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

Chen, Zugang; Song, Jia; Yang, Yibo

doi:10.3390/ijgi7030098

Cited by 14 publications

(3 citation statements)

References 47 publications

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“…At present, there are three main methods of word vector training: based on a corpus [34], text similarity [35] and contextual analysis [36]. Although it is simple and easy to use the knowledge base to mine synonyms, the knowledge base coverage is limited, and it is limited for each domain that needs its own knowledge base.…”

Section: Word Similarity Calculationmentioning

confidence: 99%

Geographic Knowledge Graph Attribute Normalization: Improving the Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence Analysis

Yin

Zhang

Liu

et al. 2022

IJGI

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Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of “landscape, forest, field, lake and grass”. The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modification results is 0.934; the results show that the accuracy is greater than 90%. Compared to traditional methods only focusing on similarity, the accuracy of synonymous attribute recognition improves the system and we are capable of identifying near-sense attributes. Integration of our system and attribute normalization can greatly improve both the processing efficiency and accuracy.

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Section: Word Similarity Calculationmentioning

confidence: 99%

Geographic Knowledge Graph Attribute Normalization: Improving the Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence Analysis

Yin

Zhang

Liu

et al. 2022

IJGI

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“…Context-based metrics calculate category similarity by means of background knowledge. For example, the similarity between geographic terminologies of thesaurus can be measured with the aid of a lexical database, such as WordNet [82].…”

Section: Category Similarity Metricsmentioning

confidence: 99%

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Sun

Zhu

Song

2019

IJGI

Self Cite

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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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“…As a consequence, significant differences existed in the technical process, data accuracy, and classification system. Furthermore, spatial overlap and semantic conflict arose among technical data, an occurrence that often leads to right disputes in practice [33][34][35]. From the perspective of unifying the administration of natural resource assets and territory space planning, three aspects of technical unification must be attained, namely, unification of the registration book, technical standard, and information platform.…”

Section: Technical Aspectmentioning

confidence: 99%

LADM-Based Model for Natural Resource Administration in China

Zhang

Zhuo

Long

et al. 2019

IJGI

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China’s rapid urbanization and industrialization have continually placed massive pressure on the country’s natural resources. The fragmented departmental administration of natural resources also intensifies the problem of sustainable use. Accordingly, China’s central government has launched natural resource administration reform from decentralization to unification. This study systematically analyzes the reform requirements from legal, organizational, and technical aspects. The right structure of China’s natural resource assets for fulfilling such requirements is examined in this work through a review of relevant legal text, and such a right structure is converted into a draft national technical standard of China’s natural resource administration on the basis of the land administration domain model (LADM). Results show that China’s natural resource administration covers lands, buildings, structures, forests, grasslands, waters, beaches, sea areas, minerals, and other fields. The types of private rights over natural resources include ownerships, land-contracted management rights (cultivated land, forest land, grassland, and water area), rights to use construction land (state-owned and collective-owned), rights to use agricultural land, rights to use homestead land, breeding rights on water areas and beaches, rights to use sea areas, rights to use uninhabited islands, and mining rights. The types of public rights over natural resources include comprehensive land use, urban and rural, sea use, and territory space planning. Furthermore, various types of these property rights can be converted into corresponding classes in LADM on the basis of the analysis of the property subject, object, and rights.

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

Cited by 14 publications

References 47 publications

Geographic Knowledge Graph Attribute Normalization: Improving the Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence Analysis

Geographic Knowledge Graph Attribute Normalization: Improving the Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence Analysis

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

LADM-Based Model for Natural Resource Administration in China

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