Matches in ScholarlyData for { ?s ?p ?o. }
- 148 label "From Queriability to Informativity, assessing "Quality in Use'' of DBpedia and YAGO".
- 148 sameAs 148.
- 148 abstract "In recent years, an increasing number of semantic data sources are published on the Web. These sources are further interlinked to form the Linking Open Data (LOD) cloud. To make full use of these data sets, it is a strong demand to learn their data qualities. Researchers have proposed several metrics and developed tools to measure qualities of the data sets in LOD from different dimensions. However, there exist few studies on evaluating data set quality from users' usability perspective while usability has great impacts on the spreading and re-use of LOD data sets. On the other hand, usability is well-studied in the area of software quality. In the newly published standard ISO/IEC 25010, usability is further broadened to include the notion “quality in use'' besides the other two factors namely internal and external. In this paper, we firstly adapt the notions and the methods used in software quality to assessing the data set quality. Secondly, we formally define two quality dimensions namely Queriability and Informativity from the perspective of quality in use. The two proposed dimensions correspond to querying and answering, which are the most frequent usage scenarios for accessing LOD data sets. Then we provide a series of metrics to measure the two dimensions. At last, we apply the metrics to two representative data sets in LOD (i.e., YAGO and DBpedia). In the evaluating process, we select dozens of questions from both QALD and WebQuestions, and ask a group of users to construct queries as well as to check answers with the help of our usability testing tool. The findings during the assessment not only illustrate the capability of our method and metrics, but also give new insights of data quality of the two knowledge bases.".
- 148 hasAuthorList authorList.
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- 148 title "From Queriability to Informativity, assessing "Quality in Use'' of DBpedia and YAGO".
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- item-1 hasContent tong-ruan.
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- item-2 hasContent yang-li.
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- item-3 hasContent haofen-wang.
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- 161 creator christin-seifert.
- 161 creator michael-granitzer.
- 161 creator stefan-zwicklbauer.
- 161 type InProceedings.
- 161 label "DoSeR - A Knowledge-Base-Agnostic Framework for Entity Disambiguation Using Semantic Embeddings".
- 161 sameAs 161.
- 161 abstract "Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. In this work, we propose DoSeR (Disambiguation of Semantic Resources), a (named) entity disambiguation framework that is knowledge-base-agnostic in terms of RDF (e.g. DBpedia) and entity-annotated document knowledge bases (e.g. Wikipedia). Initially, our framework automatically generates semantic entity embeddings given one or multiple knowledge bases. Then, DoSeR accepts documents with a given set of surface forms as input and collectively links them to an entity in a knowledge base with a graph-based approach. We evaluate DoSeR on seven different data sets against publicly available, state-of-the-art (named) entity disambiguation frameworks. Our approach outperforms the state-of-the-art approaches that make use of RDF knowledge bases and/or entity-annotated document knowledge bases by up to 10% F1 measure.".
- 161 hasAuthorList authorList.
- 161 isPartOf proceedings.
- 161 title "DoSeR - A Knowledge-Base-Agnostic Framework for Entity Disambiguation Using Semantic Embeddings".
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- item-1 hasContent stefan-zwicklbauer.
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- item-2 hasContent christin-seifert.
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- item-3 hasContent michael-granitzer.
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- 163 creator cristobal-esteban.
- 163 creator volker-tresp.
- 163 creator yinchong-yang.
- 163 type InProceedings.
- 163 label "Embedding Mapping Approaches for Tensor Factorization and Knowledge Graph Modelling".
- 163 sameAs 163.
- 163 abstract "Latent embedding models, based for example on matrix and tensor factorization, are the basis of state-of-the art statistical solutions for modelling Knowledge Graphs and Recommender Systems to predict new links between known entities and/or relations. To be able to perform predictions for new entities and/or relation types, however, the model often has to be retrained completely to derive the new latent embeddings. This could be a potential limitation for data sets when fast predictions for new entities and relation types are required. In this paper we propose approaches that can map new entities into the existing latent embedding space learned from factorization models. Without retraining of any kind, our model is solely based on the observable ---even incomplete--- features of the new entities e.g. a subset of observed links to other known entities. We show that these mapping approaches are efficient and are applicable to a wide variety of existing factorization models, including nonlinear models. We perform experiments on multiple real-world datasets and evaluate the performances from different aspects.".
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- 163 title "Embedding Mapping Approaches for Tensor Factorization and Knowledge Graph Modelling".
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- item-1 hasContent yinchong-yang.
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- item-2 hasContent cristobal-esteban.
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- 166 creator brigitte-safar.
- 166 creator celine-alec.
- 166 creator chantal-reynaud.
- 166 type InProceedings.
- 166 label "An Ontology-driven Approach for Semantic Annotation of Documents with Specific Concepts".
- 166 sameAs 166.
- 166 abstract "This paper deals with an ontology-driven approach for semantic annotation of documents from a corpus where each document describes an entity of a same domain. Our goal is to annotate each document with concepts being too specific to be explicitly mentioned into the text. The only thing we know about the concepts is their label. They have no definitions. Moreover, their characteristics in the texts are incomplete. We propose an ontology-based approach, named SAUPODOC, aiming to perform this particular annotation process by combining several approaches. Indeed, SAUPODOC relies on a domain ontology relative to the field under study, which has a pivotal role, on its population with property assertions coming from documents and external resources, and its enrichment with formal specific concept definitions. Experiments have been carried out in two application domains, showing the benefit of the approach compared to well-known classifiers.".
- 166 hasAuthorList authorList.
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- 166 title "An Ontology-driven Approach for Semantic Annotation of Documents with Specific Concepts".
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- item-1 hasContent celine-alec.
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- item-2 hasContent chantal-reynaud.