Guiyu Tian
ABSTRACT
At present, most enterprises and organizations attach great importance to the establishment of knowledge base in order to condense and summarize their core competitiveness, so as to inherit knowledge. However, at present, most of the knowledge base is a document library, and the structured information in the knowledge base is very lacking. Based on this, this paper proposes a knowledge embedding vector technology, which combines keyword extraction and text self-coding technology, so as to abstract and represent knowledge semantically in embedded vector space, and achieve the purpose of enriching structured information in knowledge base. Moreover, the knowledge embedding vector technology proposed in this paper can provide theoretical basis for the upper application of knowledge base, such as knowledge graph construction, knowledge search, knowledge recommendation and application.
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Index Terms
- Research on the knowledge embedding vector method based on TF-IDF and Auto-Encoder
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