A Hybrid Semantic Similarity Measure for Spatial Information Retrieval

Abstract

Semantic similarity is central to many cognitive processes and plays an important role in the way humans process and reason about information. In particular, the retrieval of knowledge from memory hinges crucially on similarity. Likewise, information retrieval systems use similarity to detect relevant information for a given query. Current information retrieval systems apply mainly syntactic techniques to determine similarity. Although such syntactic similarity measures have performed strongly with resources containing large amounts of text, they cannot appropriately cope with syntactic and semantic heterogeneity and ambiguity, if the semantics of the terms is not explicitly available. Therefore, they are rather rigid and inflexible as they cannot adapt to the user's requirements and conceptualization of the domain. Furthermore, geographic features are distinguished via their geometric and thematic data. It is often not possible to capture the complex semantics of geographic features by a single name or a textual description. Therefore spatial data is different from text documents typically found in enterprise databases or on the Web. Retrieval of spatial information requires new, intelligent retrieval mechanisms that satisfy its specific requirements. A semantics-based solution can more easily adapt to user needs and therefore increases the flexibility and usability of spatial data and retrieval methods. This paper investigates the suitability of various approaches -originally developed to explain human similarity judgement -in the context of spatial information retrieval. We propose a new, hybrid approach for semantic similarity measurement, which can represent the complex semantics of spatial data. It allows for retrieving relevant data by determining the similarity between the query and the semantic descriptions of geographic feature types within the database. The hybrid similarity measure combines the geometric structure of conceptual spaces with the relational structure of semantic nets to one, cognitively plausible knowledge representation with an inherent similarity measure.

An evaluative baseline for geo-semantic relatedness and similarity

“…Several theories of similarity have been used to conceptualise and measure geo-semantic similarity, including featural, transformational, geometric, and alignment models [47,22,23,48]. Specific techniques have been devised for specific knowledge-representation formalisms [20,44].…”

SIM‐NET: A View‐Based Semantic Similarity Model for Ad Hoc Networks of Geospatial Databases

“…This is not the case for concepts of different ontologies where a same real world phenomenon can be represented with different properties that are relevant to the application domain. In Schwering and Kuhn (2009), this model was extended to take into account relations between concepts. In this extended model, concepts can be defined with different dimensions; however, dimensions either match or mismatch, but there is no partial match.…”

“…In this extended model, concepts can be defined with different dimensions; however, dimensions either match or mismatch, but there is no partial match. In Schwering and Raubal (2005) and Schwering and Kuhn's (2009) models, properties are independent of each other; however, Raubal (2004) proposed that dependent properties may be modelled via non‐orthogonal dimensions, but this idea was not further formalized.…”

“…The geometric, feature and network models are all inspired from the human perception of similarity (Schwering and Kuhn 2009). At another level, we must also mention the logic‐based semantic similarity models, which define concepts with a logical language such as Description Logics (DL).…”

Semantic Similarity Measurement and Geospatial Applications

“…While feature‐based models (Tversky 1977) are most prominent, network‐based (Rada et al 1989), geometric (Torgerson 1965, Shepard 1987), alignment (Goldstone 1994), transformation‐based (Hahn et al 2003), and information theoretic (Resnik 1995) approaches have also evolved. Understanding human cognition forms the main motivation underlying these approaches, but recent research in information science has applied computational similarity theories as reasoning support for information retrieval and organization (Möller et al 1998, Maedche and Staab 2002, d’Amato et al 2005, Rissland 2006, Janowicz et al 2007, Schwering and Kuhn 2008). In the following, some core characteristics of semantic similarity are described.…”