A knowledge base construction method for science and technology information analysis

Abstract

The invention discloses a knowledge base construction method for scientific and technological information analysis, belonging to the field of computer knowledge base construction. A CWATT-BiLSTM-LSTMdmodel is provided for entity extraction, and an RL-TreeLSTM model is used for entity relation extraction. An encoding-decoding mode is adopted for entity extraction, a BiLSTM (Bidirectional Long-Short Memory Network) is used for coding, LSTMd (Long-Short Memory Network) is used for decoding, and the embedding layer and decoding layer are improved. Then the model is used to extract entities fromthe corpus in the field of science and technology information. On the basis of deep reinforcement learning, the RL-TreeLSTM model is provided to extract the relationship between entities. The RL-TreeLSTM model is divided into two parts: a selector and a classifier. The selector selects effective sentences to the classifier in order to reduce the noise caused by the remote monitoring method. The classifier extracts the entity relation from the effective sentences, which improves the accuracy of relation extraction.

Description

technical field [0001] The invention belongs to the field of computer knowledge base construction, and in particular relates to a knowledge base construction method for scientific and technological information analysis. Background technique [0002] As a collection of knowledge, knowledge base plays a very important role in intelligent information processing. One of the core technologies of building a knowledge base is the extraction of entities and their relationships. The main goal of entity extraction is to extract entities that appear in a given sentence, usually using machine learning models and deep learning models. For example, the CRF (Conditional Random Field) model of machine learning needs to define a feature function, which focuses on the linear weighted combination of local features in the entire sentence. The quality of the feature template has a direct impact on the result of entity extraction; another example is the LSTM of deep learning ( Long-term short-t...

AI Technical Summary

Problems solved by technology

[0004] This improvement aims at the problem that a single network cannot simulate label dependence in entity extraction and the noise problem of remote supervision labeling in entity relationship extraction. The CWATT-BiLSTM-LSTMd model is proposed for entity extraction, and the RL-TreeLSTM model is used for entity relationship extraction.

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