Study of Concept Similarity Algorithm Based on Ontology Structure

Similarity computing of ontological concept has made rapid progress in the field of data mining, information processing and artificial intelligence and becoming one of the hot research field of information technology, particularly the idea of the semantic Web was proposed in 2000, the concept of semantic similarity has gotten more attention, while also facilitating its further development and application in information retrieval. Considering the deficiencies of existing concept similarity algorithm, this paper design the method to reduce the candidate set of domain concept, and put forward a similarity calculation model based on the concept name, instances, properties, and semantic structure of domain ontology. Integrated several main influencing factors, the experiments show the proposed algorithm can express the impact of various factors on the similarity in the calculation concept similarity of domain ontology. By comparing with the traditional similarity method and expertise experience value, the experiment result shows that the effectiveness and correctness of the concept similarity calculation model.

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A Semantic Retrieval Method Based on Ontology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.2179 ◽

2014 ◽

Vol 989-994 ◽

pp. 2179-2183

Author(s):

Qi Shen ◽

Meng Zhang

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Language Processing ◽

Search Method ◽

Semantic Search ◽

Semantic Retrieval ◽

Retrieval Method ◽

Similarity Algorithm ◽

Tourism Information ◽

Concept Similarity

Semantic retrieval method stands at the crossroads between Natural Language Processing and Machine Intelligent. This paper makes analysis on the semantic search method and research on concept similarity algorithm, and discusses the factor of weight’s influence on concept similarity as well. On this basis, this paper proposed a new semantic search method based on ontology, and apply it to the tourism information retrieval, which intellectualized tourism information retrieval service.

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Event Detection through Lexical Chain Based Semantic Similarity Algorithm

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1166/1/012016 ◽

2021 ◽

Vol 1166 (1) ◽

pp. 012016

Author(s):

Swati Jain ◽

Suraj Prakash Narayan ◽

Nalini Meena ◽

Rupesh Kumar Dewang ◽

Utkarsh Bhartiya ◽

...

Keyword(s):

Semantic Similarity ◽

Event Detection ◽

Similarity Algorithm ◽

Lexical Chain

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User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

Journal Of Big Data ◽

10.1186/s40537-021-00425-x ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Triyanna Widiyaningtyas ◽

Indriana Hidayah ◽

Teguh B. Adji

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

User Behavior ◽

Correlation Coefficients ◽

User Profile ◽

Profile Data ◽

Similarity Algorithm ◽

Previous Algorithm ◽

Movie Recommendation ◽

Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

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