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- item-4 hasContent yuzhang-feng.
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- 63 creator gang-wu.
- 63 creator juanzi-li.
- 63 creator kehong-wang.
- 63 creator ling-feng.
- 63 type InProceedings.
- 63 label "Identifying Potentially Important Concepts and Relations in an Ontology".
- 63 sameAs 63.
- 63 abstract "More and more ontologies have been published and used widely on the web such as FOAF, CYC and WordNet. In order to make good use of an ontology, especially a new and large scale ontology, we need methods to help under- stand it first. Identifying potentially important concepts and relations in an ontology is an intuitive but challeng- ing method. In this paper, inspired by the Stream of Con- sciousness theory, we first define six features for potential important concepts and relation from the ontological struc- tural point of view. Then a simple yet effective Concept- And-Relation-Ranking (CARRank) algorithm is proposed to simultaneously rank the importance of concepts and rela- tions. Different from the traditional ranking methods, the importance of concepts and the weights of relations reinforce one another in CARRank in an iterative manner. Such an iterative process is proved to be convergent both in princi- ple and by experiments. Our experimental results show that CARRank has a similar convergent speed as the PageRank- like algorithms, but a more reasonable ranking result.".
- 63 hasAuthorList authorList.
- 63 hasTopic Semantic_Web.
- 63 isPartOf proceedings.
- 63 keyword "Ontology".
- 63 keyword "Ranking".
- 63 title "Identifying Potentially Important Concepts and Relations in an Ontology".
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- item-1 hasContent gang-wu.
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- 76 creator frank-van-harmelen.
- 76 creator zhisheng-huang.
- 76 type InProceedings.
- 76 label "Using Semantic Distances for Reasoning with Inconsistent Ontologies".
- 76 sameAs 76.
- 76 abstract "Re-using and combining multiple ontologies on the Web is bound to lead to inconsistencies between the combined vocabularies. Even many of the ontologies that are in use today turn out to be inconsistent once some of their implicit knowledge is made explicit. However, robust and efficient methods to deal with inconsistencies are lacking from current Semantic Web reasoning systems, which are typically based on classical logic. In earlier papers, we have proposed the use of syntactic relevance functions as a method for reasoning with inconsistent ontologies. In this paper, we extend that work to the use of semantic distances. We show how Google distances can be used to develop semantic relevance functions to reason with inconsistent ontologies. In essence we are using the implicit knowledge hidden in the Web for explicit reasoning purposes. We have implemented this approach as part of the PION reasoning system. We report on experiments with several realistic ontologies. The test results show that a mixed syntactic/semantic approach can significantly improve reasoning performance over the purely syntactic approach. Furthermore, our methods allow to trade-off computational cost for inferential completeness. Our experiment shows that we only have to give up a little quality to obtain a high performance gain.".
- 76 hasAuthorList authorList.
- 76 hasTopic Semantic_Web.
- 76 isPartOf proceedings.
- 76 keyword "Cost/Quality tradeoff".
- 76 keyword "Inconsistency".
- 76 keyword "Semantic relevance".
- 76 title "Using Semantic Distances for Reasoning with Inconsistent Ontologies".
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- 91 creator adam-funk.
- 91 creator ahmad-iqbal.
- 91 creator brian-davis.
- 91 creator hamish-cunningham.
- 91 creator kalina-bontcheva.
- 91 creator siegfried-handschuh.
- 91 creator valentin-tablan.
- 91 type InProceedings.
- 91 label "RoundTrip Ontology Authoring".
- 91 sameAs 91.
- 91 abstract "Controlled Language (CL) for Ontology Editing tools offer an attractive alternative for naive users wishing to create ontologies, but they are still required to spend time learning the correct syntactic structures and vocabulary in order to use the Controlled Language properly. This paper extends previous work (CLOnE) which uses standard NLP tools to process the language and manipulate an ontology. Here we also generate text in the CL from an existing ontology using template-based (or shallow) Natural Language Generation (NLG). The text generator and the CLOnE authoring process combine to form a RoundTrip Ontology Authoring environment: one can start with an existing imported ontology or one originally produced using CLOnE, (re)produce the Controlled Language, modify or edit the text as required and then turn the text back into the ontology in the CLOnE environment. Building on previous methodology we undertook an evaluation, comparing the RoundTrip Ontology Authoring process with a well-known ontology editor; where previous work required a CL reference manual with several examples in order to use the controlled language, the use of NLG reduces this learning curve for users and improves on existing results for basic ontology editing tasks.".
- 91 hasAuthorList authorList.
- 91 hasTopic Semantic_Web.
- 91 isPartOf proceedings.
- 91 keyword "Controlled Languages".
- 91 keyword "Natural Language Generation".
- 91 keyword "Natural Language Processing".
- 91 keyword "Ontology Based Information Extraction".
- 91 keyword "Ontology Engineering".
- 91 title "RoundTrip Ontology Authoring".
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