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Experimental data for computing semantic similarity between concepts using multiple inheritances in Wikipedia

Muhammad Jawad Hussain1, Shahbaz Hassan Wasti1,2, Guangjian Huang1

  • 1School of Computer Science, South China Normal University, Guangzhou 510631, China.

Data in Brief
|April 8, 2020
PubMed
Summary
This summary is machine-generated.

This study presents experimental data for the Neighbourhood Aggregated Semantic Contribution (NASC) method, utilizing Wikipedia data to evaluate semantic similarity across English, Spanish, and French benchmarks.

Keywords:
Information contentMultiple inheritancesSemantic similarityWikipedia category graph

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Information Retrieval

Background:

  • Semantic similarity is crucial for understanding word relationships.
  • Existing methods often rely on lexical resources or distributional models.
  • Wikipedia's structured data offers a rich resource for semantic analysis.

Purpose of the Study:

  • To compile and present experimental data for the Neighbourhood Aggregated Semantic Contribution (NASC) approach.
  • To provide a dataset for evaluating semantic similarity using Wikipedia concepts.
  • To facilitate the analysis of Wikipedia Category Graph (WCG) structures across languages.

Main Methods:

  • Utilized Java Wikipedia Library (JWPL)-DataMachine and JWPL WikipediaAPI to extract features from Wikipedia dumps.
  • Processed disambiguated Wikipedia concepts from standard word similarity benchmarks (MC30, RG65es, RG65fr).
  • Extracted category information and calculated neighborhood features (ancestors, common ancestors, pages) for varying parameter k.

Main Results:

  • Generated a dataset containing Wikipedia concepts and their associated categories for English, Spanish, and French.
  • The dataset includes detailed neighborhood statistics within the respective Wikipedia Category Graphs (WCGs).
  • The data enables assessment of semantic similarity for multilingual benchmarks.

Conclusions:

  • The presented dataset supports the evaluation of the NASC semantic similarity approach.
  • It offers a valuable resource for cross-lingual comparison of Wikipedia's taxonomic structures.
  • The data can be used for further research in computational semantics and knowledge representation.