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- 24 creator bo-fu.
- 24 creator declan-osullivan.
- 24 creator rob-brennan.
- 24 type InProceedings.
- 24 label "Using Pseudo Feedback to Improve Cross-Lingual Ontology Mapping".
- 24 sameAs 24.
- 24 abstract "While ontologies are widely accepted internationally as knowledge management mechanism across disciplines, the ability to reason over knowledge bases regardless of the natural languages used in them has become a pressing issue in digital content management. To enable knowledge sharing and reuse, ontology mapping techniques must be able to work with otherwise isolated ontologies that are labelled in diverse natural languages. Machine translation techniques are often employed by cross-lingual ontology mapping approaches to turn a cross-lingual mapping problem into a monolingual mapping problem which can then be solved by state of the art monolingual ontology matching tools. However in the process of doing so, complications introduced by machine translation tools can compromise the performance of the subsequent monolingual matching techniques. In this paper, a novel approach to improve the quality of cross-lingual ontology mapping is presented and evaluated. The proposed approach adopts the pseudo feedback technique that is similar to the well understood relevance feedback mechanism used in the field of information retrieval. It is shown through the evaluation that the pseudo feedback feature can enhance the effectiveness of machine translation and monolingual matching techniques in a cross-lingual ontology mapping scenario.".
- 24 hasAuthorList authorList.
- 24 isPartOf proceedings.
- 24 keyword "Cross-Lingual Ontology Mapping".
- 24 keyword "Machine Translation".
- 24 keyword "Pseudo Feedback".
- 24 title "Using Pseudo Feedback to Improve Cross-Lingual Ontology Mapping".
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- 56 creator carlo-allocca.
- 56 type InProceedings.
- 56 label "Automatic Identification of Ontology Versions Using Machine Learning Techniques".
- 56 sameAs 56.
- 56 abstract "When different versions of an ontology are published online, the links between them are often lost as the standard mechanisms (such as owl:versionInfo and owl:priorVersion) to expose these links are rarely used. This generates issues in scenarios where people or applications are required to make use of large scale, heterogenous ontology collections, implicitly containing multiple versions of ontologies. In this paper, we propose a method to detect automatically versioning links between ontologies which are available online through a Semantic Web search engine. Our approach is based on two main steps. The first step selects candidate pairs of ontologies by using versioning information expressed in their identifiers. In the second step, these candidate pairs are characterized through a set of features, including similarity measures, and classified by using Machine Learning Techniques, to distinguish the pairs that represent versions from the ones that do not. We discuss the features used, the methodology employed to train the classifiers and the precision obtained when applying this approach on the collection of ontologies of the Watson Semantic Web search engine.".
- 56 hasAuthorList authorList.
- 56 isPartOf proceedings.
- 56 keyword "Machine Learning Techinques".
- 56 keyword "Ontology Versioning".
- 56 keyword "Semantic Web Search Engines".
- 56 title "Automatic Identification of Ontology Versions Using Machine Learning Techniques".
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- 7 creator gang-wu.
- 7 creator guilin-qi.
- 7 creator haofen-wang.
- 7 creator xing-niu.
- 7 creator yong-yu.
- 7 type InProceedings.
- 7 label "Evaluating the Stability and Credibility of Ontology Matching Methods".
- 7 sameAs 7.
- 7 abstract "Ontology matching is one of the key research topics in the field of Semantic Web. In the last few years, many matching methods have been proposed to generate matches between different ontologies either automatically or semi-automatically. To select appropriate ones, users need some measures to judge whether a method can achieve the similar compliance even on one dataset without reference matches and whether such a method is reliable w.r.t. its output result along with the confidence. However, widely-used traditional measures like precision and recall fail to provide sufficient hints on them. In this paper, we design two novel evaluation measures to evaluate stability of matching methods and one measure to evaluate credibility of matching confidence values, which help to answer the above two questions. Additionally, we carry out comparison among several carefully selected methods systematically using our new measures. Besides, we report some interesting findings such as identifying potential defects of our subjects.".
- 7 hasAuthorList authorList.
- 7 isPartOf proceedings.
- 7 keyword "Credibility".
- 7 keyword "Evaluation".
- 7 keyword "Experiments".
- 7 keyword "Ontology Matching".
- 7 keyword "Stability".
- 7 title "Evaluating the Stability and Credibility of Ontology Matching Methods".
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- 11 creator anees-ul-mehdi.
- 11 creator sebastian-rudolph.
- 11 creator stephan-grimm.
- 11 type InProceedings.
- 11 label "Epistemic Queries for OWL Knowledge Bases".
- 11 sameAs 11.
- 11 abstract "Epistemic querying extends standard ontology inferencing by allowing for deductive introspection. We propose a technique for epistemic querying of OWL2 ontologies not featuring nominals and universal roles by a reduction to a series of standard OWL2 reasoning steps thereby enabling the deployment of off-the-shelf OWL2 reasoning tools for this task. We prove formal correctness of our method, justify the omission of nominals and universal role, and provide an implementation as well as evaluation results.".