Semantic relatedness algorithm for keyword sets of geographic metadata

ABSTRACT

Advances in linked geospatial data, recommender systems, and geographic information retrieval have led to urgent necessity to assess the overall semantic relatedness between keyword sets of geographic metadata. In this study, a new model is proposed for computing the semantic relatedness between arbitrary two keyword sets of geographic metadata stored in current global spatial data infrastructures. In this model, the overall semantic relatedness is derived by pairing these keywords that are found to be most relevant to each other and averaging their relatedness. To find the most relevant keywords across two keyword sets precisely, the keywords in the keyword set of geographic metadata are divided into three kinds: the thesaurus elements, the WordNet elements, and the statistical elements. The thesaurus-lexical relatedness measure (TLRM), the extended thesaurus-lexical relatedness measure (ETLRM), and the Longest Common Substring method are proposed to compute the semantic relatedness between two thesaurus elements, two WordNet elements, a thesaurus element, and a WordNet element and two statistical elements, respectively. A human data set – the geographic-metadata’s keyword set relatedness dataset, which was used to evaluate the precision of the semantic relatedness measures of keyword sets of geographic metadata, was created. The proposed method was evaluated against the human-generated relatedness judgments and was compared with the Jaccard method and Vector Space Model. The results demonstrated that the proposed method achieved a high correlation with human judgments and outperformed the existing methods. Finally, the proposed method was applied to quantitatively linked geospatial data.

KEYWORDS:

• Geographic metadata
• keyword sets
• semantic relatedness
• knowledge-based method
• linked geospatial data

Acknowledgments

We thank the editors and the anonymous reviewers for their very helpful suggestions, all of which have improved the paper. We are also grateful to the 46 experts who offered the ratings regarding the GKSRD.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the the National Earth System Science Data Sharing Infrastructure [2005DKA32300]; National Natural Science Foundation of China [41631177]; Construction Project of Ecological Risk Assessment and Basic Geographic Information Data-base of International Economic Corridor Across China, Mongolia and Russia [131A11KYSB20160091]; Multidisciplinary Joint Expedition For China-Mongolia-Russia Economic Corridor [2017FY101300]; Branch Center Project of Geography, Resources and Ecology of Knowledge Center for Chi-nese Engineering Sciences and Technology [CKCEST-2017-1-8].