Matches in ScholarlyData for { ?s ?p ?o. }
- 219 keyword "dependency linguistics".
- 219 keyword "inductive logic programming".
- 219 keyword "information extraction".
- 219 keyword "machine learning".
- 219 keyword "semantic annotation".
- 219 keyword "semantic web".
- 219 title "Towards Semantic Annotation Supported by Dependency Linguistics and ILP".
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- item-1 hasContent jan-dedek.
- 225 creator birte-glimm.
- 225 creator johanna-volker.
- 225 creator sebastian-rudolph.
- 225 type InProceedings.
- 225 label "Integrated Metamodeling and Diagnosis in OWL 2".
- 225 sameAs 225.
- 225 abstract "Ontological metamodeling has a variety of applications yet only very restricted forms are supported by OWL 2 directly. We propose a novel encoding scheme enabling class-based metamodeling inside the domain ontology with full reasoning support through standard OWL 2 reasoning systems. We demonstrate the usefulness of our method by applying it to the OntoClean methodology. En passant, we address performance problems arising from the inconsistency diagnosis strategy originally proposed for OntoClean by introducing an alternative technique where sources of conflicts are indicated by means of marker predicates.".
- 225 hasAuthorList authorList.
- 225 isPartOf proceedings.
- 225 keyword "semantic web".
- 225 title "Integrated Metamodeling and Diagnosis in OWL 2".
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- item-1 hasContent birte-glimm.
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- item-2 hasContent sebastian-rudolph.
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- item-3 hasContent johanna-volker.
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- 228 creator jens-lehmann.
- 228 creator lorenz-buhmann.
- 228 type InProceedings.
- 228 label "ORE - A Tool for Repairing and Enriching Knowledge Bases".
- 228 sameAs 228.
- 228 abstract "While the number and size of Semantic Web knowledge bases increases, their maintenance and quality assurance are still difficult. In this article, we present ORE, a tool for repairing and enriching OWL ontologies. State-of-the-art methods in ontology debugging and supervised machine learning form the basis of ORE and are adapted or extended so as to work well in practice. ORE supports the detection of a variety of ontology modelling problems and guides the user through the process of resolving them. Furthermore, the tool allows to extend an ontology through (semi-)automatic supervised learning. A wizard-like process helps the user to resolve potential issues after axioms are added.".
- 228 hasAuthorList authorList.
- 228 isPartOf proceedings.
- 228 keyword "semantic web".
- 228 title "ORE - A Tool for Repairing and Enriching Knowledge Bases".
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- item-1 hasContent jens-lehmann.
- item-1 hasContent lorenz-buhmann.
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- 234 creator claudio-giuliano.
- 234 creator katerina-tymoshenko.
- 234 creator luciano-serafini.
- 234 creator volha-bryl.
- 234 type InProceedings.
- 234 label "Supporting Natural Language Processing with Background Knowledge: Coreference Resolution Case".
- 234 sameAs 234.
- 234 abstract "Systems based on statistical and machine learning methods have been shown to be extremely effective and scalable for the analysis of large amount of textual data. However, in the recent years, it becomes evident that one of the most important directions of improvement in natural language processing (NLP) tasks, like word sense disambiguation, coreference resolution, relation extraction, and other tasks related to knowledge extraction, is by exploiting semantics. While in the past, the unavailability of rich and complete semantic descriptions constituted a serious limitation of their applicability, nowadays, the Semantic Web made available a large amount of logically encoded information (e.g. ontologies, RDF(S)-data, linked data, etc.), which constitutes a valuable source of semantics. However, web semantics cannot be easily plugged into machine learning systems. Therefore the objective of this paper is to define a reference methodology for combining semantic information available in the web under the form of logical theories, with statistical methods for NLP. The major problems that we have to solve to implement our methodology concern (i) the selection of the correct and minimal knowledge among the large amount available in the web, (ii) the representation of uncertain knowledge, and (iii) the resolution and the encoding of the rules that combine knowledge retrieved from Semantic Web sources with semantics in the text. In order to evaluate the appropriateness of our approach, we present an application of the methodology to the problem of intra-document coreference resolution, and we show by means of some experiments on the standard dataset, how the injection of knowledge leads to the improvement of this task performance.".
- 234 hasAuthorList authorList.
- 234 isPartOf proceedings.
- 234 keyword "semantic web".
- 234 title "Supporting Natural Language Processing with Background Knowledge: Coreference Resolution Case".
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- item-1 hasContent volha-bryl.
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- item-2 hasContent claudio-giuliano.
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- item-3 hasContent luciano-serafini.
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- item-4 hasContent katerina-tymoshenko.
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- 241 creator christoph-riess.
- 241 creator norman-heino.
- 241 creator sebastian-tramp.
- 241 creator soeren-auer.
- 241 type InProceedings.
- 241 label "EvoPat - Pattern-Based Evolution and Refactoring of RDF Knowledge Bases".
- 241 sameAs 241.
- 241 abstract "Facilitating the seamless evolution of RDF knowledge bases on the Semantic Web presents still a major challenge. In this work we devise EvoPat - a pattern-based approach for the evolution and refactoring of knowledge bases. The approach is based on the definition of basic evolution patterns, which are represented declaratively and can capture simple evolution and refactoring operations on both data and schema levels. For more advanced and domain-specific evolution and refactorings, several simple evolution patterns can be combined into a compound one. We performed a comprehensive survey of possible evolution patterns with a combinatorial analysis of all possible before/after combinations, resulting in an extensive catalog of usable evolution patterns. Our approach was implemented as an extension for the OntoWiki semantic collaboration platform and framework.".