A Structural-Lexical Measure of Semantic Similarity for Geo-Knowledge Graphs

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

doi:10.3390/ijgi4020471

Cited by 22 publications

(14 citation statements)

References 33 publications

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“…for geographic entities from their comments to construct semantic vectors. A matrix was constructed, each cell of which contained a similarity between two terms from the vectors [53]. The similarity scores between term pairs were computed with WordNet.…”

Section: Lexical Similarity Metricsmentioning

confidence: 99%

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Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Sun

Zhu

Song

2019

IJGI

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Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges.

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Section: Lexical Similarity Metricsmentioning

confidence: 99%

“…These algorithms can obtain excellent results when there are dense links among entities in GKBs. Ballatore et al applied the co-citation algorithm to compute the structural similarity of entities in OpenStreetMap [53,65].…”

Section: Structural Similarity Metricsmentioning

confidence: 99%

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Sun

Zhu

Song

2019

IJGI

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“…al. [18] have proposed a strategy for computing semantic similarity in Geo-Knowledge Graphs like Semantic Wikis, gazetteers, folksonomies, etc. In this approach, the Knowledge Authoring is done in a structured manner where the Ontological Concepts are modeled as vertices in the Geo-Knowledge Graphs and the Edges are linked to the relationships between the ontological entities.…”

Section: Related Workmentioning

confidence: 99%

Onto Collab: Strategic review oriented collaborative knowledge modeling using ontologies

Deepak

et al. 2015

2015 Seventh International Conference on Advanced Computing (ICoAC)

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Abstract-Modeling efficient knowledge bases for improving the semantic property of the World Wide Web is mandatory for promoting innovations and developments in World Wide Web. There is a need for efficient and organized modeling of the knowledge bases. In this paper, a strategy Onto Collab is proposed for construction of knowledge bases using ontology modeling. Ontologies are visualized as the basic building blocks of the knowledge in the web. The cognitive bridge between the human conceptual understanding of real world data and the processable data by computing systems is represented by Ontologies. A domain is visualized as a collection of similar ontologies. A review based strategy is proposed over a secure messaging system to author ontologies and a platform for retracing the domain ontologies as individuals and as a team is proposed. Evaluations for ontologies constructed pertaining to a domain for non-wiki knowledge bases is carried out.

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“…In 2013, Ballatore et al [3] proposed a lexical definition semantic similarity approach using paraphrase-detection techniques and the lexical database WordNet based on volunteered lexical definitions which were extracted from the OSM Semantic Network. More recently, Ballatore et al [10] proposed a hybrid semantic similarity measure, the network-lexical similarity measure (NLS). The main limitation of these methods lies in the lack of a precise context for the computation of the similarity measure [10], because the crowdsourcing geo-knowledge graph of the OpenStreetMap Semantic Network is not of high quality intrinsically in terms of knowledge representation and has limitations in coverage.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Ballatore et al [10] proposed a hybrid semantic similarity measure, the network-lexical similarity measure (NLS). The main limitation of these methods lies in the lack of a precise context for the computation of the similarity measure [10], because the crowdsourcing geo-knowledge graph of the OpenStreetMap Semantic Network is not of high quality intrinsically in terms of knowledge representation and has limitations in coverage.…”

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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A Structural-Lexical Measure of Semantic Similarity for Geo-Knowledge Graphs

Cited by 22 publications

References 33 publications

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Onto Collab: Strategic review oriented collaborative knowledge modeling using ontologies

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

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