Knowledge graph mining for realty domain using dependency parsing and QAT models

The real estate business has a lot of risks, and in order to minimize them, you need a lot of information from different sources. Systems based on natural language processing can help customers find this information more easily: question answering, information retrieval, etc. The existing method of question answering requires data aligned with possible questions, which are not easy to obtain, in contrast, the knowledge-graph provides structured information. In this paper, we propose semi-automated ontology generation for the realty domain and a subsequent method for information retrieval related to the knowledge-graph of this ontology. The first contribution is the method for relation extraction method based on dependency-parsing and semantic similarity evaluation, which allows us to form ontology for a particular domain. The second contribution is knowledge-graph completion method based on question answering over text neural network. Our experimental analysis shows the efficiency of the proposed approaches.