Knowledge graph question-answering system for professional field

Abstract

The invention discloses a knowledge graph question-answering system for the professional field. The system comprises a knowledge extraction module and an answer generation module, the processing process mainly comprises the following steps of extracting cross-modal collaborative professional field knowledge: performing knowledge extraction by utilizing text modal collaborative image and video modal data so as to solve the problem that a traditional knowledge extraction method is difficult to be directly applied to a professional field due to multi-source isomerism and multi-modality; questions and answers of multi-source knowledge are fused, and multi-source candidate answers are obtained through semantic matching and answer generation by utilizing professional domain knowledge information; and semantic fusion of the multi-source candidate answers, and generation of a final answer based on answer confidence reordering and answer key point reorganization of the multi-source knowledge. The knowledge graph question-answering system for the professional field solves the knowledge question-answering of multi-source heterogeneous and multi-modal data in the professional field, and is suitable for the multi-modal knowledge question-answering in other fields.

Description

technical field [0001] The invention relates to a knowledge graph question answering system for professional fields, which belongs to the technical field of natural language processing. Background technique [0002] With the development of computer and Internet technology, the data for professional fields is increasing exponentially. These data are large in scale and have the characteristics of multi-source heterogeneity. On the one hand, the data in professional fields come from different data sources, such as literature and reports. On the other hand, data in the professional field has obvious heterogeneous characteristics, covering text, picture, video and other modalities. How to quickly and accurately obtain the information needed by users from these multi-source heterogeneous professional field data has become an urgent problem to be solved, and the most important technology to solve this problem is intelligent question answering technology based on knowledge graph. ...

AI Technical Summary

Problems solved by technology

At present, the question answering technology based on knowledge graph can be roughly divided into the method based on semantic analysis and the method based on information retrieval, which cannot better meet the question answering needs of multi-source heterogeneous data in the professional field.

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