Ontology alignment

Ontology alignment, or ontology matching, is the process of determining correspondences between concepts in ontologies. A set of correspondences is also called an alignment. The phrase takes on a slightly different meaning, in computer science, cognitive science or philosophy.

Computer science

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[6] or COMA++ obtained at the moment very high precision and recall.[3] The Ontology Alignment Evaluation Initiative aims to evaluate, compare and improve the different approaches.

Formal definition

Given two ontologies $i=\langle C_{i},R_{i},I_{i},T_{i},V_{i}\rangle$ and $j=\langle C_{j},R_{j},I_{j},T_{j},V_{j}\rangle$ where $C$ is the set of classes, $R$ is the set of relations, $I$ is the set of individuals, $T$ is the set of data types, and $V$ is the set of values, we can define different types of (inter-ontology) relationships.[1] 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 $s\in [0,1]$ , describing the similarity of two terms of the input ontologies $i$ and $j$ . 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 $m=\langle id,t_{i},t_{j},s\rangle$ , where $t_{i}$ and $t_{j}$ are homogeneous ontology terms, $s$ is the similarity degree of $m$ . A (subsumption, homogeneous, atomic) mapping is defined as a pair $\mu =\langle t_{i},t_{j}\rangle$ , where $t_{i}$ and $t_{j}$ are homogeneous ontology terms.

Cognitive science

For cognitive scientists interested in ontology alignment, the "concepts" are nodes in a semantic network that reside in brains as "conceptual systems." The focal question is: if everyone has unique experiences and thus different semantic networks, then how can we ever understand each other? This question has been addressed by a model called ABSURDIST (Aligning Between Systems Using Relations Derived Inside Systems for Translation). Three major dimensions have been identified for similarity as equations for "internal similarity, external similarity, and mutual inhibition."[7]

Ontology alignment methods

Two sub research fields have emerged in ontology mapping, namely monolingual ontology mapping and cross-lingual ontology mapping. The former refers to the mapping of ontologies in the same natural language, whereas the latter refers to "the process of establishing relationships among ontological resources from two or more independent ontologies where each ontology is labelled in a different natural language".[8] Existing matching methods in monolingual ontology mapping are discussed in Euzenat and Shvaiko (2007).[1] Approaches to cross-lingual ontology mapping are presented in Fu et al. (2011).[9]

gollark: Don't actually link that directly because it's indirectly identifying information or something.

gollark: * W[REDACTED]

gollark: Project VERSIONED WILDCARD.

gollark: No, we just organize this stuff in a separate secret Discord chat to make it seem that way.

gollark: Mr W[REDACTED], the other one, and the other other one?

References

Jérôme Euzenat and Pavel Shvaiko. 2013. Ontology matching Archived 2010-01-16 at the Wayback Machine, Springer-Verlag, 978-3-642-38720-3.
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
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
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.
A. H. Doan, A. Y. Halevy. Semantic integration research in the database community: A brief survey. AI magazine, 26(1), 2005
Carlo A. Curino and Giorgio Orsi and Letizia Tanca (2007). "X-SOM: A Flexible Ontology Mapper" (PDF). International Workshop on Semantic Web Architectures for Enterprises (SWAE'07) in Conjunction with the 18th International Conference on Database and Expert Systems Applications (DEXA'07). Archived from the original (PDF) on February 13, 2012.
R. Goldstone and B. Rogosky. 2002. Using relations within conceptual systems to translate across conceptual systems. Cognition 84, pp. 295–320.
Bo Fu, Rob Brennan, Declan O'Sullivan, A Configurable Translation-Based Cross-Lingual Ontology Mapping System to adjust Mapping Outcomes. Journal of Web Semantics, Volume 15, 15-36, ISSN 1570-8268, 2012 .
Fu B., Brennan R., O'Sullivan D., Using Pseudo Feedback to Improve Cross-Lingual Ontology Mapping . In Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011), LNCS 6643, pp.336-351, Heraklion, Greece, May 2011.

External links

ITM Align: semi-automated ontology alignment
Optima: Visual ontology alignment tool
CogZ: Cognitive support and visualization for human-guided mapping systems
AgreementMaker: Matching for large real-world schemas and ontologies
Biomixer: A web-based collaborative ontology visualization tool
SDI(Semantic Data Integration) Tool: Semantic mapping representation and generation tool using UML for system engineers

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[Euzenat_Shvaiko-1] Jérôme Euzenat and Pavel Shvaiko. 2013. Ontology matching Archived 2010-01-16 at the Wayback Machine, Springer-Verlag, 978-3-642-38720-3.

[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

[coma-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

[curino-xsom2007-6] Carlo A. Curino and Giorgio Orsi and Letizia Tanca (2007). "X-SOM: A Flexible Ontology Mapper" (PDF). International Workshop on Semantic Web Architectures for Enterprises (SWAE'07) in Conjunction with the 18th International Conference on Database and Expert Systems Applications (DEXA'07). Archived from the original (PDF) on February 13, 2012.

[7] R. Goldstone and B. Rogosky. 2002. Using relations within conceptual systems to translate across conceptual systems. Cognition 84, pp. 295–320.

[8] Bo Fu, Rob Brennan, Declan O'Sullivan, A Configurable Translation-Based Cross-Lingual Ontology Mapping System to adjust Mapping Outcomes. Journal of Web Semantics, Volume 15, 15-36, ISSN 1570-8268, 2012 .

[9] Fu B., Brennan R., O'Sullivan D., Using Pseudo Feedback to Improve Cross-Lingual Ontology Mapping . In Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011), LNCS 6643, pp.336-351, Heraklion, Greece, May 2011.