Linked Forests: Semantic similarity of geographical concepts “forest”

Čerba, Otakar; Jedlička, Karel

doi:10.1515/geo-2016-0049

Cited by 3 publications

(2 citation statements)

References 38 publications

(43 reference statements)

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“…Han and Li [37] quantified the relationships among the terms in a forestry thesaurus to compute the semantic similarity of forestry terms and built a semantic retrieval tool. Otakar and Karel [38] computed the similarity of the concept "forest" from seven different thesauri. Those approaches obtained nice results when applied to improve information retrieval processes.…”

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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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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“…The research will continue by comparing explicit semantics contained in particular resources (together with domain experts; principles are mentioned in [29,30]) or by testing metrics for whole network or edges. The achieved results could be improved by processing a larger number of concepts and objects.…”

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confidence: 99%

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Čerba

Jedlička

Čada

et al. 2017

IJGI

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Clear and straightforward communication is a key aspect of all human activities related to crisis management. Since crisis management activities involve professionals from various disciplines using different terminology, clear and straightforward communication is difficult to achieve. Semantics as a broad science can help to overcome communication difficulties. This research focuses on the evaluation of available semantic resources including ontologies, thesauri, and controlled vocabularies for disaster risk reduction as part of crisis management. The main idea of the study is that the most appropriate source of broadly understandable terminology is such a semantic resource, which is accepted by-or at least connected to the majority of other resources. Important is not only the number of interconnected resources, but also the concrete position of the resource in the complex network of Linked Data resources. Although this is usually done by user experience, objective methods of resource semantic centrality can be applied. This can be described by centrality methods used mainly in graph theory. This article describes the calculation of four types of centrality methods (Outdegree, Indegree, Closeness, and Betweenness) applied to 160 geographic concepts published as Linked Data and related to disaster risk reduction. Centralities were calculated for graph structures containing particular semantic resources as nodes and identity links as edges. The results show that (with some discussed exceptions) the datasets with high values of centrality serve as important information resources, but they also include more concepts from preselected 160 geographic concepts. Therefore, they could be considered as the most suitable resources of terminology to make communication in the domain easier. The main research goal is to automate the semantic resources evaluation and to apply a well-known theoretical method (centrality) to the semantic issues of Linked Data. It is necessary to mention the limits of this study: the number of tested concepts and the fact that centralities represents just one view on evaluation of semantic resources.

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Integrating social media data: Venues, groups and activities

Silva,

Fox

2024

Expert Systems with Applications

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Linked Forests: Semantic similarity of geographical concepts “forest”

Cited by 3 publications

References 38 publications

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

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

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Integrating social media data: Venues, groups and activities

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