Abstract
Similarity measures have a long tradition in fields such as information retrieval, artificial intelligence, and cognitive science. Within the last years, these measures have been extended and reused to measure semantic similarity; i.e., for comparing meanings rather than syntactic differences. Various measures for spatial applications have been developed, but a solid foundation for answering what they measure; how they are best applied in information retrieval; which role contextual information plays; and how similarity values or rankings should be interpreted is still missing. It is therefore difficult to decide which measure should be used for a particular application or to compare results from different similarity theories. Based on a review of existing similarity measures, we introduce a framework to specify the semantics of similarity. We discuss similarity-based information retrieval paradigms as well as their implementation in web-based user interfaces for geographic information retrieval to demonstrate the applicability of the framework. Finally, we formulate open challenges for similarity research.
Highlights
Introduction and motivationSimilarity measures belong to the classical approaches to information retrieval and have been successfully applied for many years, increasingly in the domain of spatial information [82]
Symmetric similarity measures can be defined without an explicit search and target concept, though this is difficult to argue from a cognitive point of view as direction is implicitly contained in many retrieval tasks
In this article we introduced a generic framework for semantic similarity measurement
Summary
Similarity measures belong to the classical approaches to information retrieval and have been successfully applied for many years, increasingly in the domain of spatial information [82]. While they have been working previously in the background of search engines, similarity measures are nowadays becoming more visible and are integrated into user interfaces of modern search engines. Modern similarity measures are neither restricted to purely structural approaches nor to simple network measures within a subsumption hierarchy They compute the conceptual overlap between arbitrary concepts and relations, and, narrow the gap between similarity and analogy.
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