Algorithm, Implementation and Application of the SIM-DL Similarity Server

Janowicz, Krzysztof; Keßler, Carsten; Schwarz, Mirco; Wilkes, Marc; Panov, Ilija; Espeter, Martin; Bäumer, Boris

doi:10.1007/978-3-540-76876-0_9

Cited by 47 publications

(60 citation statements)

References 16 publications

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“…Research in semantic similarity has produced a wide variety of approaches, classifiable as knowledgebased (structural similarity is computed in expert-authored ontologies), corpusbased (similarity is extracted from statistical patterns in large text corpora), or hybrid (combining knowledge and corpus-based approaches) [20,23]. In the area of Geographic Information Science (GIScience), similarity techniques have been tailored on specific formalisms [25,24,12].…”

Section: Introductionmentioning

confidence: 99%

The Similarity Jury: Combining Expert Judgements on Geographic Concepts

Ballatore

Wilson²,

Bertolotto

2012

Lecture Notes in Computer Science

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Abstract. A cognitively plausible measure of semantic similarity between geographic concepts is valuable across several areas, including geographic information retrieval, data mining, and ontology alignment. Semantic similarity measures are not intrinsically right or wrong, but obtain a certain degree of cognitive plausibility in the context of a given application. A similarity measure can therefore be seen as a domain expert summoned to judge the similarity of a pair of concepts according to her subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we first define the similarity jury as a panel of experts having to reach a decision on the semantic similarity of a set of geographic concepts. Second, we have conducted an evaluation of 8 WordNet-based semantic similarity measures on a subset of OpenStreetMap geographic concepts. This empirical evidence indicates that a jury tends to perform better than individual experts, but the best expert often outperforms the jury. In some cases, the jury obtains higher cognitive plausibility than its best expert.

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Section: Introductionmentioning

confidence: 99%

The Similarity Jury: Combining Expert Judgements on Geographic Concepts

Ballatore

Wilson²,

Bertolotto

2012

Lecture Notes in Computer Science

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“…Due to their analogy to spatial proximity functions, semantic similarity measures have been widely studied and applied in GIScience [20,21,14,22,8,23]. Most of these measures are hybrid in a sense that they combine different approaches to similarity, such as features, regions in a multi-dimensional space, or network distances.…”

Section: Semantic Similaritymentioning

confidence: 99%

“…For our case study, the bounding box for the London dataset is set to (51.4158,-0.331), (51.6011,0.0796). Data was retrieved from OSM's extended API (see requests 8 ).…”

Section: Amenity Points Of Interest In Openstreetmapmentioning

confidence: 99%

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Analyzing the Spatial-Semantic Interaction of Points of Interest in Volunteered Geographic Information

Mülligann

Janowicz

et al. 2011

Spatial Information Theory

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Abstract. With the increasing success and commercial integration of Volunteered Geographic Information (VGI), the focus shifts away from coverage to data quality and homogeneity. Within the last years, several studies have been published analyzing the positional accuracy of features, completeness of specific attributes, or the topological consistency of line and polygon features. However, most of these studies do not take geographic feature types into account. This is for two reasons. First, and in contrast to street networks, choosing a reference set is difficult. Second, we lack the measures to quantify the degree of feature type miscategorization. In this work, we present a methodology to analyze the spatial-semantic interaction of point features in Volunteered Geographic Information. Feature types in VGI can be considered special in both, the way they are formed and the way they are applied. Given that they reflect community agreement more accurately than top-down approaches, we argue that they should be used as the primary basis for assessing spatial-semantic interaction. We present a case study on a spatial and semantic subset of OpenStreetMap, and introduce a novel semantic similarity measure based on the change history of OpenStreetMap elements. Our results set the stage for systems that assist VGI contributors in suggesting the types of new features, cleaning up existing data, and integrating data from different sources.

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“…Previous approaches to enable context-aware similarity measurement [45,46] regarded context as a subset of the static information at hand to enhance the cognitive plausibility of the results [47]. Combinations of semantic rules and similarity measurement show promise for adaptation of information retrieval results to the current context that goes beyond previous approaches.…”

mentioning

confidence: 99%

Semantic Rules for Context-Aware Geographical Information Retrieval

Keßler

Raubal

Wosniok

2009

Lecture Notes in Computer Science

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Abstract. Geographical information retrieval (GIR) can benefit from context information to adapt the results to a user's current situation and personal preferences. In this respect, semantics-based GIR is especially challenging because context information -such as collected from sensors -is often provided through numeric values, which need to be mapped to ontological representations based on nominal symbols. The Web Ontology Language (OWL) lacks mathematical processing capabilities that require free variables, so that even basic comparisons and distance calculations are not possible. Therefore, the context information cannot be interpreted with respect to the task and the current user's preferences. In this paper, we introduce an approach based on semantic rules that adds these processing capabilities to OWL ontologies. The task of recommending personalized surf spots based on user location and preferences serves as a case study to evaluate the capabilities of semantic rules for context-aware geographical information retrieval. We demonstrate how the Semantic Web Rule Language (SWRL) can be utilized to model user preferences and how execution of the rules successfully retrieves surf spots that match these preferences. While SWRL itself enables free variables, mathematical functions are added via built-ins -external libraries that are dynamically loaded during rule execution. Utilizing the same mechanism, we demonstrate how SWRL built-ins can query the Semantic Sensor Web to enable the consideration of real-time measurements and thus make geographical information retrieval truly context-aware.

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Algorithm, Implementation and Application of the SIM-DL Similarity Server

Cited by 47 publications

References 16 publications

The Similarity Jury: Combining Expert Judgements on Geographic Concepts

The Similarity Jury: Combining Expert Judgements on Geographic Concepts

Analyzing the Spatial-Semantic Interaction of Points of Interest in Volunteered Geographic Information

Semantic Rules for Context-Aware Geographical Information Retrieval

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