!328 Transition-based Knowledge Graph Embedding with Relational Mapping Properties

Many knowledge repositories nowadays con-tain billions of triplets, i.e. (head-entity, re-lationship, tail-entity), as relation instances. These triplets form a directed graph with enti-ties as nodes and relationships as edges. How-ever, this kind of symbolic and discrete stor-age structure makes it difficult for us to exploit the knowledge to enhance other intelligence-acquired applications (e.g. the Question-Answering System), as many AI-related al-gorithms prefer conducting computation on continuous data. Therefore, a series of e-merging approaches have been proposed to facilitate knowledge computing via encoding the knowledge graph into a low-dimensional embedding space. TransE is the latest and most promising approach among them, and can achieve a higher performance with few-er parameters by modeling the relationship as a transitional vector from the head entity to the tail entity. Unfortunately, it is not flex-ible enough to tackle well with the various mapping properties of triplets, even though it-s authors spot the harm on performance. In this paper, we thus propose a superior model called TransM to leverage the structure of the knowledge graph via pre-calculating the dis-tinct weight for each training triplet according to its relational mapping property. In this way, the optimal function deals with each triplet de-pending on its own weight. We carry out ex-tensive experiments to compare TransM with the state-of-the-art method TransE and other prior arts. The performance of each approach is evaluated within two different application s-cenarios on several benchmark datasets. Re-sults show that the model we proposed signifi-cantly outperforms the former ones with lower parameter complexity as TransE.