A New Measure of Word Semantic Similarity Based on WordNet Hierarchy and DAG Theory

Qin, Peng; Zhao, Lu; Yan, Yu; Wu, Fang

doi:10.1109/wism.2009.44

Cited by 19 publications

(7 citation statements)

References 8 publications

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“…As sample limited, the priori probability of Dv=16 cannot be calculated by statistical techniques and piecewise linear interpolation method. We stipulate that the value of DRG (16) is equal to 1, as two concepts with Dv=16 must be the same concept.…”

Section: ) Piecewise Linear Interpolation Strategymentioning

confidence: 99%

“…In this paper, P(C= c 1 |L(v 1 ,v 2 )=LValue i , D v =DValue j ) reflecting the probability of concepts' semantic being same is considered as the common information of concepts, P(C=c 2 |L(v 1 ,v 2 )=LValue i , D v =DValue j ) reflecting the probability of concepts' semantic being irrelevant is considered as the difference of concepts, and on this basis, the concept semantic similarity model can be defined as follows. 16] (1-DRG(j)). As shown in equation (16), concept semantic similarity measuring is converted to computation of P(C=c 1 |L(v 1 ,v 2 )=LValue i ,D v =DValue j ) and P(C=c 2 |L(v 1 ,v 2 )=LValue i ,D v =DValue j ).…”

Section: B Concept Semantic Similarity Measurementmentioning

confidence: 99%

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Subjective Bayes Method for Word Semantic Similarity Measurement

Wang

Zuo

et al. 2013

2013 IEEE 13th International Conference on Data Mining Workshops

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Measuring semantic similarity between words is a classical problem in nature language processing, the result of which can promote many applications such as machine translation, word sense disambiguation, ontology mapping, computational linguistics, etc. This paper combines knowledgebased methods with statistical methods in measuring words similarity, the novel aspect of which is that subjective Bayes method is employed. Firstly, extract evidences based on WordNet; secondly, analyze reasonableness of candidate evidence using scatter plot; thirdly, generate sufficiency measure by statistics and piecewise linear interpolation technique; fourthly, obtain comprehensive posteriori by integrating uncertainty reasoning with conclusion uncertainty synthetic strategy; finally, we quantify word semantic similarity. On data set R&G (65), we conducted experiment through 5-fold cross validation, and the correlation of our experimental results with human judgment is 0.912, with 0.4% improvements over existing best practice, which show that using subjective Bayes method to measure word semantic similarity is reasonable and effective.

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Section: ) Piecewise Linear Interpolation Strategymentioning

confidence: 99%

Section: B Concept Semantic Similarity Measurementmentioning

confidence: 99%

Subjective Bayes Method for Word Semantic Similarity Measurement

Wang

Zuo

et al. 2013

2013 IEEE 13th International Conference on Data Mining Workshops

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“…Common knowledge corpora, such as WordNet [24] and domain-specific corpora, can be used to help to determine the meaning of the words. Based on those corpora, several methods have been proposed [25], [26].…”

Section: A Semantic Similarity Techniquesmentioning

confidence: 99%

An Optimization Approach for Semantic-based XML Schema Matching

Kim¹,

Peng²,

Ivezic³

et al. 2011

IJTEF

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Abstract-We propose a novel solution for semantic-based XML schema matching, taking a mathematical programming approach. This method identifies the globally optimal solution for the problem of matching leaf nodes between two XML schema trees by reducing the tree-to-tree matching problem to simpler problems of path-to-path, node-to-node, and word-to-word matching. We formulate these matching problems as maximum-weighted bipartite graph matching problems with different constraints, which are solved by different mathematical programming techniques, including integer programming and dynamic programming. Solutions to simpler problems provide weights for the next stage until the optimal tree-to-tree matching solution is obtained. The effectiveness of this approach has been verified and demonstrated by computer experiments.

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“…The SWE domain ontology is the concept set of SWE related domain knowledge, shown in Fig.1. The hierarchical classification principle of the SWE ontology is explained in [9,14]. It is mainly divided into eight top-level classes.…”

Section: A Classes Definitionmentioning

confidence: 99%

SSIS: Ontology-Based Analyzing and Reasoning of the Shanghai World Expo Knowledge

Yan

Zhao

et al. 2009

2009 Fifth International Conference on Semantics, Knowledge and Grid

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As a scheduled World Expo in the grand tradition of international fairs and expositions, Shanghai World Expo (SWE) has recently gained much attention for its highly sophisticated online vision called "Online Expo Shanghai". In this context, researchers are working to provide intelligent mechanisms to SWE. To support intelligent SWE managements and services, SWE modelling is an essential requirement to understand current (and future) large scale international exhibitions and to possibly drive more complex intelligent actions. In this paper, based on the knowledge representation technique of ontology modelling, we introduce the SWE ontology specifying of SWE's domain knowledge. It precisely describes the definition and basic hierarchical rules, properties, relations and instances of SWE; furthermore, a set of domain-specific axioms is derived from the specified classes and slots. The Axiom Repository is used for consistency checking and knowledge reasoning. It is viewed as the semantic base for SWE information services, and also the primary part of the SWE Semantic Inquire System (SSIS). We also briefly describe the top architecture of SSIS in which the SWE related knowledge inquiring services are contained and will be evaluated together with SSIS in a practical application project.

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A New Measure of Word Semantic Similarity Based on WordNet Hierarchy and DAG Theory

Cited by 19 publications

References 8 publications

Subjective Bayes Method for Word Semantic Similarity Measurement

Subjective Bayes Method for Word Semantic Similarity Measurement

An Optimization Approach for Semantic-based XML Schema Matching

SSIS: Ontology-Based Analyzing and Reasoning of the Shanghai World Expo Knowledge

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