A re-ranking model for accurate knowledge base completion with knowledge base schema and web statistic

Progress and directions in low-cost redox flow batteries for large-scale energy storage National Science Review 4, 91 (2017); Research trend of large-scale supercomputers and applications from the TOP500 and Gordon Bell Prize SCIENCE CHINA Information Sciences 63, 171001 (2020); Research on evolution and prevention of the No.1 large-scale dangerous slope of Erlang Mountain on the Sichuan-Tibet Highway Science in China Series E-Technological Sciences 46, 42 (2003); Developing trend of design and manufacture technology for large-scale wind turbine blade

show abstract

Section: 翻译法unclassified

Research progress of large-scale knowledge graph completion technology

Wang

Meng

2020

Sci. Sin.-Inf.

View full text Add to dashboard Cite

show abstract

“…However, their method still does not cover n-to-n relations, since it is based on knowledge graph embedding which utilizes the characteristics of 1-to-1 relations. On the other hand, Choi et al proposed a re-ranking model that uses both internal and external information of a knowledge graph for more accurate knowledge base completion [12]. Their model first extracts top-k candidates according to the plausibility computed by knowledge graph embedding.…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes a sole ranking model that adopts a committee of knowledge graph embeddings for accurate knowledge base completion. Unlike previous work that represents knowledge base completion as a re-ranking task [12], we formulate it as a ranking task. Given a knowledge base, the proposed model generates candidate facts, and then the plausibility of each candidate is determined by a committee of knowledge graph embeddings, not by a single knowledge graph embedding.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding

2020

Self Cite

View full text Add to dashboard Cite

Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks. Nevertheless, they are not normally complete and have many missing facts. Such missing facts keep them from being used in diverse applications in spite of their usefulness. Therefore, it is significant to complete knowledge bases. Knowledge graph embedding is one of the promising approaches to completing a knowledge base and thus many variants of knowledge graph embedding have been proposed. It maps all entities and relations in knowledge base onto a low dimensional vector space. Then, candidate facts that are plausible in the space are determined as missing facts. However, any single knowledge graph embedding is insufficient to complete a knowledge base. As a solution to this problem, this paper defines knowledge base completion as a ranking task and proposes a committee-based knowledge graph embedding model for improving the performance of knowledge base completion. Since each knowledge graph embedding has its own idiosyncrasy, we make up a committee of various knowledge graph embeddings to reflect various perspectives. After ranking all candidate facts according to their plausibility computed by the committee, the top-k facts are chosen as missing facts. Our experimental results on two data sets show that the proposed model achieves higher performance than any single knowledge graph embedding and shows robust performances regardless of k. These results prove that the proposed model considers various perspectives in measuring the plausibility of candidate facts.

show abstract

A re-ranking model for accurate knowledge base completion with knowledge base schema and web statistic

Cited by 2 publications

References 16 publications

Research progress of large-scale knowledge graph completion technology

Research progress of large-scale knowledge graph completion technology

An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding

Contact Info

Product

Resources

About