A Graph-Oriented Model for Articulation of Ontology Interdependencies

Mitra, Prasenjit; Wiederhold, Gio; Kersten, Martin

doi:10.1007/3-540-46439-5_6

Cited by 197 publications

(131 citation statements)

References 4 publications

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“…Original work on mapping is presented by [19] in their tool ONION, which uses inferencing to execute mappings, but is based on manually assigned mappings or very simple heuristics. An interesting approach for schema and ontology mapping is presented by [20].…”

Section: Related Workmentioning

confidence: 99%

QOM – Quick Ontology Mapping

Ehrig

Staab

2004

The Semantic Web – ISWC 2004

285

161

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Abstract. (Semi-)automatic mapping -also called (semi-)automatic alignment -of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. We here consider QOM, Quick Ontology Mapping, as a way to trade off between effectiveness (i.e. quality) and efficiency of the mapping generation algorithms. We show that QOM has lower run-time complexity than existing prominent approaches. Then, we show in experiments that this theoretical investigation translates into practical benefits. While QOM gives up some of the possibilities for producing high-quality results in favor of efficiency, our experiments show that this loss of quality is marginal.

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Section: Related Workmentioning

confidence: 99%

QOM – Quick Ontology Mapping

Ehrig

Staab

2004

The Semantic Web – ISWC 2004

285

161

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“…The ONION system semi-automatically generates articulation rules to represent the semantic implication between terms across ontologies based on a graph-oriented model extended with some algebraic operators [4]. The bottom-up FCA-MERGE approach offers a structural description of the global merging process under a mathematical framework including the computation of the pruned concept lattice [5].…”

Section: Approaches To Aligning Ontologiesmentioning

confidence: 99%

“…An additional resource used in the alignment is the Unified Medical Language System ® (UMLS ® ) 4 developed by NLM. The UMLS Metathesaurus ® is organized by concept or meaning.…”

Section: The Umls®mentioning

confidence: 99%

Comparing two approaches for aligning representations of anatomy

Zhang

Mork

Bodenreider

et al. 2007

Artificial Intelligence in Medicine

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Objective-To analyze the comparison, through their results, of two distinct approaches applied to aligning two representations of anatomy.Materials-Both approaches use a combination of lexical and structural techniques. In addition, the first approach takes advantage of domain knowledge, while the second approach treats alignment as a special case of schema matching. The same versions of FMA and GALEN were aligned by each approach. 2199 concept matches were obtained by both approaches.Methods and results-For matches identified by one approach only (337 and 336 respectively), we analyzed the reasons that caused the other approach to fail. Conclusions-The first approach could be improved by addressing partial lexical matches and identifying matches based solely on structural similarity. The second approach may be improved by taking into account synonyms in FMA and identifying semantic mismatches. However, only 33% of the possible one-to-one matches among anatomical concepts were identified by the two approaches together. New directions need to be explored in order to handle more complex matches.

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“…Examples of such ontolo- gies are the Unified Medical Language System (UMLS) from the National Library of Medicine 9 and the Biological Process Ontology from the GeneOntology Consortium 10 . In the presence of multiple ontologies, articulations, i.e., mappings between different source ontologies [MWK00] can be used to register with the mediator information about inter-source relationships. Note that a source S usually cannot see all of the above components (i-iii) when defining its conceptual model: While S sees the mediator's ontologies ONT(M ) and thus can define its own conceptual model CM(S) relative to the mediator's ontology in a local-as-view (LAV) fashion, it cannot directly employ another source's conceptual model CM(S ), nor can it query the mediator's integrated view IVD(M ) which is defined global-as-view (GAV) on top of the sources.…”

Section: Interplay Between Mediator and Sourcesmentioning

confidence: 99%