CORE: A knowledge graph entity type prediction method via complex space regression and embedding

Gene Function Prediction via Discriminative Graph Embedding

Gene function has been a subject of interest but it is far from fully understood. It is known that some genes have certain functions but it is not clear whether those are all the functions they have. It is a recent trend to use different means to predict gene functions; one of them is to use computational methods on large data sets. Different types of information are used in computational metho...

Knowledge Graph Embedding via Dynamic Mapping Matrix

Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as TransE, TransH and TransR/CTransR regard a relation as translation from head entity to tail entity and the CTransR achieves state-of-the-art performance. In this paper, we propose a more fine-grained model named TransD, which is an improvement of TransR/CTransR. In Trans...

A Joint Embedding Method for Entity Alignment of Knowledge Bases

Safe Medicine Recommendation via Medical Knowledge Graph Embedding

Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant...

Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding

This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine, SemanticScholar.org, reveals that a major error source is its inability to understand the meaning of research concepts in queries. To addresses this challenge, ESR represents queries and documents in the entity space ...