Scientific and technological character knowledge graph construction method and device based on deep learning model, and terminal

Abstract

The invention discloses a scientific and technological character knowledge graph construction method and device based on a deep learning model and a terminal, and the method comprises the steps of extracting text data with scientific and technological information, and carrying out the entity recognition and labeling, and obtaining a sample corpus with labels; building a deep learning model to train the sample corpus with the labels, and obtaining an information extraction model; performing science and technology information extraction on the open domain text data based on an information extraction model to obtain a science and technology knowledge triple of the open domain text; performing knowledge fusion and updating; and constructing the science and technology knowledge graph based on the science and technology knowledge triple of the open domain text. According to the invention, the difficulty and time cost of information extraction are greatly reduced, and the difficulty of knowledge graph construction is effectively reduced, so that the knowledge graph with timeliness and accuracy can be constructed for continuously updated and changed scientific and technological information text data.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a method, device and terminal for constructing a knowledge map of scientific and technological figures based on a deep learning model. Background technique [0002] With the advent of the "big data" era, the amount of data available for research and analysis has exploded. However, most of these massive data are unstructured text data composed of natural language. Therefore, how to extract effective information from unstructured text information and form structured information that is easy to understand and store has become a research hotspot in recent years. . [0003] To process text data with complex entity relationships, there are generally the following two steps. First, extract information from the text data, and then store the extracted knowledge in a structured manner for further processing and utilization. In terms of information extraction, there are extra...

AI Technical Summary

Problems solved by technology

The traditional way of information extraction is manual processing, but with the acceleration of data update, manual processing of text data not only takes a lot of time and energy, but also brings information update delay

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