Computing Semantic Similarity of Concepts in Knowledge Graphs

Zhu, Ganggao and Iglesias Fernandez, Carlos Angel (2016). Computing Semantic Similarity of Concepts in Knowledge Graphs. "IEEE Transactions on Knowledge And Data Engineering", v. 28 (n. 99); ISSN 1041-4347. https://doi.org/10.1109/TKDE.2016.2610428.

Description

Title: Computing Semantic Similarity of Concepts in Knowledge Graphs
Author/s:
  • Zhu, Ganggao
  • Iglesias Fernandez, Carlos Angel
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Knowledge And Data Engineering
Date: 21 September 2016
ISSN: 1041-4347
Volume: 28
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería de Sistemas Telemáticos [hasta 2014]
Creative Commons Licenses: None

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (387kB) | Preview

Abstract

This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g., path length and depth), or only on the Information Content (IC) of concepts. We propose a semantic similarity method, namely wpath, to combine these two approaches, using IC to weight the shortest path length between concepts. Conventional corpus-based IC is computed from the distributions of concepts over textual corpus, which is required to prepare a domain corpus containing annotated concepts and has high computational cost. As instances are already extracted from textual corpus and annotated by concepts in KGs, graph-based IC is proposed to compute IC based on the distributions of concepts over instances. Through experiments performed on well known word similarity datasets, we show that the wpath semantic similarity method has produced a statistically significant improvement over other semantic similarity methods. Moreover, in a real category classification evaluation, the wpath method has shown the best performance in terms of accuracy and F score.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2015-68284-RSEMOLAUniversidad Politécnica de MadridTecnologías de análisis de sentimientos y emociones para agentes sociales empáticos en inteligencia ambiental
Government of SpainRTC-2016-5053-7EmoSpacesINNOVATI NETWORKS SLUnspecified
Madrid Regional GovernmentS2013/ICE‐3019MOSI-AGIL-CMCarlos Ángel Iglesias FernándezModelado social de inteligencia ambiental aplicado a grandes instalaciones
FP7296277EUROSENTIMENTPARADIGMA TECNOLOGICO SLLanguage Resource Pool for Sentiment Analysis in European Languages
Horizon 2020141111MixedEmotionsUniversidad Politécnica de MadridSocial Semantic Emotion Analysis for Innovative Multilingual Big Data Analytics Markets

More information

Item ID: 43462
DC Identifier: http://oa.upm.es/43462/
OAI Identifier: oai:oa.upm.es:43462
DOI: 10.1109/TKDE.2016.2610428
Official URL: http://ieeexplore.ieee.org/document/7572993/
Deposited by: Ph.D. Alvaro Carrera Barroso
Deposited on: 06 Oct 2016 07:59
Last Modified: 15 Mar 2019 18:24
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM