Ontology Matching Task

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An Ontology Matching Task is a knowledge base record matching task for the ontology records of two or more ontologies.

References

2016a

  • (Wikipedia, 2016) ⇒ http://wikipedia.org/wiki/Ontology_alignment#Computer_Science Retrieved:2016-3-4.
    • For computer scientists, concepts are expressed as labels for data. Historically, the need for ontology alignment arose out of the need to integrate heterogeneous databases, ones developed independently and thus each having their own data vocabulary. In the Semantic Web context involving many actors providing their own ontologies, ontology matching has taken a critical place for helping heterogeneous resources to interoperate. Ontology alignment tools find classes of data that are “semantically equivalent," for example, "Truck" and "Lorry." The classes are not necessarily logically identical. According to Euzenat and Shvaiko (2007),[1] there are three major dimensions for similarity: syntactic, external, and semantic. Coincidentally, they roughly correspond to the dimensions identified by Cognitive Scientists below. A number of tools and frameworks have been developed for aligning ontologies, some with inspiration from Cognitive Science and some independently.

      Ontology alignment tools have generally been developed to operate on database schemas, [2] XML schemas,[3] taxonomies, [4] formal languages, entity-relationship models, [5] dictionaries, and other label frameworks. They are usually converted to a graph representation before being matched. Since the emergence of the Semantic Web, such graphs can be represented in the Resource Description Framework line of languages by triples of the form <subject, predicate, object>, as illustrated in the Notation 3 syntax. In this context, aligning ontologies is sometimes referred to as "ontology matching". The problem of Ontology Alignment has been tackled recently by trying to compute matching first and mapping (based on the matching) in an automatic fashion. Systems like DSSim, X-SOM or COMA++ obtained at the moment very high precision and recall. The Ontology Alignment Evaluation Initiative aims to evaluate, compare and improve the different approaches.

      More recently, a technique useful to minimize the effort in mapping validation and visualization has been presented which is based on Minimal Mappings. Minimal mappings are high quality mappings such that i) all the other mappings can be computed from them in time linear in the size of the input graphs, and ii) none of them can be dropped without losing property i).

  1. ↑ Jérôme Euzenat and Pavel Shvaiko. 2007. Ontology matching, Springer-Verlag, 978-3-540-49611-3.
  2. ↑ J. Berlin and A. Motro. 2002. Database Schema Matching Using Machine Learning with Feature Selection. Proc. of the 14th International Conference on Advanced Information Systems Engineering, pp. 452-466
  3. ↑ D. Aumueller, H. Do, S. Massmann, E. Rahm. 2005. Schema and ontology matching with COMA++. Proc. of the 2005 International Conference on Management of Data, pp. 906-908
  4. ↑ S. Ponzetto, R. Navigli. 2009. "Large-Scale Taxonomy Mapping for Restructuring and Integrating Wikipedia". Proc. of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), Pasadena, California, pp. 2083-2088.
  5. ↑ A. H. Doan, A. Y. Halevy. Semantic integration research in the database community: A brief survey. AI magazine, 26(1), 2005

2016b

  • (Wikipedia, 2016) ⇒ http://wikipedia.org/wiki/Ontology_alignment#Formal_Definition Retrieved:2016-3-4.
    • Given two ontologies [math]\displaystyle{ i=\langle C_{i}, R_{i}, I_{i}, A_{i}\rangle }[/math] and [math]\displaystyle{ j=\langle C_{j}, R_{j}, I_{j}, A_{j}\rangle }[/math] we can define different type of (inter-ontology) relationships among their terms.

      Such relationships will be called, all together, alignments and can be categorized among different dimensions:

      • similarity vs logic: this is the difference between matchings (predicating about the similarity of ontology terms), and mappings (logical axioms, typically expressing logical equivalence or inclusion among ontology terms)
      • atomic vs complex: whether the alignments we considered are one-to-one, or can involve more terms in a query-like formulation (e.g., LAV/GAV mapping)
      • homogeneous vs heterogeneous: do the alignments predicate on terms of the same type (e.g., classes are related only to classes, individuals to individuals, etc.) or we allow heterogeneity in the relationship?
      • type of alignment: the semantics associated to an alignment. It can be subsumption, equivalence, disjointness, part-of or any user-specified relationship.
    • Subsumption, atomic, homogeneous alignments are the building blocks to obtain richer alignments, and have a well defined semantics in every Description Logic.

      Let's now introduce more formally ontology matching and mapping.

      An atomic homogeneous matching is an alignment that carries a similarity degree [math]\displaystyle{ s\in [0,1] }[/math] , describing the similarity of two terms of the input ontologies [math]\displaystyle{ i }[/math] and [math]\displaystyle{ j }[/math] .

      Matching can be either computed, by means of heuristic algorithms, or inferred from other matchings.

      Formally we can say that, a matching is a quadruple [math]\displaystyle{ m=\langle id, t_{i}, t_{j}, s\rangle }[/math] , where [math]\displaystyle{ t_{i} }[/math] and [math]\displaystyle{ t_{j} }[/math] are homogeneous ontology terms, [math]\displaystyle{ s }[/math] is the similarity degree of [math]\displaystyle{ m }[/math] .

      A (subsumption, homogeneous, atomic) mapping is defined as a pair [math]\displaystyle{ \mu=\langle t_{i}, t_{j}\rangle }[/math] , where [math]\displaystyle{ t_{i} }[/math] and [math]\displaystyle{ t_{j} }[/math] are homogeneous ontology terms.


2015a

2015b

2014

2013a

2013b

2007a

2007b

  • (Obitko, 2007) ⇒ Marek Obitko. (2007). “Translations Between Ontologies in Multi-agent Systems - Ontology Operations.” PhD Thesis, Czech Technical University
    • QUOTE: It is possible that one application uses multiple ontologies, especially when using modular design of ontologies or when we need to integrate with systems that use other ontologies. In this case, some operations on ontologies may be needed in order to work with all of them. We will summarize some of these operations. The terminology in this areas is still not stable and different authors may use these terms in a bit shifted meaning, and so the terms may overlap, however, all of these operations are important for maintenance and integration of ontologies.
      • Merge of ontologies (...)
      • Mapping from one ontology to another one is expressing of the way how to translate statements from ontology to the other one. Often it means translation between concepts and relations. In the simplest case it is mapping from one concept of the first ontology to one concept of the second ontology. It is not always possible to do such one to one mapping. Some information can be lost in the mapping. This is permissible, however mapping may not introduce any inconsistencies.
      • Alignment is a process of mapping between ontologies in both directions whereas it is possible to modify original ontologies so that suitable translation exists (i.e., without losing information during mapping). Thus it is possible to add new concepts and relations to ontologies that would form suitable equivalents for mapping. The specification of alignment is called articulation. Alignment, as well as mapping, may be partial only.
      • Refinement is mapping from ontology A to another ontology B so that every concept of ontology A has equivalent in ontology B, however primitive concepts from ontology A may correspond to non-primitive (defined) concepts of ontology B. Refinement defines partial ordering of ontologies.
      • Unification is aligning all of the concepts and relations in ontologies so that inference in one ontology can be mapped to inference in other ontology and vice versa. Unification is usually made as refinement of ontologies in both directions.
      • Integration is a process of (...)

2006a

2006b

2004

2003

  • (Kalfoglou & Schorlemmer, 2003) ⇒ Yannis Kalfoglou, and Marco Schorlemmer. (2003). “Ontology Mapping: the State of the Art.” In: The Knowledge Engineering Review. doi:10.1017/S0269888903000651
    • QUOTE: A total ontology mapping from O1 = (S1,A1) to O2 = (S2,A2) is a morphism f : S1 → S2 of ontological signatures, such that, A2 |= f(A1), i.e., all interpretations that satisfy O2’s axioms also satisfy O1’s translated axioms. This makes an ontology mapping a theory morphism as it is usually defined in the field of algebraic specification (see, for instance, (Meseguer 1989)).
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