WNOntoSim: A Hybrid Approach for Measuring Semantic Similarity between Ontologies Based on WordNet

2011 ◽  
Author(s):  
Wei He ◽  
Xiaoping Yang ◽  
Dupei Huang
Keyword(s):  
Semantic Similarity ◽  
Hybrid Approach

A hybrid approach for measuring semantic similarity based on IC-weighted path distance in WordNet

2017 ◽  
Vol 51 (1) ◽  
pp. 23-47 ◽  
Author(s):  
Yuanyuan Cai ◽  
Qingchuan Zhang ◽  
Wei Lu ◽  
Xiaoping Che
Keyword(s):  
Semantic Similarity ◽  
Hybrid Approach ◽  
Path Distance

Lexical Co-Occurrence and Contextual Window-Based Approach With Semantic Similarity for Query Expansion

2018 ◽  
pp. 1552-1575
Author(s):  
Jagendra Singh ◽  
Rakesh Kumar
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Efficient Method ◽  
Query Expansion ◽  
Ad Hoc ◽  
Hybrid Approach ◽  
Information Retrieval System ◽  
Optimal Combination ◽  
Benchmark Datasets ◽  
Pseudo Feedback

Query expansion (QE) is an efficient method for enhancing the efficiency of information retrieval system. In this work, we try to capture the limitations of pseudo-feedback based QE approach and propose a hybrid approach for enhancing the efficiency of feedback based QE by combining corpus-based, contextual based information of query terms, and semantic based knowledge of query terms. First of all, this paper explores the use of different corpus-based lexical co-occurrence approaches to select an optimal combination of query terms from a pool of terms obtained using pseudo-feedback based QE. Next, we explore semantic similarity approach based on word2vec for ranking the QE terms obtained from top pseudo-feedback documents. Further, we combine co-occurrence statistics, contextual window statistics, and semantic similarity based approaches together to select the best expansion terms for query reformulation. The experiments were performed on FIRE ad-hoc and TREC-3 benchmark datasets. The statistics of our proposed experimental results show significant improvement over baseline method.

Lexical Co-Occurrence and Contextual Window-Based Approach with Semantic Similarity for Query Expansion

2017 ◽  
Vol 13 (3) ◽  
pp. 57-78 ◽  
Author(s):  
Jagendra Singh ◽  
Rakesh Kumar
Keyword(s):  
Semantic Similarity ◽  
Query Expansion ◽  
Ad Hoc ◽  
Retrieval System ◽  
Hybrid Approach ◽  
Information Retrieval System ◽  
Query Reformulation ◽  
Baseline Method ◽  
Benchmark Datasets ◽  
Pseudo Feedback

Query expansion (QE) is an efficient method for enhancing the efficiency of information retrieval system. In this work, we try to capture the limitations of pseudo-feedback based QE approach and propose a hybrid approach for enhancing the efficiency of feedback based QE by combining corpus-based, contextual based information of query terms, and semantic based knowledge of query terms. First of all, this paper explores the use of different corpus-based lexical co-occurrence approaches to select an optimal combination of query terms from a pool of terms obtained using pseudo-feedback based QE. Next, we explore semantic similarity approach based on word2vec for ranking the QE terms obtained from top pseudo-feedback documents. Further, we combine co-occurrence statistics, contextual window statistics, and semantic similarity based approaches together to select the best expansion terms for query reformulation. The experiments were performed on FIRE ad-hoc and TREC-3 benchmark datasets. The statistics of our proposed experimental results show significant improvement over baseline method.

scholarly journals Semantic Similarity Measures Applied to an Ontology for Human-Like Interaction

2012 ◽  
Vol 44 ◽  
pp. 397-421 ◽  
Author(s):  
E. Albacete ◽  
J. Calle ◽  
E. Castro ◽  
D. Cuadra
Keyword(s):  
Knowledge Base ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Hybrid Approach ◽  
Similarity Measures ◽  
Similarity Function ◽  
Training Methods ◽  
Five Dimensions ◽  
Interaction System ◽  
Global Similarity

The focus of this paper is the calculation of similarity between two concepts from an ontology for a Human-Like Interaction system. In order to facilitate this calculation, a similarity function is proposed based on five dimensions (sort, compositional, essential, restrictive and descriptive) constituting the structure of ontological knowledge. The paper includes a proposal for computing a similarity function for each dimension of knowledge. Later on, the similarity values obtained are weighted and aggregated to obtain a global similarity measure. In order to calculate those weights associated to each dimension, four training methods have been proposed. The training methods differ in the element to fit: the user, concepts or pairs of concepts, and a hybrid approach. For evaluating the proposal, the knowledge base was fed from WordNet and extended by using a knowledge editing toolkit (Cognos). The evaluation of the proposal is carried out through the comparison of system responses with those given by human test subjects, both providing a measure of the soundness of the procedure and revealing ways in which the proposal may be improved.

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