By Jérôme Euzenat
Ontologies are typically came upon in every single place. they're considered because the silver bullet for lots of functions, corresponding to database integration, peer-to-peer structures, e-commerce, semantic internet providers, or social networks. despite the fact that, in open or evolving platforms, resembling the semantic internet, diverse events may, as a rule, undertake diversified ontologies. hence, simply utilizing ontologies, like utilizing XML, doesn't lessen heterogeneity: it simply increases heterogeneity difficulties to the next level.
Euzenat and Shvaiko’s booklet is dedicated to ontology matching as an answer to the semantic heterogeneity challenge confronted via desktops. Ontology matching goals at discovering correspondences among semantically similar entities of alternative ontologies. those correspondences may possibly stand for equivalence in addition to different relatives, comparable to final result, subsumption, or disjointness, among ontology entities. many alternative matching ideas were proposed to this point from a variety of viewpoints, e.g., databases, details platforms, and synthetic intelligence.
The moment version of Ontology Matching has been completely revised and up-to-date to mirror the newest advances during this speedy constructing region, which led to greater than a hundred and fifty pages of recent content material. particularly, the ebook contains a new bankruptcy devoted to the method for appearing ontology matching. It additionally covers rising issues, similar to info interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and consumer involvement in matching, to say a number of. greater than a hundred cutting-edge matching structures and frameworks have been reviewed.
With Ontology Matching, researchers and practitioners will discover a reference publication that offers at present on hand paintings in a uniform framework. specifically, the paintings and the recommendations provided during this e-book may be both utilized to database schema matching, catalog integration, XML schema matching and different comparable difficulties. The pursuits of the ebook comprise proposing (i) the cutting-edge and (ii) the newest study ends up in ontology matching by way of offering a scientific and designated account of matching concepts and matching structures from theoretical, sensible and alertness perspectives.
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Ontology repair (McNeill 2006) and web APIs discovery (Taheriyan et al. 2012), or new ones, such as data interlinking on the web of data. Since semantic heterogeneity is an intrinsic problem of any application involving more than one party, it is reasonable to consider ontology matching as a unified object of study. However, there are notable differences in the way these applications use matching. Application-related differences must be clearly identified in order to provide the best suited solution in each case.
2006a). Of course, each independent entity tends to develop its own directory based on its own needs and tastes (see Fig. 3). Fig. 3 Fragments of two directories. In Fig. 3, the directory on the left represents the set of items of a bookstore or a cultural good seller, while the one on the right is the directory of a person that illustrates the content of his or her personal library. com. org. , selling versus classifying. Finally, there exist some consensus classifications. In library science, the Dewey classification has been used for more than a century for classifying books by topics (Chan et al.
However, they do not specify the actual content of the message, which is often expressed with respect to some ontology accessible to the agent. Current standards for expressing these messages provide slots for declaring the content language and the ontology used. As a consequence, when two autonomous and independently designed agents meet, they have the opportunity to exchange messages but little chance to understand one another if they do not share the same content language and ontology. It is thus useful to help these agents to match their ontologies in order to either translate their messages or integrate bridge axioms in their own models.