Noisy Knowledge Graph Representation Learning: a Rule-Enhanced Method

Abstract

Knowledge graphs are used to store structured facts, which are presented in the form of triples, i.e., (head entity, relation, tail entity). Current large-scale knowledge graphs are usually constructed with (semi-) automated methods for knowledge extraction and the process inevitably introduces noise, which may affect the effectiveness of the knowledge representation. However, most traditional representation learning methods assume that the triples in knowledge graphs are correct and represent knowledge in a distributed manner accordingly. Therefore, noise detection on knowledge graphs is a crucial task. In addition, the incompleteness of knowledge graphs has also attracted people’s attention. The above problems are studied and a knowledge representation learning framework combining logical rules and relation path information is proposed, which accomplishes knowledge representation learning and achieves a mutual enhancement effect while detecting possible noise. Specifically, the framework is divided into a triple embedding part and a triple trustworthiness estimation part. In the triple embedding part, relation path information and logical rule information are introduced to construct a better knowledge representation based on the triple structure information, the latter of which is used to enhance the ability of relation path reasoning and the interpretability of the representation learning. In the triple trustworthiness estimation part, three types of information are further utilized to detect possible noise. Experiments are conducted on three public evaluated datasets and the results show that the model achieves significant performance improvement in tasks such as knowledge graph noise detection and knowledge complementation compared with all baseline methods.

Keywords

• knowledge graph; knowledge graph completion; noise detection; triple trustworthiness; triple embedding