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In the theory of knowledge graphs, words are represented by word graphs. Sentences are to be represented by sentence graphs. This is called structural parsing. Under consideration of the semantic and syntactic features of natural language, both semantic and syntactic word graphs are formed, the latter expressing the function of word types like nouns, verbs,(More)
In this paper we present a new approach to extract relevant information by knowledge graphs from natural language text. We give a multiple level model based on knowledge graphs for describing template information, and investigate the concept of partial structural parsing. Moreover, we point out that expansion of concepts plays an important role in thinking,(More)
Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm include collaborative filtering method applied in Amazon, matrix factorization algorithm from Netflix, etc. In this article, we hope to combine traditional model with behavior pattern extraction(More)
Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm includes item-based collaborative filtering method applied in Amazon, matrix factorization based collaborative filtering algorithm from Netflix, etc. In this article, we hope to combine traditional(More)
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