several relations in parallel, enabling a new method of rule filtering. The system detects both binary and n-ary relations. We target 39 relations from Freebase, for which 3M sentences extracted from 20M web pages serve as the basis for learning an average of 40K distinctive rules per relation. Employing an efficient dependency parser, the average run time for each relation is only 19 hours. We compare these rules with ones learned from local corpora of different sizes and demonstrate that the Web is indeed needed for a good coverage of linguistic variation.".
• paper-39 abstract "For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the finegrained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore they tend not to cover the most recent research trends. In addition, as we move towards a semantic characterization of these relations, there is arguably a need for a more sophisticated characterization than a homogeneous taxonomy, to reflect the different ways in which research areas can be related. In this paper we propose Klink, a new approach to i) automatically generating relations between research areas and ii) populating a bibliographic ontology, which combines both machine learning methods and external knowledge, which is drawn from a number of resources, including Google Scholar and Wikipedia. We have tested a number of alternative algorithms and our evaluation shows that a method relying on both external knowledge and the ability to detect temporal relations between research areas performs best with respect to a manually constructed standard.".
• paper-40 abstract "Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. ''Apple product'') as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.".
• paper-41 abstract "The last decade of research in ontology alignment has brought a variety of computational techniques to discover correspondences between ontologies. While the accuracy of automatic approaches has continuously improved, human contributions remain a key ingredient of the process: this input serves as a valuable source of domain knowledge that is used to train the algorithms and to validate and augment automatically computed alignments. In this paper, we introduce CROWDMAP, a model to acquire such human contributions via microtask crowdsourcing. For a given pair of ontologies, CROWDMAP translates the alignment problem into microtasks that address individual alignment questions, publishes the microtasks on an online labor market, and evaluates the quality of the results obtained from the crowd. We evaluated the current implementation of CROWDMAP in a series of experiments using ontologies and reference alignments from the Ontology Alignment Evaluation Initiative and the crowdsourcing platform CrowdFlower. The experiments clearly demonstrated that the overall approach is feasible, and can improve the accuracy of existing ontology alignment solutions in a fast, scalable, and cost-effective manner.".
• paper-01 abstract "A class of search queries which contain abstract concepts are studied in this paper. These queries cannot be correctly interpreted by traditional keyword-based search engines. This paper presents a simple framework that detects and instantiates the abstract concepts by their concrete entities or meanings to produce alternate queries that yield better search results.".
• paper-02 abstract "A typical software engineer spends a significant amount of time and effort reading technical manuals to find answers to questions especially those related to features, versions, compatibilities and dependencies of software and hardware components, languages, standards, modules, libraries and products. It is currently not possible to provide a semantic solution to their problem primarily due to the non-availability of comprehensive semantic datasets in the domains of information technology. In this work, we have extracted, integrated and curated a linked open dataset (LOD) called LOaD-IT exclusively on this domain from a variety of sources including other LODs such as Freebase and DBPedia, technical documentation such as JavaDocs and others. Further, we have built a technical helpdesk system using a semantic query engine that derives answers from LOaD-IT. Our system demonstrates how productivity of the software engineer can be improved by eliminating the need to read through lengthy technical manuals. We expect LOaD-IT to become more comprehensive in the future and to find other related practical applications.".
• paper-03 abstract "Searching and browsing relationships between entities is an important task in many domains. To support users in interactively exploring a large set of relationships, we present a novel relationship search engine called RelClus, which automatically groups search results into a dynamically generated hierarchy with meaningful labels. This hierarchical clustering of relationships exploits their schematic patterns and a similarity measure based on information theory.".
• paper-04 abstract "In this paper we introduce DiTTO, an online service that allows one to convert a E/R diagram created through the yEd diagram editor into a proper OWL ontology according to three different conversion strategies.".
• paper-05 abstract "The Optique project aims at developing an end-to-end system for semantic data access to Big Data in industries such as Statoil ASA and Siemens AG. In our demonstration we present the first version of the Optique system customised for the Norwegian Petroleum Directorate's FactPages, a public data available for engineers at Statoil ASA. The system provides different options, including visual, to formulate queries over ontologies and to display query answers. Optique~1.0 offers two installation wizards that allow to extract ontologies from relational schemas, extract and define mappings connecting ontologies and schemas, and align and approximate ontologies. Moreover, the system offers tools to edit these components and highly optimised techniques for query answering.".
• paper-06 abstract "This paper presents an association discovery framework for proteins based on semantic annotations from biomedical literature. An automatic ontology-based annotation method is used to create a semantic protein annotation knowledge base. A semantic reasoning service enables realisation reasoning on original annotations to infer more accurate associations and executes semantic query transformation. A case study on protein-disease association discovery on a real-world colorectal cancer dataset is presented.".
• paper-07 abstract "QAKiS, a system for open domain Question Answering over linked data, allows to query DBpedia multilingual chapters with natural language questions. But since such chapters can contain different information w.r.t. the English version (e.g. more specificity on certain topics, or fill information gaps), i) different results can be obtained for the same query, and ii) the combination of these query results may lead to inconsistent information about the same topic. To reconcile information obtainedby distributed SPARQL endpoints, an argumentation-based module is integrated into QAKiS to reason over inconsistent information sets, and to provide a unique and motivated answer to the user.".
• paper-08 abstract "Our purpose is to provide end-users a means to query ontology based knowledge bases using natural language queries and thus hide the complexity of formulating a query expressed in a graph query language such as SPARQL. The main originality of our approach lies in the use of query patterns. Our contribution is materialized in a system named SWIP, standing for Semantic Web Interface Using Patterns. The demo will present use cases of this system.".
• paper-09 abstract "Semantic web applications are integrating data from more and more different types of sources about events. However, most data annotation frameworks do not translate well to semantic web. We present the grounded annotation framework (GAF), a two-layered framework that aims to build a bridge between mentions of events in a data source such as a text document and their formal representation as instance}. By choosing a two-layered approach, neither the mention layer, nor the semantic layer needs to compromise on what can be represented. We demonstrate the strengths of GAF in flexibility and reasoning through a use case on earthquakes in Southeast Asia.".
• paper-10 abstract "Taxonomy classification and query answering are the core reasoning services provided by most of the Semantic Web (SW) reasoners. However, the algorithms used by those reasoners are based on Tableau method or Rules. These well-known methods in the literature have already shown their limitations for large-scale reasoning.In this demonstration, we shall present the CEDAR system for classifying and reasoning on very large taxonomies using a technique based on lattice operations. This technique makes the CEDAR reasoner perform on a par with the best systems for concept classification and several orders-of-magnitude more efficiently in terms of response time for query-answering. The experiments were carried out using very large taxonomies (Wikipedia: 111599 sorts, MESH: 286381 sorts, NCBI: 903617 sorts and Biomodels: 182651 sorts). The results achieved by CEDAR were compared to those obtained by well-known Semantic Web reasoners, namely FaCT++, Pellet, HermiT, TrOWL, SnoRocket and RacerPro.".
• paper-11 abstract "Linked Data applications often assume that connectivity to data repositories and entity resolution services are always available. This may not be a valid assumption in many cases. Indeed, there are about 4.5 billion people in the world who have no or limited Web access. Many data-driven applications may have a critical impact on the life of those people, but are inaccessible to those populations due to the architecture of today's data registries. In this demonstration, we show a new open-source system that can be used as a general-purpose entity registry suitable for deployment in poorly-connected or ad-hoc environments.".
• paper-12 abstract "We present NoHR, a Protege plug-in that allows the user to take an EL ontology, add a set of non-monotonic (logic programming) rules - suitable e.g. to express defaults and exceptions - and query the combined knowledge base. Provided the given ontology alone is consistent, the system is capable of dealing with potential inconsistencies between the ontology and the rules, and, after an initial brief pre-processing period utilizing OWL 2 EL reasoner ELK, returns answers to queries at an interactive response time by means of XSB Prolog.".
• paper-13 abstract "The W3C Relational Database to RDF (RDB2RDF) standards are positioned to bridge the gap between Relational Databases and the Semantic Web. The standards consist of two interrelated and complementary specifications: “Direct Mapping of Relational Data to RDF” and “R2RML: RDB to RDF Mapping Language”. In this paper we present initial results on the formal study of the R2RML mapping language by defining its semantics using Datalog. We prove that there are a total of 57 distinct Datalog rules which can be used to generate RDF triples from a relational table. Additionally, we provide insights on the relationship between R2RML and Direct Mapping.".
• paper-14 abstract "Formal concept analysis (FCA) is used for knowledge discovery within data. In FCA, concept lattices are very good tools for classification and organization of data, hence, they enable the user to visualize the answers of its SPARQL query as concept lattices instead of the usual answer formats such as: RDF/XML, JSON, CSV, and HTML. Consequently, in this work, we apply FCA to reveal hidden relations within SPARQL query answers by means of concept lattices.".
• paper-15 abstract "The description logic $\mathcal{EL}$ has been used to support ontology design in various domains, and especially in biology and medecine. $\mathcal{EL}$ is known for its efficient reasoning and query answering capabilities. By contrast, ontology design and query answering can be supported and guided within an FCA framework. Accordingly, in this paper, we propose a formal transformation of $\mathcal{ELI}$ (an extension of $\mathcal{EL}$ with \textit{inverse roles}) ontologies into an FCA framework, i.e. $K_\mathrm{\mathcal{ELI}}$, and we provide a formal characterization of this transformation. Then we show that SPARQL query answering over $\mathcal{ELI}$ ontologies can be reduced to lattice query answering over $K_\mathrm{\mathcal{ELI}}$ concept lattices. This simplifies the query answering task and shows that some basic semantic web tasks can be improved when considered from an FCA perspective.".
• paper-16 abstract "This paper describes a work in progress that explores the applicability of ontology for providing solutions in medical domain. We investigate whether it is feasible to use ontologies and ontology-based data access to automate one of common clinical tasks that are constantly faced by general practitioners but labor intensive and error prone in term of relevant information retrieved from electronic health records. The focus of our study is on improving diabetes patient selection for clinical trials or medical research. The biggest impediment to automating such clinical tasks is the essential requirement of bridging the semantic gaps between existing patient data from electronic health records, such as reasons for visit, chronic conditions and diagnoses from practice notes, pathology tests and prescriptions stored in general practice information systems, and the ways which researchers or general practitioners interpret those records. Our current comprehension is that automation of identifying diabetes patients for clinical or research purposes can be specified systematically as a solution supported by semantic retrieval. We detail the challenges to build a realistic case study, which consists of solving issues related to conceptualization of data and domain context, integration of different datasets, ontology creation based on SNOMED CT-AU® standard, mapping between existing data and ontology, and dilemma of data fitness for research use. Our prototype is based on data which scale to thirteen years of approximate 100,000 anonymous patient records from four general practices in south western Sydney.".
• paper-17 abstract "Web tables comprise a rich source of factual information.However, without semantic annotation of the tables’ content the infor-mation is not usable for automatic integration and search. We propose amethodology to annotate table headers with semantic type informationbased on the content of column’s cells. In our experiments on 50 tableswe achieved an F1 value of 0.55, where the accuracy greatly varies de-pending on the used ontology. Regarding computational complexity wefound out that 94% of the maximal F1 score on average 20 cells (37%)need to be considered. Results suggest that the choice of the ontologyplays a more crucial role for type inference than the number of cells used.".
• paper-18 abstract "We demo an online system that tracks the availability of over four-hundred public SPARQL endpoints and makes up-to-date results available to the public. Our demo currently focuses on how often an endpoint is online/offline, but we plan to extend the system to collect metrics about available meta-data descriptions, SPARQL features supported, and performance for generic queries.".
• paper-19 abstract "This demo paper describes a real-time passenger information system based on citizen sensing and linked data.".
• paper-20 abstract "Research information is widely available on the Web. Both as peer-reviewed research publications or as resources shared via (micro)blogging platforms or other Social Media. Usually the platforms supporting this information exchange have an API that allows access to the structured content. This opens a new way to search and explore research information. In this paper, we present an approach that visualizes interactively an aligned knowledge base of these resources. We show that visualizing resources, such as conferences, publications and proceedings, expose affnities between researchers and those resources. We characterize each affinity, between researchers and resources, by the amount of shared interests and other commonalities.".
• paper-21 abstract "This poster proposes a minimal, backward compatible and combinable restful interface for RDF Stream Engine.".
• paper-22 abstract "TripleRush is a parallel in-memory triple store designed to address the need for efficient graph stores that answer queries over large-scale graph data fast. To that end it leverages a novel, graph-based architecture. Specifically, TripleRush is built on our parallel and distributed graph processing framework Signal/Collect. The index structure is represented as a graph where each index vertex corresponds to a triple pattern. Partially matched queries are routed in parallel along different paths of this index structure. We show experimentally that TripleRush takes about a third of the time to answer queries compared to the fastest of three state-of-the-art triple stores, when measuring time as the geometric mean of all queries for two common benchmarks.".
• paper-23 abstract "Tables are widely used in Wikipedia articles to display relational information – they are inherently concise and information rich. However, aside from info-boxes, there are no automatic methods to exploit the integrated content of these tables. We thus present DRETa: a tool that uses DBpedia as a reference knowledge-base to extract RDF triples from generic Wikipedia tables.".
• paper-24 abstract "Linked Data resources change rapidly over time, making a valid consistent state difficult. As a solution, the Memento framework offers content negotiation in the datetime dimension. However, due to a lack of formally described versioning, every server needs a costly custom implementation. In this poster paper, we exploit published provenance of Linked Data resources to implement a generic Memento servics. Based on the w3c prov standard, we propose a loosely coupled architecture that offers a Memento interface to any Linked Data service publishing provenance.".
• paper-25 abstract "Semantic Web ontologies are fast-growing knowledge sources on the Web. Searching relevant concepts from this large repository is a challenging problem. The current Semantic Web search engines provide either (1) coarse-grained search over ontologies or (2) very fine-grained search over individuals. We believe searching and ranking concepts across ontologies provides an ideal granularity for certain tasks such as ontology population and web page annotation. Towards this objective, we propose a novel approach of indexing concepts using ontology axioms in an inverted file structure and ranking them using a dynamic ranking algorithm. Our proposed method is generic and domain-independent. A preliminary evaluation indicates that our proposed method is effective, outperforming the search function of BioPortal, a large and widely-used ontology repository.".
• paper-26 abstract "The Web of Data (WoD) continues to grow steadily each year. At over 31 billion triples in 2011, querying this globally distributed data space poses several scalability challenges. One critical aspect when processing distributed SPARQL queries is given by the number and type of distributed joins needed. Traditionally, query optimizers alleviate this issue by attempting to find an optimal query plan assuming a given and fixed data distribution. Discarding this fixed data partitioning assumption, offers the opportunity to create a data distribution that minimizes the number of distributed joins. Recent research focused on data- and query-driven partitioning strategies for both RDF and relational data. In this paper we propose a novel and naive workload-driven approach to data partitioning and investigate the impact of various critical factors on the number of resulting distributed joins. In a preliminary experiment we empirically compare our method to traditional partitioning strategies using a DBpedia query log of 400’000 queries and observe that it can produce up to 50% less distributed joins than an expert (manual) partitioning scheme, 45% less than partitioning based on hashing by subject and up to 83% less distributed joins than just random assignment.".
• paper-27 abstract "Software development communities use different communication channels such as mailing lists, forums and bug tracking systems. These channels are not integrated which makes finding information difficult and inefficient. As a result of the ALERT project we developed a system that is able to collect and annotate information from various communication channels and store it in a single knowledge base. Using the stored knowledge the system can provide users valuable functionalities such as semantic search, finding potential bug duplicates, custom notifications and issue recommendations.".
• paper-28 abstract "The new W3C standard R2RML\footnote{See: http://www.w3.org/TR/r2rml/} defines a language for expressing mappings from relational databases to RDF, allowing applications built on top of the W3C Semantic Technology stack to seamlessly integrate relational data. A major obstacle in using R2RML, though, is the creation and maintenance of mappings. In this demo, we present a novel R2RML mapping editor, which provides a user interface to create and edit mappings interactively. Hiding the R2RML vocabulary intricacies from the user, the editor enables even non-experts to create R2RML mappings in a guided way, offers immediate feedback by means of integrated preview functionality, and covers all the major language constructs defined in the R2RML standard.".
• paper-29 abstract "Ontology Based Data Access (OBDA) enables access to relational data with a complex structure through ontologies as conceptual domain models. A key component of an OBDA system are mappings between the schematic elements in the ontology and their correspondences in the relational schema. Today, in existing OBDA systems these mappings typically need to be compiled by hand. In this paper we present IncMap, a system that supports a semiautomatic approach for matching relational schemata and ontologies. Our approach is based on a novel matching technique that represents the schematic elements of an ontology and a relational schema in a unified way. Finally, IncMap can extend user-verified mapping suggestions in a pay as you go fashion.".
• paper-30 abstract "According to the Linked Data principles, a tripleset should be interlinked with others to take advantage of existing knowledge. However, interlinking is a laborious task. Thus, users interlink their triplesets mostly with data hubs, such as DBpedia and Freebase, ignoring the more specic yet often even more promising triplesets. To alleviate this problem, this paper describes a tripleset interlinking recommendation tool based on link prediction techniques and evaluates the tool on a real-world tripleset repository.".
• paper-31 abstract "Cite4Me is a Web application that leverages Semantic Web technologies to provide a new perspective on search and retrieval of bibliographical data. The Web application presented in this work focuses on: (i) semantic recommendation of papers; (ii) novel semantic search & retrieval of papers; (iii) data interlinking of bibliographical data with related data sources from LOD; (iv) innovative user interface design; and (v) sentiment analysis of extracted paper citations. Finally, as this work also targets some educational aspects, our application provides an in-depth analysis of the data that guides a user on his research field.".
• paper-32 abstract "A distributed reasoning platform is presented to reduce the energy consumption of Wireless Sensor Networks (WSNs) offering geospatial services by minimizing the amount of wireless communication. It combines local, rule-based reasoning on the sensors and gateways with global, ontology-based reasoning on the back-end servers. The Semantic Sensor Network (SNN) Ontology was extended to model the WSN energy consumption. Two prototypes are presented: the Personal Parking Assistant (PPA) and Garbage Bin Tampering Monitor (GBTM).".
• paper-33 abstract "This study proposes a novel method with which Chinese, Japanese, and Korean (CJK) resources on the Web can be effectively matched and connected. The three countries share Chinese characters even though Japan and Korea have their own language. Utilizing the Unihan database, which covers more than 45,000 characters commonly used by the three countries, we show that the proposed method outperforms the traditional method based on string matching in finding similar characters and words used in these countries. The results represent a first step towards overcoming the multilingual barrier in semantically interlinking Asian LOD resources.".
• paper-34 abstract "The details of reasoning in RDF are generally well known. The model-theoretic characteristcs of RDF have been less studied, particularly when datatypes are added. RDF reasoning can be performed by only considering finite models or pre-models, and sometimes only very small models need be considered.".
• paper-35 abstract "Although reaching the fifth star of the Open Data deployment scheme demands the data to be represented in RDF and linked, a generic and standard mapping procedure to deploy raw data in RDF was not established so far. Only the R2RML mapping language was standardized but its applicability is limited to mappings from relational databases to RDF. We propose the extension of R2RML to also support mappings of data sources in other structured formats. Broadening its scope, the focus is put on the mappings and their optimal reuse. The language becomes source-agnostic, and resources are integrated and interlinked at a primary stage.".
• paper-36 abstract "In this demo paper, we present the first ontology-based Vietnamese question answeringsystem KbQAS in which a knowledge acquisition approach for question analysis is integrated.".
• paper-37 abstract "In this paper, we discuss PigSPARQL, a competitive, yet easy to use, SPARQL query processing system based on MapReduce and thus intended for Big Data applications. Instead of a direct mapping, PigSPARQL uses the query language of Pig, a data analysis platform on top of Hadoop, as an intermediate layer between SPARQL and MapReduce. The additional level of abstraction makes our approach independent of the actual Hadoop version. Thus, it is automatically compatible to future changes of the Hadoop framework as they will be neutralized by the Pig layer and allows ad-hoc SPARQL query processing on large RDF graphs out of the box. In the paper we first revisit PigSPARQL and demonstrate PigSPARQL's gain of efficiency simply because switching from version Pig 0.5.0 to Pig 0.11.0. Because of this sustainability, PigSPARQL is an attractive long-term baseline for comparing various MapReduce based SPARQL implementations. This is underlined by PigSPARQL's competitiveness with existing systems, e.g. HadoopRDF.".
• paper-38 abstract "Accessing Linked Open Data sources with query languages such as SPARQL provides more flexible possibilities than access based on derefencerable URIs only. However, discovering a SPARQL endpoint on the fly, given a URI, is not trivial. This paper provides a quantitative analysis on the automatic discoverability of SPARQL endpoints using different mechanisms.".
• paper-39 abstract "While there exists an increasingly large number of Linked Data, metadata about the content covered by individual datasets is sparse. In this paper, we introduce a processing pipeline to automatically assess, annotate and index available linked datasets. Given a minimal description of a dataset from the DataHub, the process produces a structured RDF-based description that includes information about its main topics. Additionally, the generated descriptions embed datasets into an interlinked graph of datasets based on shared topic vocabularies. We adopt and integrate techniques for Named Entity Recognition and automated data validation, providing a consistent workflow for dataset profiling and annotation. Finally, we validate the results obtained with our tool.".
• paper-40 abstract "As a massive linked open data is available in RDF, the scalable storage and efficient retrieval using MapReduce have been actively studied. Most of previous researches focus on reducing the number of MapReduce jobs for processing join operations in SPARQL queries. However, the cost of shuffle phase still occurs due to their reduce-side joins. In this paper, we propose RDFChain which supports the scalable storage and efficient retrieval of a large volume of RDF data using a combination of MapReduce and HBase which is NoSQL storage system. Since the proposed storage schema of RDFChain reflects all the possible join patterns of queries, it provides a reduced number of storage accesses depending on the join pattern of a query. In addition, the proposed cost-based map-side join of RDFChain reduces the number of map jobs since it processes as many joins as possible in a map job using statistics.".
• paper-41 abstract "With the advent of Linked Data the amount of automatically generated machine-readable data on the Web, often obtained by means of mapping relational data to RDF, has risen significantly. However, manually created, quality-assured and crowd-sourced data based on ontologies, is not available in the quantities that would realise the full potential of the semantic Web. One of the barriers for semantic Web novices to create machine-readable data, is the lack of easy-to-use Web publishing tools that separate the schema modelling from the data creation. In this demonstration we present ActiveRaUL, a Web service that supports the automatic generation of Web form-based user interfaces from any input ontology. The resulting Web forms are unique in supporting users, inexperienced in semantic Web technologies, to create and maintain RDF data modelled according to an ontology. We report on a use case based on the Sensor Network Ontology that supports the viability of our approach.".
• paper-42 abstract "Current Wikipedia-based multilingual knowledge bases still suffer the following problems: (i) the scarcity of non-English knowledge, (ii) the noise in the semantic relations and (iii) the limited coverage of equivalent cross-lingual entities. In this demo, we present a large-scale bilingual knowledge graph named XLore, which has adequately solved the above problems.".
• paper-43 abstract "Data provenance is defined as information about entities, activities and people producing or modifying a piece of data. On the Web, the interchange of standardized provenance of (linked) data is an essential step towards establishing trust. One mechanism to track (part of) the provenance of data, is through the use of version control systems (VCS), such as Git. These systems are widely used to facilitate collaboration primarily for both code and data. Here, we describe a system to expose the provenance stored in VCS in a new standard Web-native format: W3C PROV. This enables the easy publication of VCS provenance on the Web and subsequent integration with other systems that make use of PROV. The system is exposed as a RESTful Web service, which allows integration into user-friendly tools, such as browser plugins.".
• paper-44 abstract "We propose a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of automated context-aware configuration of filtering, fusion, and reasoning mechanisms in IoT middleware according to the problems at hand. We incorporate semantic technologies in solving the above challenges.".
• paper-45 abstract "Answering SPARQL queries over the Web of Linked Data is a challenging problem. Approaches based on distributed query processing provide up-to-date results but can suffer from delayed response times, indexing-based approaches provide fast response times but results can be out-of-date and the costs of indexing the growing Web of Linked Data are potentially huge. Hybrid approaches try to offer the best of both. In this demo paper we describe a system for answering SPARQL queries within fixed time constraints by accessing SPARQL endpoints and the Web of Linked Data directly.".
• paper-46 abstract "Shi3ld is a context-aware authorization framework for protecting SPARQL endpoints. It assumes the definition of access policies using RDF and SPARQL, and the specification of named graphs to identify the protected resources. These assumptions lead to the incapability for users who are not familiar with such languages and technologies to use the authorization framework. In this paper, we present a graphical user interface to support dataset administrators to define access policies and the target elements protected by such policies.".
• paper-47 abstract "This work describes OU Social, an application that collects and analyses data from public Facebook groups set up by students to discuss particular Open University courses. This application exploits semantic technologies to monitor the behaviour of users over time as well as the topics that emerge from Facebook group discussions. The paper describes the architecture of OU Social and provides a brief overview of the analysis results obtained from 44 different Facebook groups examined over a 6 year period (2007-2013)".
• paper-48 abstract "GSN is an open source middleware for managing data produced by sensors deployed in a sensor network. We have extended and enhanced GSN to enable (i) semantically aware preparation, exchange and processing of the data (ii) user specified event processing for alerts and (iii) associate sensor data to 'things'. Here, we demonstrate our smart farm as a use case of a semantically aware sensor network for better integration of sensor data.".
• paper-49 abstract "Knowledge Discovery consists in discovering hidden regulari- ties in large amount of data using data mining techniques. The obtained patterns require an interpretation that is usually achieved using some background knowledge given by experts from several domains. On the other hand, the rise of Linked Data has increased the number of con- nected cross-disciplinary knowledge, in the form of RDF datasets, classes and relationships. Here we show how Linked Data can be used in an Inductive Logic Programming process, where they provide background knowledge for finding hypotheses regarding the unrevealed connections between items of a cluster. By using an example with clusters of books, we show how different Linked Data sources can be used to automatically generate rules giving an underlying explanation to such clusters.".
• paper-50 abstract "In this paper we present the contextual tag cloud system: a novel application that helps users explore a large scale RDF dataset. Unlike folksonomy tags used in most traditional tag clouds, the tags in our system are ontological terms (classes and properties), and a user can construct a context with a set of tags that defines a subset of instances. Then in the contextual tag cloud, the font size of each tag depends on the number of instances that are associated with that tag and all tags in the context. Each contextual tag cloud serves as a summary of the distribution of relevant data, and by changing the context, the user can quickly gain an understanding of patterns in the data. Furthermore, the user can choose to include RDFS taxonomic and/or domain/range entailment in the calculations of tag sizes, thereby understanding the impact of semantics on the data. The system runs on the BTC2012 dataset with more than 1.4 billion triples from which we extract over 380,000 tags. Several scalability challenges must be overcome in order to achieve a responsive interface.".
• paper-51 abstract "Museums around the world have built databases with meta-data about millions of objects, their history, the people who created them, and the entities they represent. This data is stored in proprietary databases and is not readily available for use. Recently, museums embraced the Semantic Web as a means to make this data available to the world, but the experience so far shows that publishing museum data to the linked data cloud is difficult: the databases are large and complex, the information is richly structured and varies from museum to museum, and it is difficult to link the data to other datasets. We have been collaborating with the Smithsonian American Art Museum to create a set of tools that allow museums and other cultural heritage institutions to publish their data as Linked Open Data. In this demonstration we will show the end-to-end process of starting with the original source data, modeling the data with respect to a ontology of cultural heritage data, linking the data to DBpedia, and then publishing the information as Linked Open Data. Video: http://youtu.be/1Vaytr09H1w".
• paper-52 abstract "We present GRAPHIUM a tool to visualize trends and patterns in the performance of existing graph and RDF engines. We will demonstrate GRAPHIUM and attendees will be able to observe and analyze the performance exhibited by Neo4j, DEX, HypergraphDB and RDF-3x when core graph-based and mining tasks are run against a variety of benchmark of graphs of diverse characteristics.".
• paper-53 abstract "SPARQL federated queries can be affected by both characteristics of the query and datasets in the federation. We present SILURIAN a Sparql vIsuaLizer for UndeRstanding querIes And federatioNs. SILURIAN visualizes SPARQL queries and, thus, it allows the analysis and understanding of a query complexity with respect to relevant endpoints and shapes of the possible plans.".
• paper-54 abstract "Understanding the way information is propagated and made visible on Facebook is a difficult task. The privacy settings and the rules that apply to individual items are reasonably straightforward. However, for the user to track all of the information that needs to be integrated and the inferences that can be made on their posts is complex, to the extent that it is almost impossible for any individual to achieve. In this demonstration, we investigate the use of knowledge modeling and reasoning techniques (including basic ontological representation, rules and epistemic logics) to make these inferences explicit to the user.".
• paper-55 abstract "The detection and presentation of changes between OWL ontologies (in the form of a diff) is an important service for ontology engineering, being an active research topic. We present here a diff tool that incorporates structural and semantic techniques in order to, firstly, distinguish effectual and ineffectual changes between ontologies and, secondly, align and categorise those changes according to their impact. Such a categorisation of changes is shown to facilitate the navigation through, and analysis of change sets. The tool is made available as a web-based application, as well as a standalone command-line tool. Both of these output an XML change set file and a transformation into HTML, which allows users to browse through and focus on those changes of utmost interest using any web-browser.".
• paper-56 abstract "We present D-SPARQ, a distributed RDF query engine that combines the MapReduce processing framework with a NoSQL distributed data store, MongoDB. The performance of processing SPARQL queries mainly depends on the efficiency of handling the join operations between the RDF triple patterns. Our system features two unique characteristics that enable efficiently tackling this challenge: 1) Identifying specific patterns of the input queries that enable improving the performance by running different parts of the query in a parallel mode. 2) Using the triple selectivity information for reordering the individual triples of the input query within the identified query patterns. The preliminary results demonstrate the scalability and efficiency of our distributed RDF query engine.".
• paper-57 abstract "Given two sets of entities – potentially the results of two queries on aknowledge graph like YAGO or DBpedia – characterizing the relationship betweenthese sets in the form of important people, events and organizations is an analyticstask useful in many domains. In this paper, we present an intuitive and efficientlycomputable vertex centrality measure that captures the importance of a nodewith respect to the explanation of the relationship between the pair of query sets.Using a weighted link graph of entities contained in the English Wikipedia, wedemonstrate the usefulness of the proposed measure.".
• paper-58 abstract "This paper discusses the integration of an ontology with a natural language query engine to calculate and interpret epidemiological indicators for population health assessments. In this paper, we discuss the application of this approach to one type of possible query, which retrieves health determinants, causally associated with diabetes mellitus.".
• paper-59 abstract "In this paper we present preliminary results on the extraction of ORA: the Natural Ontology of Wikipedia. ORA is obtained through an automatic process that analyses the natural language definitions of DBpedia entities provided by their Wikipedia pages. Hence, this ontology reflects the richness of terms used and agreed by the crowds, and can be updated periodically according to the evolution of Wikipedia.".
• paper-60 abstract "This work demonstrates Treo, a framework which converges elements from Natural Language Processing, Semantic Web, Information Retrieval and Databases, to create a semantic search engine and question answering (QA) system for heterogeneous data. Jeopardy and Question Answering queries over open domain structured and unstructured data are used to demonstrate the approach. In this work, Treo is extended to cope with unstructured data in addition to structured data. The setup of the framework is done in 3 steps and can be adapted to other datasets by practitioners in a simple DIY process.".
• paper-61 abstract "FRED is a machine reading tool for converting text into internally well-connected and quality linked-data-ready ontologies in web-service-acceptable time. It implements a novel approach for ontology design from natural language sentences, combining Discourse Representation Theory (DRT), linguistic frame semantics, and Ontology Design Patterns (ODP). The tool is based on Boxer which implements a DRT-compliant deep parser. The logical output of Boxer is enriched with semantic data from VerbNet (or FrameNet) frames and transformed into RDF/OWL by means of a mapping model and a set of heuristics following best practices of OWL ontologies and RDF data design. The current version of the tool includes Earmark-based markup, and enrichment with WSD and NER off-the-shelf components.".
• paper-62 abstract "In this demo paper we introduce a Linked Data-driven, Semantically-enabled Journal Portal (SEJP) that offers a variety of interactive scientometrics modules. SEJP allows editors, reviewers, authors, and readers to explore and analyze (meta)data published by a journal. Besides Linked Data created from the journal's internal data, SEJP also links out to other sources and includes them to develop more powerful modules. These modules range from simple descriptive statistics, over the spatial analysis of visitors and authors, to topics trending modules. While SEJP will be available for multiple journals, this paper shows its deployment to the Semantic Web journal by IOS Press. Due to its open & transparent review process, SWJ offers a wide variety of additional information, e.g., about reviewers, editors, paper decisions, and so forth.".
• paper-63 abstract "We present ONTOMS2, an efficient and scalable ONTOlogy Management System with an incremental reasoning. ONTOMS2 stores an OWL document and processes OWL-QL and SPARQL queries. Especially, ONTOMS2 supports SPARQL Update queries with an incremental instance reasoning of inverseOf, symmetric and transitive properties.".
• paper-64 abstract "Querying Linked Data means to pose queries on various data sources without information about the data and the schema of the data. This demo shows SPACE, a tool to support autocompletion for SPARQL queries. It takes as input SPARQL query logs and builds an index structure for efficient and fast computation of query suggestions. To demonstrate SPACE, we use available query logs from the USEWOD Data Challenge 2013.".
• paper-65 abstract "The linked open data cloud is constantly evolving as datasets are continuously updated with newer versions. As a result, representing, querying, and visualizing the temporal dimension of linked data is crucial. This is especially important for geospatial datasets that form the backbone of large scale open data publication efforts in many sectors of the economy (the public sector, the Earth observation sector). Although there has been some work on the representation and querying of linked geospatial data that change over time, to the best of our knowledge, there is currently no tool that offers spatio-temporal visualization of such data. In this demo paper we present the system SexTant that addresses this issue. SexTant is a web-based tool that enables the exploration of time-evolving linked geospatial data as well as the creation, sharing, and collaborative editing of "temporally-enriched" thematic maps by combining different sources of geospatial and temporal information.".
• paper-66 abstract "In spite of the recent renaissance in lightweight description logics (DLs), many prominent DLs, such as that underlying the Web Ontology Language (OWL), have high worst case complexity for their key inference services. Modern reasoners have a large array of optimization, tuned calculi, and implementation tricks that allow them to perform very well in a variety of application scenarios, even though the complexity results ensure that they will perform poorly for some inputs. For users, the key question is how often they will encounter those pathological inputs in practice, that is, how robust are reasoners. We attempt to determine this question for classification of existing ontologies as they are found on the Web. It is a fairly common user task to examine ontologies published on the Web as part of their development process. Thus, the robustness of reasoners in this scenario is both directly interesting and provides some hints toward answering the broader question. From our experiments, we show that the current crop of OWL reasoners, in collaboration, is very robust against the Web.".
• paper-67 abstract "In order to cope with the ever-increasing data volume, distributed stream processing systems have been proposed. To ensure scalability most distributed systems partition the data and distribute the workload among multiple machines. This approach does, however, raise the question how the data and the workload should be partitioned and distributed. A uniform scheduling strategy---a uniform distribution of computation load among available machines---typically used by stream processing systems, disregards network-load as one of the major bottlenecks for throughput resulting in an immense load in terms of inter-machine communication. We propose a graph-partitioning based approach for workload scheduling within stream processing systems.We implemented a distributed triple-stream processing engine on top of the Storm realtime computation framework and evaluate its communication behavior using two real-world datasets. We show that the application of graph partitioning algorithms can decrease inter-machine communication substantially (by 40% to 99%) whilst maintaining an even workload distribution, even using very limited data statistics. We also find that processing RDF data as single triples at a time rather than graph fragments (containing multiple triples), may decrease throughput indicating the usefulness of semantics.".
• paper-68 abstract "The pervasivity of mobile phones opens an unprecedented opportunityof deepening into the human dynamics through the analysis of the data they generate.This enables a novel human-driven approach to service creation in a wideset of domains such as health-care, transportation and urban safety. The telecomoperators own and manage billions of mobile network events (like the Call DetailedRecords - CDR) per day: the interpretation of such a big stream of dataneeds a deep understanding of the context where the events have occurred. Theexploitation of available background knowledge is a key element in this scenario.In this paper we introduce a novel method for the semantic interpretation of humanbehavior in mobility based on the merge of the mobile network data streamand the geo-referred available background knowledge. We modeled the humanbehavior making use of the geo and time-referenced knowledge available on theweb (e.g., geo-tagged resources, info on weather forecast, social events, etc.)matching it with the mobile network coverage map. The model is intended tocharacterize the contexts where the mobile network events occur in order to helpin interpreting the behavioral traits that generated by them. This will allow us toachieve a set of predictive tasks such as the prediction of human activities in certain contextual conditions (e.g., when an accident occurs on highway before theworking time starts, etc.), or the characterization of exceptional events detectedfrom anomalies in mobile network data.We created an ontological and stochastic high-level representation behavioralmodel (HRBModel) that maps the human activities to the different contexts.Given the mobile phone network and the geo-tagged resource Openstreetmap,the model is used to rank the activities associated to a particular network event(e.g. a sudden call amount peak) according to their probability. We also describethe design of an experimental evaluation and the preliminary evaluation resultsto measure the performance of the model and to improve the activity predictiontask.".
• paper-69 abstract "Generating useful RDF linked data is not a straightforward process for scientists using today’s tools. In this paper we introduce the SemantEco Annotator, a semantic web application that leverages community-based vocabularies and ontologies during the translation process itself to ease the process of drawing out implicit relationships in tabular data so that they may be immediately available for use within the LOD cloud. Our goal for the SemantEco Annotator is to make advanced RDF translation techniques available to the layperson.".
• paper-70 abstract "While the web of linked data gets increasingly richer in size and complexity, its use is still constrained by the lack of applications consuming this data. We propose a Web-based tool to build and execute complex applications to transform, integrate and visualize Semantic Web data. Applications are composed as pipelines of a few basic components and completely based on Semantic Web standards, including SPARQL Construct for data transformation and SPARQL Update for state transition. The main novelty of the approach lays in the support to interaction, through the availability of user interface event streams as pipeline inputs.".
• paper-71 abstract "Social care and Healthcare are unique in terms of cultural importance, economic size and domain complexity. Combining information systems from both domains poses unique scientific and technical challenges with regard to information representation, access, integration and retrieval granularity. We present a semantics-based approach that is uniquely positioned to access information across domains using a combination of business rules and contextual exploration. A proof of concept illustrates that semantic technologies can cope in a scenario where traditional data integration approaches are too costly and reduce the addressable information space.".
• paper-72 abstract "This demo will present the advantages of the new, bookkeeping-free method for incremental reasoning in OWL EL on incremental classification of large ontologies. In particular, we will show how a typical experience of a user editing a large ontology can be improved if the reasoner (or ontology IDE) provides the capability of instantaneously re-classifying the ontology in the background mode when a change is made. In addition, we intend to demonstrate how incremental reasoning helps in other tasks such as answering DL queries and computing explanations of entailments. We will use our OWL EL reasoner ELK and its Protege plug-in as the main tools to highlight these benefits.".
• paper-01 abstract "Text-rich structured data become more and more ubiquitous on the Web and on the enterprise databases by encoding heterogeneous structural information between entities such as people, locations, or organizations and the associated textual information. For analyzing this type of data, existing topic modeling approaches, which are highly tailored toward document collections, require manually-defined regularization terms to exploit and to bias the topic learning towards structure information. We propose an approach, called Topical Relational Model, as a principled approach for automatically learning topics from both textual and structure information. Using a topic model, we can show that our approach is effective in exploiting heterogeneous structure information, outperforming a state-of-the-art approach that requires manually-tuned regularization.".
• paper-02 abstract "We illustrate several novel attacks to the confidentiality of knowledge bases (KB). Then we introduce a new confidentiality model, sensitive enough to detect those attacks, and a method for constructing secure KB views. We identify safe approximations of the background knowledge exploited in the attacks; they can be used to reduce the complexity of constructing secure KB views.".
• paper-03 abstract "Although an increasing number of RDF knowledge bases are published, many of those consist primarily of instance data and lack sophisticated schemata. Having such schemata allows more powerful querying, consistency checking and debugging as well as improved inference. One of the reasons why schemata are still rare is the effort required to create them. In this article, we propose a semi-automatic schemata construction approach addressing this problem: First, the frequency of axiom patterns in existing knowledge bases is discovered. Afterwards, those patterns are converted to SPARQL based pattern detection algorithms, which allow to enrich knowledge base schemata. We argue that we present the first scalable knowledge base enrichment approach based on real schema usage patterns. The approach is evaluated on a large set of knowledge bases with a quantitative and qualitative result analysis.".

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