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A Method for Generating Ontology Concept and Hierarchical Relationship

A technology of hierarchical relationships and concepts, applied in the field of ontology learning, can solve problems such as poor learning effect of ontology concepts and levels

Active Publication Date: 2015-10-28
TONGJI UNIV
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AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention will overcome the shortcomings of ontology concepts and hierarchical learning effects in the prior art, provide an improved ontology concept and hierarchical relationship generation method, combine the ontology concept learning method based on probability statistics and linguistic models, PAM The probabilistic topic model is applied to the concept learning stage of ontology to improve the effect of ontology concept and hierarchical learning, so as to achieve more accurate and effective generation of ontology concepts and hierarchical relationships

Method used

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  • A Method for Generating Ontology Concept and Hierarchical Relationship
  • A Method for Generating Ontology Concept and Hierarchical Relationship
  • A Method for Generating Ontology Concept and Hierarchical Relationship

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Embodiment Construction

[0040] like figure 1 As shown in the structural block diagram, the method of the present invention is applied to the ontology knowledge learning in the field of biomedicine, and the implementation steps of the ontology concept and the hierarchical learning method of this application example are as follows:

[0041] 1) Term extraction module:

[0042] In this embodiment, the ontology concept and hierarchical relationship generation method of the present invention are adopted, and the Genia corpus in the field of biomedicine is used for preprocessing, which consists of 2000 abstracts and a total of 168384 biological terms. Use Pos-tagger for part-of-speech tagging, Stemming for stem extraction, and remove stop words according to the stop word list. After processing these natural language processing technologies, the obtained word set is the domain term set T.

[0043] 2) Statistical inference module:

[0044] In this embodiment, the extracted term set T is subjected to word fr...

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Abstract

The invention relates to the field of ontology learning, in particular to an ontology concept and hierarchical relation generation method. The technical scheme includes that a PAM (probability aim model) is applied to extraction of ontology concept and hierarchical relation to improve effect of ontology concept and hierarchical relation learning so as to generate ontology concept more accurately and effectively. The method includes: establishing a PAM-based ontology concept and hierarchy generation model to effectively convert a domain ontology concept learning problem into a domain document set based statistic inference problem, and adopting a Gibbs sampling method to obtain a probability distribution feature vector; performing Wordnet-based semantic similarity calculation, generating a conception according to similarity correlation, and obtaining a set and hierarchical relation of ontology concepts. By the method, concept sets and hierarchical relation among concepts in domain ontology can be obtained more accurately and effectively.

Description

technical field [0001] The invention relates to the field of ontology learning, in particular to a method for generating ontology concepts and hierarchical relations. Background technique [0002] In the Semantic Web architecture, the ontology used to represent the semantics of Web information is the core and key of the system. As a modeling tool that can describe conceptual models at semantic and knowledge levels and a shared conceptual model that expresses knowledge, ontology is widely used in semantic Web, knowledge engineering, natural language processing, multi-agent systems, information retrieval, intelligent information integration and other related fields. play an important role in. [0003] Ontology learning is to automatically or semi-automatically obtain desired ontology knowledge from existing data resources through techniques such as machine learning and statistics. Since it is unrealistic to realize fully automatic knowledge acquisition technology, the whole ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/27
Inventor 王俊丽王志成赵卫东柳先辉余淼淼梁梅连
Owner TONGJI UNIV
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