An Analysis Method of Semantic Relevancy on Controlled Tag

2012 ◽  
Vol 263-266 ◽  
pp. 1588-1592
Author(s):  
Jiu Qing Li ◽  
Chi Zhang ◽  
Peng Zhou Zhang
Keyword(s):  
Semantic Similarity ◽  
Calculation Method ◽  
Domain Ontology ◽  
Analysis Method ◽  
Semantic Correlation ◽  
Retrieval Efficiency ◽  
Similarity Calculation

To solve resource-tagging inefficiency and low-precision retrieval in special field, an analysis method of tag semantic relevancy based on controlled database was proposed. The characteristic of special field and building method for controlled database were discussed. Domain ontology correlation calculation method was used to get semantic correlation. The tag semantic similarity calculation method was developed for semantic similarity, and normalization was used to increase the similarity accuracy. With semantic correlation and similarity as parameters, the semantic relevancy in special field can be obtained. This method was used successfully in the special field of actual projects, improved resource-tagging and retrieval efficiency.

A Novel Method for Text Similarity Calculation

2013 ◽  
Vol 660 ◽  
pp. 202-206
Author(s):  
Cai Rui ◽  
Li Fei ◽  
Chen Bin ◽  
Quan Cong
Keyword(s):  
Vector Space ◽  
Word Frequency ◽  
Word Order ◽  
Vector Space Model ◽  
Longest Common Subsequence ◽  
Text Similarity ◽  
Space Model ◽  
Frequency Information ◽  
Similarity Calculation ◽  
Common Subsequence

In view of the fact that traditional vector space model for text similarity calculation which does not take word order into consideration leads to bias, this paper puts forward a longest common subsequence and the traditional vector space model of combining text similarity calculation. This method takes the word order and word frequency information into account, using the texts of the longest common subsequence and substring of their information from all public records and the use of word order and word frequency in the text. The importance of similarity calculation is acknowledged, and the traditional vector space model in the calculation of the weight is used on the word frequency information. Some of the dataset collected through the web crawler are used in the proposed text similarity calculation method for testing, and the results proved the effectivity of the method.

Study on Application of Domain Ontology in Semantic Information Retrieval

2013 ◽  
Vol 433-435 ◽  
pp. 1662-1665
Author(s):  
Huan Hai Yang ◽  
Ming Yu Sun
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Calculation Method ◽  
Semantic Information ◽  
Domain Ontology ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Retrieval Method ◽  
Similarity Calculation ◽  
Semantic Information Retrieval

Considering weakness of the traditional retrieval method based on keyword matching, the paper introduced semantic into information retrieval, and proposed a semantic retrieval model based on ontology. The paper offered a construction method of domain ontology and implemented semantic reasoning using Jena and improved a semantic similarity calculation method.

Short Text Similarity Computing Method towards Agriculture Question and Answering Systems

2013 ◽  
Vol 756-759 ◽  
pp. 1309-1313 ◽  
Author(s):  
Bing Jie Sun ◽  
Zhi Chao Liang ◽  
Qing Tian Zeng ◽  
Hua Zhao ◽  
Wei Jian Ni ◽  
...  
Keyword(s):  
Question Answering ◽  
Extended Version ◽  
Text Similarity ◽  
Short Text ◽  
Question Answering System ◽  
The Core ◽  
Similarity Calculation ◽  
Core Issue ◽  
Short Text Similarity ◽  
Computing Method

Text similarity computing is the core issue that question-answering system needs to solve. It is mainly used to filter out the existed problems which are similar to the users questions from database. Because of the low recall of domain keywords in domain text similarity computing based on traditional semantic dictionary, this paper proposed a short text similarity computing method in the field of agriculture based on the extended version of <<Tongyicicilin>> which referred to as <<CiLin>>. This paper propose to consider both the similarity and correlation when calculate the words final similarity. The experimental results show that the proposed short text similarity computing method resolve the problem of the low recall of domain words in traditional semantic dictionary well, and improve the similarity calculation performance of high relevant keywords greatly.

Research on Domain Ontology-Based Concept Similarity Calculation

2013 ◽  
Vol 284-287 ◽  
pp. 3512-3516
Author(s):  
Wen Jie Li ◽  
Sha Sha Shi ◽  
Si Liu
Keyword(s):  
Calculation Model ◽  
Domain Ontology ◽  
Research Field ◽  
Similarity Calculation ◽  
Similarity Algorithm ◽  
The Impact ◽  
Further Development ◽  
Similarity Method ◽  
Domain Concept ◽  
Concept Similarity

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.

Text Similarity Computation Model for Identifying Rumor Based on Bayesian Network in Microblog

2020 ◽  
Vol 17 (5) ◽  
pp. 731-741
Author(s):  
Chengcheng Li ◽  
Fengming Liu ◽  
Pu Li
Keyword(s):  
Bayesian Network ◽  
Calculation Model ◽  
Computation Model ◽  
Text Similarity ◽  
Data Set ◽  
Proposed Model ◽  
Similarity Calculation ◽  
Similarity Algorithm ◽  
User Data ◽  
And Control

The research of text similarity, especially for rumor texts, which constructed the calculation model by known rumors and calculated its similarity. From which, people can recognize the rumor in advance, and improve their vigilance to effectively block and control rumors dissemination. Based on the Bayesian network, the similarity calculation model of microblog rumor texts was built. At the same time, taking into account not only the rumor texts have similar characters, but also the rumor producers have similar characters, and therefore the similarity calculation model of rumor texts makers was constructed. Then, the similarity between the text and the user was integrated, and the microblog similarity calculation model was established. Finally, also experimentally studied the performance of the proposed model on the microblog rumor text and the user data set. The experimental results indicated that the similarity algorithm proposed in this paper could be used to identify the rumors of texts and predict the characters of users more accurately and effectively

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