Research Article
A Visual Semantic Relations Detecting Method Based on WordNet
@INPROCEEDINGS{10.1007/978-3-030-32388-2_40, author={Wenxin Li and Tiexin Wang and Jingwen Cao and Chuanqi Tao}, title={A Visual Semantic Relations Detecting Method Based on WordNet}, proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings}, proceedings_a={MLICOM}, year={2019}, month={10}, keywords={WordNet Neo4J Semantic relations Knowledge-Driven}, doi={10.1007/978-3-030-32388-2_40} }- Wenxin Li
Tiexin Wang
Jingwen Cao
Chuanqi Tao
Year: 2019
A Visual Semantic Relations Detecting Method Based on WordNet
MLICOM
Springer
DOI: 10.1007/978-3-030-32388-2_40
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.
