Jun Jiang

TL;DR: In this paper, a pre-trained decision tree is used to obtain the attribute similarity and the confidence coefficient of each attribute of an instance pair to be matched, and then the confidence coefficients are combined with a regulation coefficient to calculate and output the entity similarity of the instance pair.

TL;DR: In this paper, the authors proposed a method and system for entities matching, which comprises the following steps: respectively determining respective attribute distinction degree according to attribute value distributes of two to-be-processed entities; computing attribute similarity degree of the two to be-processing entities according to non-primary attribute values of each entity; weighting and summing the attribute distinction degrees and the attribute similarity degrees to obtain an entity similarity of the entities; comparing the entity similarity degree with a similarity threshold value to judge the similarity of entities.

TL;DR: A rule-based algorithm based on a tree-like structure, which can not only deal with noisy and missing values, but also greatly improve the efficiency of the method by finding out matched instances or filtering unmatched instances as early as possible is proposed.

TL;DR: This demonstration introduces HouseIn, a novel HRP that expect to provide users a clear big picture in several aspects about the housing rental market of a particular interested area; and detect those advertisements referring to the same property, such that to give users a price comparison between various platforms for the same apartment for rental.