Abstract. Determining the grade of semantic similarity between geospatial concepts is the basis for evaluating semantic interoperability of geographic information services and their users. Geometrical models, such as conceptual spaces, offer one way of representing geospatial concepts, which are modelled as n-dimensional regions. Previous approaches have suggested to measure semantic similarity between concepts based on their approximation by single points. This paper presents a way to measure semantic similarity between conceptual regions—leading to more accurate results. In addition, it allows for asymmetries by measuring directed similarities. Examples from the geospatial domain illustrate the similarity measure and demonstrate its plausibility. 1

Integration of geographic information has increased in importance because of new possibilities arising from the interconnected world and the increasing availability of geographic information. Ontologies support the creation of conceptual models and help with information integration. In this paper, we propose a way to link the formal representation of semantics (i.e., ontologies) to conceptual schemas describing information stored in databases. The main result is a formal framework that explains a mapping between a spatial ontology and a geographic conceptual schema. The mapping of ontologies to conceptual schemas is made using three different levels of abstraction: formal, domain, and application levels. At the formal level, highly abstract concepts are used to express the schema and the ontologies. At the domain level, the schema is regarded as an instance of a generic data model. At the application level, we focus on the particular case of geographic applications. We also discuss the in¯uence of ontologies in both the traditional and geographic systems development methodologies, with an emphasis on the conceptual design phase. Keywords: systems ontologies, geographic conceptual models, geographic data modeling, geographic information 1.

As more information becomes available on the Web, there has been a crescent interest in effective personalization techniques. Personal agents providing assistance based on the content of Web documents and the user interests emerged as a viable alternative to this problem. Provided that these agents rely on having knowledge about users contained into user profiles, i.e., models of user preferences and interests gathered by observation of user behavior, the capacity of acquiring and modeling user interest categories has become a critical component in personal agent design. User profiles have to summarize categories corresponding to diverse user information interests at different levels of abstraction in order to allow agents to decide on the relevance of new pieces of information. In accomplishing this goal, document clustering offers the advantage that an a priori knowledge of categories is not needed, therefore the categorization is completely unsupervised. In this paper we present a document clustering algorithm, named WebDCC (Web Document Conceptual Clustering), that carries out incremental, unsupervised concept learning over Web documents in order to acquire user profiles. Unlike most user profiling approaches, this algorithm offers comprehensible clustering solutions that can be easily interpreted and explored by both users and other agents. By extracting semantics from Web pages, this algorithm also produces intermediate results that can be finally integrated in a machine-understandable format such as an ontology. Empirical results of using this algorithm in the context of an intelligent Web search agent proved it can reach high levels of accuracy in suggesting Web pages.

Information integration has been an important area of research for many years, and the problem of integration of geographic data has recently emerged. This paper presents an approach based on the use of Ontologies for solving the problem of semantic heterogeneity in the process of the construction of a Federated Schema in the framework of geographic data. We make use of standard technologies (abstract model and GML from OpenGIS, XMI based XML, SDTS from USGS). The principal ontology for the matching process is derived from Spatial Data Transfer Standard and WordNet. To obtain similarities and differences between entities from Export Schema, this work makes use of a semantic similarity model. The notion of context is also an important issue for the evaluation of semantic similarity. The main goal achieved in this work is the use of a Federated Architecture for Spatial Databases in conjunction with the assessment of semantic similarity across ontologies.

This work presents an approach in which user pro les, represented by ontologies that were learned by an interface agent, are compared to foster collaboration for information retrieval from the web. It is shown how the interface agent represents the knowledge about the user along with the pro les that were empirically developped. Departing from a speci c matching model, briey presented here, quantitative results were achieved by comparing such particular ontologies in a fully automatic way. The results are presented and their implications are discussed.

Information integration has been an important area of research for many years, and the problem of integration of geographic data has recently emerged. This paper presents an approach based on the use of Ontologies for solving the problem of semantic heterogeneity in the process of the construction of a Federated Schema in the framework of geographic data. We make use of a standard technology (OMT-G based UML, XMI based XML, GML from OpenGIS) 1