Abstract. Similarity measurement theories play an increasing role in GIScience and especially in information retrieval and integration. Existing feature and geometric models have proven useful in detecting close but not identical concepts and entities. However, until now none of these theories are able to handle the expressivity of description logics for various reasons and therefore are not applicable to the kind of ontologies usually developed for geographic information systems or the upcoming geospatial semantic web. To close the resulting gap between available similarity theories on the one side and existing ontologies on the other, this paper presents ongoing work to develop a context-aware similarity theory for concepts specified in expressive description logics such as ALCNR. 1

Abstract. Semantic similarity measurement gained attention as a methodology for ontology-based information retrieval within GIScience over the last years. Several theories explain how to determine the similarity between entities, concepts or spatial scenes, while concrete implementations and applications are still missing. In addition, most existing similarity theories use their own representation language while the majority of geoontologies is annotated using the Web Ontology Language (OWL). This paper presents a context and blocking aware semantic similarity theory for the description logic ALCHQ as well as its prototypical implementation within the open source SIM-DL similarity server. An application scenario is introduced showing how the Alexandria Digital Library Gazetteer can benefit from similarity in terms of improved search and annotation capabilities. Directions for further work are discussed. 1

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

Abstract. Context plays a crucial role when measuring the similarity of two concepts. Nonetheless, the modelling of context has been mostly neglected in existing similarity measurement theories. In this paper, we explore the influence of context in existing similarity measurement approaches for the geospatial domain, focussing on whether and how these approaches account for it. Based on these observations, the processing of context during similarity measurement is analysed, and general implementation issues, especially ease of integration into existing reasoning systems and computability, are discussed. The results of the different analyses are then combined into a generic set of characteristics of context for similarity measurement, with regard to the geospatial domain.

Abstract. The paper proposes a framework to assess the semantic similarity among instances within an ontology. It aims to define a sensitive measurement of semantic similarity, which takes into account different hints hidden in the ontology definition and explicitly considers the application context. The similarity measurement is computed by combining and extending existing similarity measures and tailoring them according to the criteria induced by the context. Experiments and evaluation of the similarity assessment are provided. 1

All aspects of human activity are rooted in geographic space in some respect. As a consequence, many web resources include references to geographic context. In order to assist in recognising spatial terms employed in a query, it is proposed to use a geographical ontology. A geo-ontology play a key role in the development of spatially-aware search engine, with regards to providing support for query disambiguation, query expansion, relevance ranking and web resource annotation. This paper describes the geo-ontology designed for the SPIRIT system, before focussing on the problem of integrating multiple datasets for constructing such an ontology. Similarity checking of datasets is an essential step in the process of integration. The validity and effect of the different measures are studied by building a prototype geo-ontology utilising different datasets. The experimental results obtained confirmed the effect of quality of the datasets and the importance of the flexibility of the technique proposed for adjusting the similarity measures to handle such an effect.

Gazetteers are more than basic place name directories containing names and locations for named geographic places. Most of them contain additional information, including a categorization of gazetteer entries using a typing scheme. This paper focuses on the nature of these categorization schemes. We argue that gazetteers can benefit from an ontological approach to typing schemes, providing a formalization that will better support gazetteer applications, maintenance, interoperability, and semi-automatic feature annotation. We discuss the process of developing such an ontology as a modification of an existing feature type thesaurus; the difficulties in mapping from thesauri to ontologies are described in detail. To demonstrate the benefits of a categorization based on ontologies, a new gazetteer Web (and programming) interface is introduced and the impact on gazetteer interoperability is discussed. 1

Abstract—Semantic similarity measurement gained attention over the last years as a non-standard inference service for various kinds of knowledge representations including description logics. Most existing similarity measures compute an undirected overall similarity, i.e., they do not take the context of the similarity query into account. If they do, the notion of context is usually reduced to the selection of particular concepts for comparison (instead of comparing all concepts within an examined ontology). The importance of context in deriving meaningful similarity judgments is beyond question and has been examined within recent research. This paper argues that there are several kinds of contexts. Each of them has its own impact on the resulting similarity values, but also on their interpretation. To support this view, the paper introduces definitions for the examined contexts and illustrates their influence by example. I.

Interoperability is a crucial problem for geographic information systems. The transfer of data and models between different systems requires the ability to set up a correspondence between concepts in one system to concepts in the other. Concept matching is helped by ontologies. However, the challenge of making ontologies themselves interoperable continues. In other words, given two geo-ontologies, the basic question is: to which degree are these two geo-ontologies interoperable? In this paper, we consider that a geo-ontology describes things that can be assigned to locations on the surface of the Earth and relations between these things. A geo-ontology has concepts that correspond to physical and social phenomena in the real world. We suggest a classification of these concepts based on their use for describing geo-objects. We present a basic set of concepts for a geographical ontology, based on descriptions of the physical world and of the social reality. We also present a framework for measuring the degree of interoperability between geo-ontologies. We consider that this problem is a special case of Bernstein’s model management algebra for metadata descriptions. We propose to use a matching operator for measuring

Abstract. Acquisition and semantic annotation of data are fundamental tasks within the domain of cultural heritage. With the increasing amount of available data and ad hoc cross linking between their providers and users (e.g. through web services), data integration and knowledge refinement becomes even more important. To integrate information from several sources it has to be guaranteed that objects of discourse (which may be artifacts, events, persons, places or periods) refer to the same real world phenomena within all involved data sources. Local (database) identifiers however only disambiguate internal data, but fail in establishing connections to/between external data, while global identifiers can only partially solve this problem. Software assistants should support users in establishing such connections by delivering identity assumptions, i.e. by estimating whether examined data actually concerns the same real word phenomenon. This paper points out how similarity measures can act as groundwork for such assistants by introducing a similarity-based identity assumption assistant for historical places to support scholars in establishing links between distributed historical knowledge. 1