Entity Resolution of Japanese Apartment Property Information Using Neural Networks

In Japan, there are many real estate companies and agencies, who create apartment room property records and register them to some real estate portal sites to be advertised. The apartment room records include the apartment building attributes information. However, the building attributes values are not entered by referring to the common building database but are arbitrarily created and entered by each company or agency. For effective use of property information, apartment rooms must be linked to the correct apartment building. In this regard, aggregating property information belonging to the same building (entity resolution) is typically performed by a rule-based process that statistically considers the similarity of attributes such as the building name, number of floors, or year/month the building was built. However, when property information is stored by room and registered by different businesses, the corresponding building information may be inconsistent, incomplete, or inaccurate. Therefore, entity resolution using a rule-based method is insufficient and requires extensive manual post-processing. This study proposes an entity resolution method for apartment properties using neural networks with inputs containing traditional property attributes and new attributes obtained from the phonetic and semantic pre-processing of building names. The experimental results show that the proposed method improves entity resolution accuracy.