Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontologies. This paper provides a brief survey of the approaches to semantic integration developed by researchers in the ontology community. We focus on the approaches that differentiate the ontology research from other related areas. The goal of the paper is to provide a reader who may not be very familiar with ontology research with introduction to major themes in this research and with pointers to different research projects. We discuss techniques for finding correspondences between ontologies, declarative ways of representing these correspondences, and use of these correspondences in various semantic-integration tasks 1. ONTOLOGIES AND SEMANTIC INTE-

As ontology development becomes a more ubiquitous and collaborative process, the developers face the problem of maintaining versions of ontologies akin to maintaining versions of software code in large software projects. Versioning systems for software code provide mechanisms for tracking versions, checking out versions for editing, comparing different versions, and so on. We can directly reuse many of these mechanisms for ontology versioning. However, version comparison for code is based on comparing text files---an approach that does not work for comparing ontologies. Two ontologies can be identical but have different text representation. We have

Abstract. Ontologies are today a key part of every knowledge based system. They provide a source of shared and precisely defined terms, resulting in system interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that a domain can be conceptualized results in the creation of different ontologies with contradicting or overlapping parts. For this reason ontologies need to be brought into mutual agreement (aligned). One important method for ontology alignment is the comparison of class and property names of ontologies using stringdistance metrics. Today quite a lot of such metrics exist in literature. But all of them have been initially developed for different applications and fields, resulting in poor performance when applied in this new domain. In the current paper we present a new string metric for the comparison of names which performs better on the process of ontology alignment as well as to many other field matching problems. 1

Integrating heterogeneous resources of the web will require finding agreement between the underlying ontologies. A variety of methods from the literature may be used for this task, basically they perform pair-wise comparison of entities from each of the ontologies and select the most similar pairs. We introduce a similarity measure that takes advantage of most of the features of OWL-Lite ontologies and integrates many ontology comparison techniques in a common framework. Moreover, we put forth a computation technique to deal with one-to-many relations and circularities in the similarity definitions.

This paper describes how we used generic schema matching algorithms to align the Foundational Model of Anatomy (FMA) and the GALEN Common Reference Model (CRM), two large models of human anatomy. We summarize the generic schema matching algorithms we used to identify correspondences. We present sample results that highlight the similarities and differences between the FMA and the CRM. We also identify uses of aggregation, transitivity, and reification, for which generic schema matching fails to produce an accurate mapping and present manually constructed solutions for them.

Model management offers a higher level interface than current techniques for metadata management, and generic operators drastically reduce amount of programming for metadata applications. The interactive nature of generic model management operators inevitably demands an intuitive representation. This paper proposes a visual representation for model management operators based on graph transformation. Graph transformation formalisms, as the theoretic foundation of many visual programming languages, can formally represent model management operators by visual and intuitive expressions. By using graphical representations users can easily comprehend and manipulate the operators and desired outputs.

Abstract. This paper summarizes the results of PRIOR system, which is an ontology mapping system based on Profile pRopagation and InfOrmation Retrieval techniques, for OAEI 2006 campaign. The PRIOR system exploits both linguistic and structural information to map small ontologies, and integrates Indri search engine to process large ontologies. The preliminary results of the experiments for four tasks (i.e. benchmark, web directories, anatomy and food) are presented. A discussion of the results and future work are given at the end. 1 Presentation of the system 1.1 State, purpose, general statement The World Wide Web (WWW) makes a large number of digital resources publicly accessible. However, finding relevant information, i.e. searching for digital resources from various sources and manually organizing them for relevance, becomes more and more intractable. Semantic interoperability research is aimed at enabling different information systems to communicate information consistently with the intended

Abstract. An ontology is a formal representation of a domain modeling the entities in the domain and their relations. When a domain is represented by multiple ontologies, there is need for creating mappings among these ontologies in order to facilitate the integration of data annotated with and reasoning across these ontologies. The objective of this paper is to present our experience in aligning two large anatomical ontologies and to reflect on some of the issues and challenges encountered along the way. The anatomical ontologies under investigation are the Foundational Model of Anatomy (FMA) and GALEN. Our approach to aligning concepts is automatic, rule-based, and operates at the schema level, generating mostly point-to-point mappings. It uses a combination of domain-specific lexical techniques and structural and semantic techniques (to validate the mappings suggested lexically). It also takes advantage of domainspecific knowledge (lexical knowledge from external resources such as the Unified Medical Language System, as well as knowledge augmentation and inference techniques). Overall, the lexical alignment followed by structural validation identified 3,029 pairs of equivalent concepts in the FMA and GALEN, accounting for about 4 % of all FMA concepts and 32 % of all GALEN concepts. 1 Presentation of the system

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e. using instance information of the ontology to enrich the original ontologies and calculate similarities between elements in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported.

With the advance of E-Commerce over Web-based information, the interoperability of isolated XML repositories and databases over the Internet has drawn an increasing interest recently. Little effort, however, has been made to preserve necessary autonomy and security of each individual XML repository or database during information exchange or evolution. Generic model management has been intensively researched and also implemented in a prototype since its first introduction. Security related research is yet to be conducted for model management. This paper presents a uniform security model for access control specifications of heterogeneous data models over the Web. Based on the uniform representation, we present security extensions to our previous work on visual model management operators for managing access control specifications to allow heterogeneous Web data models to exchange information over public networks. 1.