Matching Unstructured Vocabularies Using a Background Ontology

Aleksovski, Zharko; Klein, Michel; Kate, Warner ten; Harmelen, Frank van

doi:10.1007/11891451_18

Cited by 96 publications

(88 citation statements)

References 7 publications

(15 reference statements)

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“…Domain specific ontologies are often seen as quality sources of background knowledge. In [15,16], the alignment process takes place in two steps: anchoring and driving relations. Anchoring consists in matching the concepts of the source and target ontologies to the concepts of the reference knowledge using standard ontology matching techniques.…”

Section: Ontology Matching Using Background Knowledgementioning

confidence: 99%

Light-Weight Cross-Lingual Ontology Matching with LYAM++

Tigrine

Bellahsène

Todorov

2015

Lecture Notes in Computer Science

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Abstract. During the last decade, several automatic ontology matching systems were developed to address the problem of ontology heterogeneity. Aligning cross-lingual ontologies is among the current challenging issues in the field of ontology matching. The majority of the existing approaches rely on machine translation to deal with this problem. However, inherent problems of machine translation are imprecision and ambiguity. In this paper, we propose a novel approach to the cross-lingual ontology matching task, relying on the large multilingual semantic network BabelNet as a source of background knowledge to assist the matching process. We have designed and tested a novel orchestration of the components of the matching workflow. Our approach is implemented under the form of a prototype named LYAM++ (Yet Another Matcher-Light)-a fully automatic cross-lingual ontology matching system that does not rely on machine translation. We report the results of our experiments that show that LYAM++ outperforms considerably the best techniques in the state-of-the-art according to the obtained results on the MultiFarm datasets of the Ontology Alignment Evaluation Initiative 2014.

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Section: Ontology Matching Using Background Knowledgementioning

confidence: 99%

Light-Weight Cross-Lingual Ontology Matching with LYAM++

Tigrine

Bellahsène

Todorov

2015

Lecture Notes in Computer Science

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“…A typical use of semantic models in the context of semantic matching are-as in semantic search-the disambiguation of terms. Beyond that semantic models are used to derive implicit semantic relationships between Data items [1] and for detecting inconsistencies that arise due to wrong matches [36].…”

Section: Data Integrationmentioning

confidence: 99%

Data Semantics on the Web

Stuckenschmidt

2012

J Data Semant

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Data Semantics is a wide area that continuously faces new challenges arising from the invention of new information formats and novel applications. An area that is particularly challenging with respect to identifying, representing and using data semantics is the Web. This paper attempts to characterize the nature and challenges of Data Semantics on the Web as an interesting research area to be covered by the Journal on Data Semantics.

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“…[32]). This type of matching is sometimes referred to as 'lexical matching' and is used in cases where the ontologies do not have any instances or structure; e.g., in [1] lexical comparison of labels is used to map both the source and the target ontology to a semantically rich external source of background knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…Semantic query suggestion, in contrast, tries to understand (or learn) which concepts the user used in her query or, phrased alternatively, the concepts she is interested in and wants to find. 1 Moreover, the properties of each concept, and any other resources associated with it, could serve as additional, useful information for the user. In our current work, we use DBpedia as our target ontology.…”

Section: Introductionmentioning

confidence: 99%

Learning Semantic Query Suggestions

Meij

Bron

Hollink

et al. 2009

Lecture Notes in Computer Science

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Abstract. An important application of semantic web technology is recognizing human-defined concepts in text. Query transformation is a strategy often used in search engines to derive queries that are able to return more useful search results than the original query and most popular search engines provide facilities that let users complete, specify, or reformulate their queries. We study the problem of semantic query suggestion, a special type of query transformation based on identifying semantic concepts contained in user queries. We use a feature-based approach in conjunction with supervised machine learning, augmenting term-based features with search history-based and concept-specific features. We apply our method to the task of linking queries from real-world query logs (the transaction logs of the Netherlands Institute for Sound and Vision) to the DBpedia knowledge base. We evaluate the utility of different machine learning algorithms, features, and feature types in identifying semantic concepts using a manually developed test bed and show significant improvements over an already high baseline. The resources developed for this paper, i.e., queries, human assessments, and extracted features, are available for download.

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Matching Unstructured Vocabularies Using a Background Ontology

Cited by 96 publications

References 7 publications

Light-Weight Cross-Lingual Ontology Matching with LYAM++

Light-Weight Cross-Lingual Ontology Matching with LYAM++

Data Semantics on the Web

Learning Semantic Query Suggestions

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