Putting Ontology Alignment in Context: Usage Scenarios, Deployment and Evaluation in a Library Case

Isaac, Antoine; Matthezing, Henk; Meij, Leander van der; Schlobach, Stefan; Wang, Shenghui; Zinn, Claus

doi:10.1007/978-3-540-68234-9_31

Cited by 20 publications

(20 citation statements)

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“…At fi rst sight, the results were disappointing. 6 For the best systems, nearly half of the generated Brinkman concepts were incorrect (precision = 54 percent for Silas without relatedMatch). Recall is weak as well, as more than 60 percent of the gold standard was not found (recall = 39 percent for Silas with relatedMatch).…”

Section: Allergens From Cats and Dogsmentioning

confidence: 99%

“…During OAEI 2007, we evaluated the three participants in the setting of the merging scenario. 6 By comparing concept labels (literal string matching) and by exploiting a Dutch morphology database to recognize word variants (that is, singular and plural forms), we constructed a reference alignment of 3,659 equivalence mappings between the GTT and Brinkman thesauri. We also did a representative sampling from the alignments produced by the three participants that were not in the original reference alignment and manually evaluated an additional 330 mappings.…”

Section: Thesaurus Merging/integrationmentioning

confidence: 99%

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Evaluating Thesaurus Alignments for Semantic Interoperability in the Library Domain

et al. 2009

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Published by the IEEE Computer Societyother controlled vocabularies) specific to fields, institutions, and even collections. The desire to make CH resources available to the general public (for example, see www. europeana.eu) increases the need to facilitate interoperability across different contexts.By providing representational standards such as the Simple Knowledge Organization System (SKOS; www.w3.org/2004/02/skos) and generic tool support, the Semantic Web community has taken a prominent role in this facilitation. Its ontology-matching branch aims at developing technology to produce alignments-that is, sets of semantic mappings bet ween elements from different vocabularies. 1 One can exploit alignments, for instance, to access a collection via thesauri it is not originally indexed with, to interconnect distributed, differently annotated collections on the object level, or to merge two thesauri to rationalize thesaurus maintenance.Unfortunately, our experience shows that existing matching tools often do not perform well in CH applications. 2 We believe part of the problem is that they strive for generality. To this end, we argue that the generation and evaluation of thesaurus alignments must take into account well-understood realworld application contexts and their specific requirements. Ontology MatchingOntology matching aims at determining the semantic relations bet ween elements of two given knowledge organization systems (for example, ontologies and thesauri). 1 The set of semantic relations usually comprises concept equivalence, hierarchical concept links

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Section: Allergens From Cats and Dogsmentioning

confidence: 99%

Section: Thesaurus Merging/integrationmentioning

confidence: 99%

Evaluating Thesaurus Alignments for Semantic Interoperability in the Library Domain

et al. 2009

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“…7 The second evaluation method is novel: a bidirectional reindexing method based on the unidirectional method proposed in [14]. In this method a separate set of dually annotated instances is used to measure the correctness of mappings.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

“…14 In this scenario we match the controlled vocabularies of the English and French national libraries, using their book catalogs as collections of instances.…”

Section: Datasetsmentioning

confidence: 99%

Instance-Based Ontology Matching by Instance Enrichment

et al. 2012

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The ontology matching (OM) problem is an important barrier to achieve true Semantic Interoperability. Instance-based ontology matching (IBOM) uses the extension of concepts, the instances directly associated with a concept, to determine whether a pair of concepts is related or not. While IBOM has many strengths it requires instances that are associated with concepts of both ontologies, (i.e) dually annotated instances. In practice, however, instances are often associated with concepts of a single ontology only, rendering IBOM rarely applicable. In this paper we discuss a method that enables IBOM to be used on two disjoint datasets, thus making it far more generically applicable. This is achieved by enriching instances of each dataset with the conceptual annotations of the most similar instances from the other dataset, creating artificially dually annotated instances. We call this technique instance-based ontology matching by instance enrichment (IBOMbIE ). We have applied the IBOMbIE algorithm in a real-life use-case where large datasets are used to match the ontologies of European libraries. Existing gold standards and dually annotated instances are used to test the impact and significance of several design choices of the IBOMbIE algorithm. Finally, we compare the IBOMbIE algorithm to other ontology matching algorithms.

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“…This offers a cost effective approach for dealing with thesauri for STAR purposes. For other recent work employing SKOS see, for example, Isaac et al [10] on aligning thesauri and Tuominen et al [11] on various SKOS based semantic web services.…”

Section: Skos Based Terminology Servicesmentioning

confidence: 99%

Implementing Archaeological Time Periods Using CIDOC CRM and SKOS

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2010

Lecture Notes in Computer Science

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Abstract. Within the archaeology domain, datasets frequently refer to time periods using a variety of textual or numeric formats. Traditionally controlled vocabularies of time periods have used classification notation and the collocation of terms in the printed form to represent and convey tacit information about the relative order of concepts. The emergence of the semantic web entails encoding this knowledge into machine readable forms, and so the meaning of this informal ordering arrangement can be lost. Conversion of controlled vocabularies to Simple Knowledge Organisation System (SKOS) format provides a formal basis for semantic web indexing but does not facilitate chronological inference -as thesaurus relationship types are an inappropriate mechanism to fully describe temporal relationships. This becomes an issue in archaeological data where periods are often described in terms of (e.g.) named monarchs or emperors, without additional information concerning relative chronological context. An exercise in supplementing existing controlled vocabularies of time period concepts with dates and temporal relationships was undertaken as part of the Semantic Technologies for Archaeological Resources (STAR) project. The general aim of the STAR project is to demonstrate the potential benefits in cross searching archaeological data conforming to a common overarching conceptual data structure schema -the CIDOC Conceptual Reference Model (CRM). This paper gives an overview of STAR applications and services and goes on to particularly focus on issues concerning the extraction and representation of time period information.

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Putting Ontology Alignment in Context: Usage Scenarios, Deployment and Evaluation in a Library Case

Cited by 20 publications

References 5 publications

Evaluating Thesaurus Alignments for Semantic Interoperability in the Library Domain

Evaluating Thesaurus Alignments for Semantic Interoperability in the Library Domain

Instance-Based Ontology Matching by Instance Enrichment

Implementing Archaeological Time Periods Using CIDOC CRM and SKOS

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