Abstract
In the process of learning and reasoning knowledge graph, the existing tensor decomposition technology only considers the direct relationship between entities in knowledge graph. However, it ignores the characteristics of the graph structure of knowledge graph. To solve this problem, a knowledge graph reasoning algorithm based on multihop relational paths learning (MHRP-learning) and tensor decomposition is proposed in this paper. Firstly, MHRP-learning is adopted to obtain the relationship path between entity pairs in the knowledge graph. Then, the tensor decomposition is performed to get a novel learning framework. Finally, experiments show that the proposed method achieves advanced results, and it is applicable to knowledge graph reasoning.
Highlights
In recent years, the path sorting algorithm has become a promising method for learning large-scale knowledge graph inference paths [15,16,17]
(2) Tensor decomposition is used to make inference in these paths, and the path between entity pairs in knowledge graph is calculated by means of MHRP-learning. (3) e multipath relationship between entities in knowledge graph and the new facts between entities are explored to further enrich and improve of knowledge graph
Knowledge graph can have hierarchical relationships of concepts, the number of these relationships is much less than the number of relationships between entities, and the early semantic network is mainly used for the representation of natural language sentences
Summary
Compared with the earlier Semantic Web, the knowledge graph has its own characteristics. Erefore, through the link prediction of knowledge map, these missing relationships can be completed or the wrong relationships can be corrected so as to achieve the function of improving knowledge map. It is a very important task in knowledge graph learning and reasoning. In response to the question, “Does a dog have a tail?” the triplet (dog, has part, tail) can be constructed and the correctness of the triplet is judged, so as to achieve the knowledge map learning and reasoning [19]. Rough learning and reasoning algorithms, the missing entities and relationships can be extracted from Semantic Web or other relevant databases, enriching and perfecting the knowledge map Free-base has about 71% without the attribute of place of birth and about 75% without the attribute of nationality [20]. erefore, improving knowledge graph is one of the most important tasks in knowledge graph learning and reasoning. rough learning and reasoning algorithms, the missing entities and relationships can be extracted from Semantic Web or other relevant databases, enriching and perfecting the knowledge map
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