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- paper-01 type InProceedings.
- paper-01 label "Cross Lingual Semantic Search by Improving Semantic Similarity and Relatedness Measures".
- paper-01 sameAs paper-01.
- paper-01 abstract "Since 2001, the semantic web community has been working hard towards creating standards which will increase the accessibility of available information on the web. Yahoo research recently reported that 30% of all HTML pages contain structured data such as microdata, RDFa, or microformat. Although multilinguality of the web is a hurdle in information access, the rapid growth of the semantic web enables us to retrieve fine grained information across the language barrier. In this thesis, firstly, we focus on developing a methodology to perform cross-lingual semantic search over structured data (knowledge base), by transforming natural language queries into SPARQL. Secondly, we focus on improving the semantic similarity and relatedness measures, to overcome the semantic gap between the vocabulary in the knowledge base and the terms appearing in the query. The preliminary results are evaluated against the QALD-2 test dataset, which achieved a F1 score of 0.46, an average precision of 0.44, and an average recall of 0.48.".
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- paper-01 title "Cross Lingual Semantic Search by Improving Semantic Similarity and Relatedness Measures".
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- item-1 hasContent nitish-aggarwal.
- paper-02 type InProceedings.
- paper-02 label "Quality Reasoning in the Semantic Web".
- paper-02 sameAs paper-02.
- paper-02 abstract "Assessing the quality of data published on the Web has been identified as an essential step in selecting reliable information for use in tasks such as decision making. This paper discusses a quality assessment framework based on semantic web technologies and outlines a role for provenance in supporting and documenting such assessments.".
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- paper-02 title "Quality Reasoning in the Semantic Web".
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- item-1 hasContent chris-baillie.
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- item-2 hasContent peter-edwards.
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- item-3 hasContent edoardo-pignotti.
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- paper-03 type InProceedings.
- paper-03 label "Burst the Filter Bubble: Using Semantic Web to Enable Serendipity".
- paper-03 sameAs paper-03.
- paper-03 abstract "Personalization techniques aim at helping people dealing with the ever growing amount of information by filtering it according to their interests. However, to avoid the information overload, such techniques often create an over-personalization effect, i.e. users are exposed only to the content systems assume they would like. To break this "personalization bubble" we introduce the notion of serendipity as a performance measure for recommendation algorithms. For this, we first identify aspects from the user perspective, which can determine level and type of serendipity desired by users. Then, we propose a user model that can facilitate such user requirements, and enables serendipitous recommendations. The use case for this work focuses on TV recommender systems, however the ultimate goal is to explore the transferability of this method to different domains. This paper covers the work done in the first eight months of research and describes the plan for the entire PhD trajectory.".
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- paper-03 title "Burst the Filter Bubble: Using Semantic Web to Enable Serendipity".
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- item-1 hasContent valentina-maccatrozzo.
- paper-04 type InProceedings.
- paper-04 label "Reconstructing Provenance".
- paper-04 sameAs paper-04.
- paper-04 abstract "Provenance is an increasingly important aspect of data management that is often underestimated and neglected by practitioners. In our work, we target the problem of reconstructing provenance of files in a shared folder setting, assuming that only standard filesystem metadata are available. We propose a content-based approach that is able to reconstruct provenance automatically, leveraging several similarity measures and edit distance algorithms, adapting and integrating them into a multi-signal pipeline. We discuss our research methodology and show some promising preliminary results.".
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- paper-04 title "Reconstructing Provenance".
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- item-1 hasContent sara-magliacane.
- paper-05 type InProceedings.
- paper-05 label "Very Large Scale OWL Reasoning through Distributed Computation".
- paper-05 sameAs paper-05.
- paper-05 abstract "Due to recent developments in reasoning algorithms of the various OWL profiles, the classification time for an ontology has come down drastically. For all of the popular reasoners, in order to process an ontology, an implicit assumption is that the ontology should fit in primary memory. The memory requirements for a reasoner are already quite high, and considering the ever increasing size of the data to be processed and the goal of making reasoning Web scale, this assumption becomes overly restrictive. In our work, we study several distributed classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.".
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- paper-05 title "Very Large Scale OWL Reasoning through Distributed Computation".
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- item-1 hasContent raghava-mutharaju.
- paper-06 type InProceedings.
- paper-06 label "Replication for Linked Data".
- paper-06 sameAs paper-06.
- paper-06 abstract "With the Semantic Web scaling up, and more triple-stores with update facilities being available, the need for higher levels of simultaneous triple-stores with identical information becomes more and more urgent. However, where such Data Replication approaches are common in the database community, there is no comprehensive approach for data replication for the Semantic Web. In this research proposal, we will discuss the problem space and scenarios of data replication in the Semantic Web, and explain how we plan on dealing with this issue.".
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- paper-06 isPartOf proceedings-1.
- paper-06 title "Replication for Linked Data".
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- item-1 hasContent laurens-rietveld.
- paper-07 type InProceedings.
- paper-07 label "Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages Across Heterogeneous Data Sources".
- paper-07 sameAs paper-07.
- paper-07 abstract "Due to the decentralized nature of the Semantic Web, the same real world entity may be described in various data sources and assigned syntactically distinct identifiers. In order to facilitate data utilization in the Semantic Web, without compromising the freedom of people to publish their data, one critical problem is to appropriately interlink such heterogeneous data. This interlinking process can also be referred to as Entity Coreference, i.e., finding which identifiers refer to the same real world entity. This proposal will investigate algorithms to solve this entity coreference problem in the Semantic Web in several aspects. The essence of entity coreference is to compute the similarity of instance pairs. Given the diversity of domains of existing datasets, it is important that an entity coreference algorithm be able to achieve good precision and recall across domains represented in various ways. Furthermore, in order to scale to large datasets, an algorithm should be able to intelligently select what information to utilize for comparison and determine whether to compare a pair of instances to reduce the overall complexity. Finally, appropriate evaluation strategies need to be chosen to verify the effectiveness of the algorithms.".
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- paper-07 isPartOf proceedings-1.
- paper-07 title "Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages Across Heterogeneous Data Sources".
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