Matching unstructured vocabularies using a background ontology

Zharko Aleksovski; Michel Klein; Warner Ten Kate; Frank Van Harmelen

doi:10.1007/11891451_18

Matching unstructured vocabularies using a background ontology

Zharko Aleksovski^*
, Michel Klein
, Warner Ten Kate
, Frank Van Harmelen

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Existing ontology matching algorithms use a combination of lexical and structural correspondence between source and target ontologies. We present a realistic case-study where both types of overlap are low: matching two unstructured lists of vocabulary used to describe patients at Intensive Care Units in two different hospitals. We show that indeed existing matchers fail on our data. We then discuss the use of background knowledge in ontology matching problems. In particular, we discuss the case where the source and the target ontology are of poor semantics, such as flat lists, and where the background knowledge is of rich semantics, providing extensive descriptions of the properties of the concepts involved. We evaluate our results against a Gold Standard set of matches that we obtained from human experts.

Original language	English
Title of host publication	Managing Knowledge in a World of Networks - 15th International Conference, EKAW 2006, Proceedings
Publisher	Springer/Verlag
Pages	182-197
Number of pages	16
Volume	4248 LNAI
ISBN (Print)	3540463631, 9783540463634
DOIs	https://doi.org/10.1007/11891451_18
Publication status	Published - 2006
Event	15th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2006 - Podebrady, Czech Republic Duration: 2 Oct 2006 → 6 Oct 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4248 LNAI
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	15th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2006
Country/Territory	Czech Republic
City	Podebrady
Period	2/10/06 → 6/10/06

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 1 No Poverty

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10.1007/11891451_18Licence: Unspecified

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Aleksovski, Z., Klein, M., Ten Kate, W., & Van Harmelen, F. (2006). Matching unstructured vocabularies using a background ontology. In Managing Knowledge in a World of Networks - 15th International Conference, EKAW 2006, Proceedings (Vol. 4248 LNAI, pp. 182-197). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4248 LNAI). Springer/Verlag. https://doi.org/10.1007/11891451_18

Aleksovski, Zharko ; Klein, Michel ; Ten Kate, Warner et al. / Matching unstructured vocabularies using a background ontology. Managing Knowledge in a World of Networks - 15th International Conference, EKAW 2006, Proceedings. Vol. 4248 LNAI Springer/Verlag, 2006. pp. 182-197 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Matching unstructured vocabularies using a background ontology",

abstract = "Existing ontology matching algorithms use a combination of lexical and structural correspondence between source and target ontologies. We present a realistic case-study where both types of overlap are low: matching two unstructured lists of vocabulary used to describe patients at Intensive Care Units in two different hospitals. We show that indeed existing matchers fail on our data. We then discuss the use of background knowledge in ontology matching problems. In particular, we discuss the case where the source and the target ontology are of poor semantics, such as flat lists, and where the background knowledge is of rich semantics, providing extensive descriptions of the properties of the concepts involved. We evaluate our results against a Gold Standard set of matches that we obtained from human experts.",

author = "Zharko Aleksovski and Michel Klein and \{Ten Kate\}, Warner and \{Van Harmelen\}, Frank",

year = "2006",

doi = "10.1007/11891451\_18",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Managing Knowledge in a World of Networks - 15th International Conference, EKAW 2006, Proceedings",

note = "15th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2006 ; Conference date: 02-10-2006 Through 06-10-2006",

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Aleksovski, Z, Klein, M, Ten Kate, W & Van Harmelen, F 2006, Matching unstructured vocabularies using a background ontology. in Managing Knowledge in a World of Networks - 15th International Conference, EKAW 2006, Proceedings. vol. 4248 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4248 LNAI, Springer/Verlag, pp. 182-197, 15th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2006, Podebrady, Czech Republic, 2/10/06. https://doi.org/10.1007/11891451_18

Matching unstructured vocabularies using a background ontology. / Aleksovski, Zharko; Klein, Michel; Ten Kate, Warner et al.
Managing Knowledge in a World of Networks - 15th International Conference, EKAW 2006, Proceedings. Vol. 4248 LNAI Springer/Verlag, 2006. p. 182-197 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4248 LNAI).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AB - Existing ontology matching algorithms use a combination of lexical and structural correspondence between source and target ontologies. We present a realistic case-study where both types of overlap are low: matching two unstructured lists of vocabulary used to describe patients at Intensive Care Units in two different hospitals. We show that indeed existing matchers fail on our data. We then discuss the use of background knowledge in ontology matching problems. In particular, we discuss the case where the source and the target ontology are of poor semantics, such as flat lists, and where the background knowledge is of rich semantics, providing extensive descriptions of the properties of the concepts involved. We evaluate our results against a Gold Standard set of matches that we obtained from human experts.

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Aleksovski Z, Klein M, Ten Kate W, Van Harmelen F. Matching unstructured vocabularies using a background ontology. In Managing Knowledge in a World of Networks - 15th International Conference, EKAW 2006, Proceedings. Vol. 4248 LNAI. Springer/Verlag. 2006. p. 182-197. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11891451_18

Matching unstructured vocabularies using a background ontology

Abstract

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UN SDGs

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