Relation extraction method for property involved based on multiple templates and fuzzy competition

Abstract:

Extracting the relation of the property involved from laws and regulations to build a knowledge base is of great significance to the intelligent management of the property involved in the judicial practice. However, in this scenario, the training corpus is small and single, while high extraction accuracy required, so the relation extraction method of machine learning is not applicable. Aiming at addressing this problem, we propose a new relation extraction approach that extracts relations by template matching with fuzzy logic mechanism. In this approach, in order to cater to the actual needs of the knowledge base for property involved, two attributes are added to the traditional threetuple relation to obtain the five-tuple relation model. Three extraction templates were designed from different dimensions, and the extraction results were judged with the help of fuzzy logic to compete for a better five-tuple relation, which provided support for the construction of the knowledge base for property involved.