Abstract
In order to implement automatic inference, this paper proposes a visual semantic-relations detecting method (VSRDM) based on WordNet. WordNet is an excellent relational dictionary, but it lacks deep semantic topology function because of its index-based text storage structure. As a graphical database, Neo4J provides visualization of its internal data. Since the abstract data structure in WordNet matches Neo4J’s ternary storage structure, it is very suitable to map WordNet completely with Neo4J graph instance. This paper studies how to fully describe WordNet in Neo4J through a ternary structure. Neo4J stores the data as graphs (nodes and edges) and provides certain native graph algorithms to search the data. The speed of matching query between nodes is varying linearly with the number of nodes, so the efficiency of basic operation is guaranteed. With the help of Neo4J, VSRDM works as a semantic dictionary providing relationships matching, reasoning auxiliary and other functions.
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Acknowledgement
This work was supported by the “Fundamental Research Funds for the Central Universities Nos. 3082018NS2018057”, the National Natural Science Foundation of China (61872182), the National Natural Science Foundation of China under Grant No. 61402229 and No. 61602267, the Open Fund of the State Key Laboratory for Novel Software Technology (KFKT2018B19) and the Open Fund of the Ministry Key Laboratory for Safety-Critical Software Development and Verification (1015-XCA1816401).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, W., Wang, T., Cao, J., Tao, C. (2019). A Visual Semantic Relations Detecting Method Based on WordNet. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_40
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DOI: https://doi.org/10.1007/978-3-030-32388-2_40
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