An evaluative baseline for geo-semantic relatedness and similarity

Ballatore, Andrea; Bertolotto, Michela; Wilson, David C.

doi:10.1007/s10707-013-0197-8

Cited by 43 publications

(32 citation statements)

References 54 publications

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“…The indices of IRR and IRA in Table 5 indicate that the responses of experts possess a high reliability and are in agreement. The correlation is satisfactory and is better than analogous surveys [47].…”

Section: Survey Design and Resultsmentioning

confidence: 54%

Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach

Chen

Song

Yang

2018

IJGI

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Abstract:To help users discover the most relevant spatial datasets in the ever-growing global spatial data infrastructures (SDIs), a number of similarity measures of geospatial data based on metadata have been proposed. Researchers have assessed the similarity of geospatial data according to one or more characteristics of the geospatial data. They created different similarity algorithms for each of the selected characteristics and then combined these elementary similarities to the overall similarity of the geospatial data. The existing combination methods are mainly linear and may not be the most accurate. This paper reports our experiences in attempting to learn the optimal non-linear similarity integration functions, from the knowledge of experts, using an artificial neural network. First, a multiple-layer feed forward neural network (MLFFN) was created. Then, the intrinsic characteristics were used to represent the metadata of geospatial data and the similarity algorithms for each of the intrinsic characteristics were built. The training and evaluation data of MLFFN were derived from the knowledge of domain experts. Finally, the MLFFN was trained, evaluated, and compared with traditional linear combination methods, which was mainly a weighted sum. The results show that our method outperformed the existing methods in terms of precision. Moreover, we found that the combination of elementary similarities of experts to the overall similarity of geospatial data was not linear.

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Section: Survey Design and Resultsmentioning

confidence: 54%

Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach

Chen

Song

Yang

2018

IJGI

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“…There are many benchmark data sets used to evaluate similarity/relatedness algorithms, for example, that of Miller and Charles [48] [7] collected similarity judgements about geographical terms to evaluate their Matching-Distance Similarity Measure (MDSM). Ballatore et al [2] proposed GeReSiD as a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. Rodríguez and Egenhofer's similarity evaluation datasets and GeReSiD are gold standards for the evaluation of geographic term similarity or relatedness measures.…”

Section: Discussionmentioning

confidence: 99%

“…Semantic similarity is a subset of semantic relatedness: all similar terms are related, but related terms are not necessarily similar [2]. For example, "river" and "stream" are semantically similar, while "river" and "boat" are dissimilar but semantically related [3].…”

Section: Introductionmentioning

confidence: 99%

An Approach to Measuring Semantic Relatedness of Geographic Terminologies Using a Thesaurus and Lexical Database Sources

Chen

Song

Yang

2018

IJGI

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Abstract:In geographic information science, semantic relatedness is important for Geographic Information Retrieval (GIR), Linked Geospatial Data, geoparsing, and geo-semantics. But computing the semantic similarity/relatedness of geographic terminology is still an urgent issue to tackle. The thesaurus is a ubiquitous and sophisticated knowledge representation tool existing in various domains. In this article, we combined the generic lexical database (WordNet or HowNet) with the Thesaurus for Geographic Science and proposed a thesaurus-lexical relatedness measure (TLRM) to compute the semantic relatedness of geographic terminology. This measure quantified the relationship between terminologies, interlinked the discrete term trees by using the generic lexical database, and realized the semantic relatedness computation of any two terminologies in the thesaurus. The TLRM was evaluated on a new relatedness baseline, namely, the Geo-Terminology Relatedness Dataset (GTRD) which was built by us, and the TLRM obtained a relatively high cognitive plausibility. Finally, we applied the TLRM on a geospatial data sharing portal to support data retrieval. The application results of the 30 most frequently used queries of the portal demonstrated that using TLRM could improve the recall of geospatial data retrieval in most situations and rank the retrieval results by the matching scores between the query of users and the geospatial dataset.

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“…First, we aim to extend SSMWCL to the measurement of semantic relatedness. Semantic similarity has been regarded as a particular subset of the notion of semantic relatedness [46]. In SSMWCL, the network hierarchy of a weighted concept lattice is only applied to assign weights to the concepts according to the conceptual relative hierarchical depth in the lattice.…”

Section: Discussionmentioning

confidence: 99%

A Novel Approach to Semantic Similarity Measurement Based on a Weighted Concept Lattice: Exemplifying Geo-Information

Xiao

Zhu

2017

IJGI

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Abstract:The measurement of semantic similarity has been widely recognized as having a fundamental and key role in information science and information systems. Although various models have been proposed to measure semantic similarity, these models are not able effectively to quantify the weights of relevant factors that impact on the judgement of semantic similarity, such as the attributes of concepts, application context, and concept hierarchy. In this paper, we propose a novel approach that comprehensively considers the effects of various factors on semantic similarity judgment, which we name semantic similarity measurement based on a weighted concept lattice (SSMWCL). A feature model and network model are integrated together in SSMWCL. Based on the feature model, the combined weight of each attribute of the concepts is calculated by merging its information entropy and inclusion-degree importance in a specific application context. By establishing the weighted concept lattice, the relative hierarchical depths of concepts for comparison are computed according to the principle of the network model. The integration of feature model and network model enables SSMWCL to take account of differences in concepts more comprehensively in semantic similarity measurement. Additionally, a workflow of SSMWCL is designed to demonstrate these procedures and a case study of geo-information is conducted to assess the approach.

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An evaluative baseline for geo-semantic relatedness and similarity

Cited by 43 publications

References 54 publications

Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach

Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach

An Approach to Measuring Semantic Relatedness of Geographic Terminologies Using a Thesaurus and Lexical Database Sources

A Novel Approach to Semantic Similarity Measurement Based on a Weighted Concept Lattice: Exemplifying Geo-Information

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