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Mao, M.: Ontology mapping : towards semantic interoperability in distributed and heterogeneous environments (2008)
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- Abstract
- This dissertation studies ontology mapping: the problem of finding semantic correspondences between similar elements of different ontologies. In the dissertation, elements denote classes or properties of ontologies. The goal of this research is to use ontology mapping to make heterogeneous information more accessible. The World Wide Web (WWW) now is widely used as a universal medium for information exchange. Semantic interoperability among different information systems in the WWW is limited due to information heterogeneity, and the non semantic nature of HTML and URLs. Ontologies have been suggested as a way to solve the problem of information heterogeneity by providing formal, explicit definitions of data and reasoning ability over related concepts. Given that no universal ontology exists for the WWW, work has focused on finding semantic correspondences between similar elements of different ontologies, i.e., ontology mapping. Ontology mapping can be done either by hand or using automated tools. Manual mapping becomes impractical as the size and complexity of ontologies increases. Full or semi-automated mapping approaches have been examined by several research studies. Previous full or semiautomated mapping approaches include analyzing linguistic information of elements in ontologies, treating ontologies as structural graphs, applying heuristic rules and machine learning techniques, and using probabilistic and reasoning methods etc. In this paper, two generic ontology mapping approaches are proposed. One is the PRIOR+ approach, which utilizes both information retrieval and artificial intelligence techniques in the context of ontology mapping. The other is the non-instance learning based approach, which experimentally explores machine learning algorithms to solve ontology mapping problem without requesting any instance. The results of the PRIOR+ on different tests at OAEI ontology matching campaign 2007 are encouraging. The non-instance learning based approach has shown potential for solving ontology mapping problem on OAEI benchmark tests.
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Mitchell, J.S.; Panzer, M.: Dewey linked data : Making connections with old friends and new acquaintances (2012)
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- Abstract
- This paper explores the history, uses cases, and future plans associated with availability of the Dewey Decimal Classification (DDC) system as linked data. Parts of the Dewey Decimal Classification (DDC) system have been available as linked data since 2009. Initial efforts included the DDC Summaries (the top three levels of the DDC) in eleven languages exposed as linked data in dewey.info. In 2010, the content of dewey.info was further extended by the addition of assignable numbers and captions from the Abridged Edition 14 data files in English, Italian, and Vietnamese. During 2012, we will add assignable numbers and captions from the latest full edition database, DDC 23. In addition to the "old friends" of different Dewey language versions, institutions such as the British Library and Deutsche Nationalbibliothek have made use of Dewey linked data in bibliographic records and authority files, and AGROVOC has linked to our data at a general level. We expect to extend our linked data network shortly to "new acquaintances" such as GeoNames, ISO 639-3 language codes, and Mathematics Subject Classification. In particular, we will examine the linking process to GeoNames as an example of cross-domain vocabulary alignment. In addition to linking plans, we report on use cases that facilitate machine-assisted categorization and support discovery in the Semantic Web environment.
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Neubauer, G.: Visualization of typed links in linked data (2017)
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Neumaier, S.: Data integration for open data on the Web (2017)
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