Mining valuable hidden knowledge from large-scale data relies on the support of reasoning
technology. Knowledge graphs, as a new type of knowledge representation, have gained …

Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …

With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …

Knowledge graph (KG) embedding is to embed components of a KG including entities and
relations into continuous vector spaces, so as to simplify the manipulation while preserving …

Abstract Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their creation and …

Abstract Knowledge base completion (KBC) aims to automatically infer missing facts by
exploiting information already present in a knowledge base (KB). A promising approach for …

We consider learning representations of entities and relations in KBs using the neural-
embedding approach. We show that most existing models, including NTN (Socher et al …

Relational machine learning studies methods for the statistical analysis of relational, or
graph-structured, data. In this paper, we provide a review of how such statistical models can …

In this paper we show the surprising effectiveness of a simple observed features model in
comparison to latent feature models on two benchmark knowledge base completion …