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

https://www.alaakhamis.org/teaching/MCT200/reading/Information%20Fusion.pdf

Multisensor data fusion: A review of the state-of-the-art

Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. Often in high dimensional data, many dimensions are irrelevant and can mask existing clusters in noisy data. Feature selection removes irrelevant and redundant dimensions by analyzing the entire dataset. Subspace clustering algorithms localize the search for relevant dimensions allowing them to find clusters that exist in multiple, possibly overlapping subspaces. There are two major branches of subspace clustering based on their search strategy. Top-down algorithms find an initial clustering in the full set of dimensions and evaluate the subspaces of each cluster, iteratively improving the results. Bottom-up approaches find dense regions in low dimensional spaces and combine them to form clusters. This paper presents a survey of the various subspace clustering algorithms along with a hierarchy organizing the algorithms by their defining characteristics. We then compare the two main approaches to subspace clustering using empirical scalability and accuracy tests and discuss some potential applications where subspace clustering could be particularly useful.

/pdf/subspace-clustering-for-high-dimensional-data-a-review-56727kn4va.pdf

Subspace clustering for high dimensional data: a review

Schema and ontology matching is a critical problem in many application domains, such as semantic web, schema/ontology integration, data warehouses, e-commerce, etc. Many different matching solutions have been proposed so far. In this paper we present a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching. Some innovations are in introducing new criteria which are based on (i) general properties of matching techniques, (ii) interpretation of input information, and (iii) the kind of input information. In particular, we distinguish between approximate and exact techniques at schema-level; and syntactic, semantic, and external techniques at element- and structure-level. Based on the classification proposed we overview some of the recent schema/ontology matching systems pointing which part of the solution space they cover. The proposed classification provides a common conceptual basis, and, hence, can be used for comparing different existing schema/ontology matching techniques and systems as well as for designing new ones, taking advantages of state of the art solutions.

/pdf/a-survey-of-schema-based-matching-approaches-1iseu9xjqp.pdf

A survey of schema-based matching approaches

技術解説 IEEE Computer

We review the use on ontologies for the integration of heterogeneous information sources. Based on an in-depth evaluation of existing approaches to this problem we discuss how ontologies are used to support the integration task. We evaluate and compare the languages used to represent the ontologies and the use of mappings between ontologies as well as to connect ontologies with information sources. We also ask for ontology engineering methods and tools used to develop ontologies for information integration. Based on the results of our analysis we summarize the state of the art in ontology-based information integration and name areas of further research activities.

/pdf/ontology-based-integration-of-information-a-survey-of-4d6okm0dof.pdf

Ontology-Based Integration of Information — A Survey of Existing Approaches

Ontologies for geographic information processing

Among the most fundamental requirements for Semantic Web applications is their ability to access and to interpret information sources that are geographically distributed, as well as highly heterogeneous with respect to syntax, structure and semantics (Visser and Klein 2002). In order to be able to decide whether an information source is relevant to a specific information request and to resolve semantic heterogeneities during the subsequent integration of this information source, Semantic Web applications must have access to metadata that describe the semantics of an information source. The Bremen University Semantic Translator for Enhanced Retrieval (BUSTER) is an information broker middleware that was built to tackle the challenges of the Semantic Web. BUSTER is able to access heterogeneous and distributed information sources and to assess their conceptual, spatial, and temporal relevance with respect to a specific information request. BUSTER can also be used to integrate heterogeneous information through the resolution of structural, syntactical, and semantic heterogeneities. In this article, we focus on the application of BUSTER as a tool for intelligent search and information retrieval in the Semantic Web. The BUSTER search module supports the specification of queries of the type concept @ location in time. In addition to the conceptual semantics, the system evaluates the spatial as well as the temporal relevance of an information source. A typical example for an information request would be: “Find a conference-hotel near the Thüringer Wald for the period between Christmas and New Year 2003”. In order to be able to reason about conceptual (“Which concepts are subsumed by the concept specified in the query?”), spatial (“Which regions are in or near the specified geographic region?”), and temporal relevance (“Which dates fall within the given time period?”), BUSTER utilizes metadata that provide formal descriptions of the respective context of an information source. While well-established methods to formalize and to reason about conceptual semantics could be used, the inclusion of spatial and temporal aspects demanded the development of new representation schemes and reasoning mechanisms. Given the distributed structure of the Semantic Web and its implicit open world assumption, a major challenge was to provide a framework for the interoperability and integration of formal conceptual, spatial, and temporal representations. In the following, we will give a brief introduction to the methods and solutions we developed to meet these challenges. BUSTER – An Information Broker for the Semantic Web

BUSTER - An Information Broker for the Semantic Web.

Center for Computing TechnologiesUniversity of Bremen, GermanyAbstract. We present an approach for developing shared ontologieswhich can be used to deﬁne terms from diﬀerent vocabularies and toautomatically translate them from one vocabulary into another. We ﬁrstmotivate the use of shared ontologies as a means for identifying semanticcorrespondences between terms and underline the need for a shared on-tology. Then, the general steps of a specialized methodology for buildingsuch shared ontologies are described. We further illustrate the applica-tion of the methodology using a real-life example from the domain ofgeo-informatics.

/pdf/building-shared-ontologies-for-terminology-integration-13vc99xgjk.pdf

Gerhard Schuster

Papers

Ontology-Based Integration of Information — A Survey of Existing Approaches

Ontologies for geographic information processing

BUSTER - An Information Broker for the Semantic Web.

Building Shared Ontologies for Terminology Integration