Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

http://sipi.usc.edu/~mendel/publications/Mendel%20INS%202007%20AT2.pdf

Advances in type-2 fuzzy sets and systems

This invention relates to an advertisement machine which provides advertisements to a user searching for desired information within a data network. The machine receives, from a user, a search request including a search argument corresponding to the desired information and searches, based upon the received search argument, a first database having data network related information to generate search results. It also correlates the received search argument to a particular advertisement in a second database having advertisement related information. The search results together with the particular advertisement are provided by the machine to the user.

Associative search engine

An efficient centroid type-reduction strategy for general type-2 fuzzy logic system

We present a literature review regarding articles on ontology matching published in the last decade.It serves the purpose of offering an up-to-date review of the field and showing its evolution trends.Over 1600 papers have been sorted according to a classification framework that we have defined.This framework helps in identifying the distribution of the load work in the last decade.Practitioners have been consulted to contrast and validate the results of the review. The amount of research papers published nowadays related to ontology matching is remarkable and we believe that reflects the growing interest of the research community. However, for new practitioners that approach the field, this amount of information might seem overwhelming. Therefore, the purpose of this work is to help in guiding new practitioners get a general idea on the state of the field and to determine possible research lines.To do so, we first perform a literature review of the field in the last decade by means of an online search. The articles retrieved are sorted using a classification framework that we propose, and the different categories are revised and analyzed. The information in this review is extended and supported by the results obtained by a survey that we have designed and conducted among the practitioners.

http://disi.unitn.it/~p2p/RelatedWork/Matching/Cerdeira-Ontology%20Matching-2015.pdf

Ontology matching

Assessing the degree to which two objects, an object and a query, or two concepts are similar or compatible is a fundamental component of human reasoning and consequently is critical in the development of automated diagnosis, classification, information retrieval and decision systems. The assessment of similarity has played an important role in such diverse disciplines such as taxonomy, psychology, and the social sciences. Each discipline has proposed methods for quantifying similarity judgments suitable for its particular applications. This book presents a unified approach to quantifying similarity and compatibility within the framework of fuzzy set theory and examines the primary importance of these concepts in approximate reasoning. Examples of the application of similarity measures in various areas including expert systems, information retrieval, and intelligent database systems are provided.

Similarity and Compatibility in Fuzzy Set Theory: Assessment and Applications

Fuzzy objects for geographical information systems

/pdf/similarity-and-compatibility-in-fuzzy-set-theory-52i3j9ie39.pdf

Similarity and Compatibility in Fuzzy Set Theory

Over the past decade, information retrieval has emerged as an active research area in the application of fuzzy set theory. Fuzzy information retrieval utilizes fuzzy sets to represent documents, membership degrees for query term relevance, fuzzy logical operators to define queries, and fuzzy compatibility measures to assess the retrieval status value of a document. This paper presents an overview of fuzzy relational databases and fuzzy information retrieval. A general description of the main components of fuzzy information retrieval are given: document representation, query representation, computer-aided query formulation, document retrieval status, and performance measures. Examples of areas currently being researched are provided. The relation between fuzzy information retrieval and fuzzy relational databases is examined.

Fuzzy information retrieval

The Fuzzy Object Data Management Group has been formed as a joint international collaborative research effort among fuzzy database researchers in order to establish common terminology and concepts, to formalize and integrate the current body of fuzzy object model research, to provide a basis for future extensions, and to contribute to the commercial success of a fuzzy object data model. This paper presents the initial research efforts to use ODMG-93 object data model standard as the basis for defining a fuzzy object data model since it is becoming a defacto standard and several object-oriented database vendors are currently releasing commercial products in compliance with this standard. The syntactic extensions to the ODMG object model and the semantic issues related to these extensions in order to provide fuzzy set objects and fuzzy objects are presented. These extensions are an initial effort in accomplishing the challenging task of defining a fuzzy type and fuzzy hierarchy and their related semantics.

Valerie Cross

Papers

Similarity and Compatibility in Fuzzy Set Theory: Assessment and Applications

Fuzzy objects for geographical information systems

Similarity and Compatibility in Fuzzy Set Theory

Fuzzy information retrieval

A perspective from the Fuzzy Object Data Management Group (FODMG)