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- 253 creator yi-dong-shen.
- 253 type InProceedings.
- 253 label "A Decomposition-based Approach to Optimizing Conjunctive Query Answering in OWL DL".
- 253 sameAs 253.
- 253 abstract "Scalable query answering over Description Logic (DL) based ontologies plays an important role for the success of the Semantic Web. Towards tackling the scalability problem, we propose a decomposition-based approach to optimizing existing OWL DL reasoners in evaluating conjunctive queries in OWL DL ontologies. The main idea is to decompose a given OWL DL ontology into a set of target ontologies without duplicated ABox axioms so that the evaluation of a given conjunctive query can be separately performed in every target ontology by applying existing OWL DL reasoners. This approach guarantees sound and complete results for the category of conjunctive queries that the applied OWL DL reasoner correctly evaluates. Experimental results on large benchmark ontologies and benchmark queries show that the proposed approach can significantly improve scalability and efficiency in evaluating general conjunctive queries.".
- 253 hasAuthorList authorList.
- 253 hasTopic Semantic_Web.
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- 253 title "A Decomposition-based Approach to Optimizing Conjunctive Query Answering in OWL DL".
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- item-1 hasContent jianfeng-du.
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- item-2 hasContent guilin-qi.
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- item-3 hasContent jeff-z-pan.
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- item-4 hasContent yi-dong-shen.
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- 278 creator chiara-di-francescomarino.
- 278 creator chiara-ghidini.
- 278 creator luciano-serafini.
- 278 creator marco-rospocher.
- 278 creator paolo-tonella.
- 278 type InProceedings.
- 278 label "Semantically-aided business process modeling".
- 278 sameAs 278.
- 278 abstract "Enriching business process models with semantic annotations taken from an ontology has become a crucial necessity both in service provisioning, integration and composition, and in business processes management. In our work we represent semantically annotated business processes as part of an OWL knowledge base that formalises the business process structure, the business domain, and a set of criteria describing correct semantic annotations. In this paper we show how Semantic Web representation and reasoning techniques can be effectively applied to formalise, and automatically verify, sets of constraints on Business Process Diagrams that involve both knowledge about the domain and the process structure. We also present a tool for the automated transformation of an annotated Business Process Diagram into an OWL ontology. The use of the semantic web techniques and tool presented in the paper results in a novel support for the management of business processes in the phase of process modeling, whose feasibility and usefulness will be illustrated by means of a concrete example.".
- 278 hasAuthorList authorList.
- 278 hasTopic Semantic_Web.
- 278 isPartOf proceedings.
- 278 title "Semantically-aided business process modeling".
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- item-1 hasContent chiara-di-francescomarino.
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- item-2 hasContent chiara-ghidini.
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- 279 creator antje-schultz.
- 279 creator sergej-sizov.
- 279 creator steffen-staab.
- 279 creator thomas-franz.
- 279 type InProceedings.
- 279 label "TripleRank: Ranking Semantic Web Data By Tensor Decomposition".
- 279 sameAs 279.
- 279 abstract "The Semantic Web fosters novel applications targeting a more efficient and satisfying exploitation of the data available on the web, e.g. faceted browsing of linked open data. Large amounts and high diversity of knowledge in the Semantic Web pose the challenging question of appropriate relevance ranking for producing fine-grained and rich descriptions of the available data, e.g. to guide the user along most promising knowledge aspects. Existing methods for graph-based authority ranking lack support for fine-grained latent coherence between resources and predicates (i.e. support for link semantics in the linked data model). In this paper, we present TripleRank, a novel approach for faceted authority ranking in the context of RDF knowledge bases. TripleRank captures the additional latent semantics of Semantic Web data by means of statistical methods in order to produce richer descriptions of the available data. We model the Semantic Web by a 3-dimensional tensor that enables the seamless representation of arbitrary semantic links. For the analysis of that model, we apply the PARAFAC decomposition, which can be seen as a multi-modal counterpart to Web authority ranking with HITS. The result are groupings of resources and predicates that characterize their authority and navigational (hub) properties with respect to identified topics. We have applied TripleRank to multiple data sets from the linked open data community and gathered encouraging feedback in a user evaluation where TripleRank results have been exploited in a faceted browsing scenario.".
- 279 hasAuthorList authorList.
- 279 hasTopic Semantic_Web.
- 279 isPartOf proceedings.
- 279 title "TripleRank: Ranking Semantic Web Data By Tensor Decomposition".
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- item-1 hasContent thomas-franz.
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