308:, for example, "truck" and "lorry". The classes are not necessarily logically identical. According to Euzenat and Shvaiko (2007), 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.
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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
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resources from two or more independent ontologies where each ontology is labelled in a different natural language". Existing matching methods in monolingual ontology mapping are discussed in
Euzenat and Shvaiko (2007). Approaches to cross-lingual ontology mapping are presented in Fu et al. (2011).
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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
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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.
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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?
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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
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is the set of values, we can define different types of (inter-ontology) relationships. Such relationships will be called, all together, alignments and can be categorized among different dimensions:
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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)
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Inside
Systems for Translation). Three major dimensions have been identified for similarity as equations for "internal similarity, external similarity, and mutual inhibition."
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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
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Collection of surveys and research papers related to ontology mapping, matching, and alignment
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Using relations within conceptual systems to translate across conceptual systems
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The references used may be made clearer with a different or consistent style of
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CogZ: Cognitive support and visualization for human-guided mapping systems
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Semantic integration research in the database community: A brief survey
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type of alignment: the semantics associated to an alignment. It can be
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Database Schema
Matching Using Machine Learning with Feature Selection
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Ontology alignment tools have generally been developed to operate on
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Process of determining correspondences between concepts in ontologies
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AgreementMaker: Matching for large real-world schemas and ontologies
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interested in ontology alignment, the "concepts" are nodes in a
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aims to evaluate, compare and improve the different approaches.
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atomic vs complex: whether the alignments we considered are
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Semantic integration: a survey of ontology-based approaches
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Carlo A. Curino and
Giorgio Orsi and Letizia Tanca (2007).
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329:entity-relationship models
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341:Notation 3
321:taxonomies
302:ontologies
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262:ontologies
201:footnoting
151:April 2019
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290:integrate
56:talk page
1399:Biomixer
1385:Archived
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1097:See also
1013:, where
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675:mapping)
258:concepts
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979:t
972:=
949:m
929:s
907:j
903:t
880:i
876:t
852:s
849:,
844:j
840:t
836:,
831:i
827:t
823:,
820:d
817:i
811:=
808:m
775:j
755:i
735:]
732:1
729:,
726:0
723:[
717:s
636:V
616:T
596:I
576:R
556:C
531:j
527:V
523:,
518:j
514:T
510:,
505:j
501:I
497:,
492:j
488:R
484:,
479:j
475:C
468:=
465:j
440:i
436:V
432:,
427:i
423:T
419:,
414:i
410:I
406:,
401:i
397:R
393:,
388:i
384:C
377:=
374:i
240:)
234:(
222:)
216:(
211:)
207:(
203:.
193:.
164:)
158:(
153:)
149:(
139:·
132:·
125:·
118:·
91:.
66:)
62:(
20:)
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