Wenqiang Liu

Stanford University | SU · Stanford Center for Biomedical Informatics Research

The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Mostly current works have demonstrated the benefits of knowledge graph embedding in single knowledge graph completion, such as relation extraction. The most significant distinction between multiple knowledge graph...

Considerable effort has been exerted to increase the scale of Linked Data. However, an inevitable problem arises when dealing with data integration from multiple sources. Various sources often provide conflicting objects for a certain predicate of the same real-world entity, thereby causing the so-called object conflict problem. Existing truth disc...

Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient’s symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and corresponding treatments. In this p...

Electronic medical records contain multi-format electronic medical data that consist of abundance of medical knowledge. Facing with patient's symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis and corresponding treatments. In this paper...

Considerable effort has been exerted to increase the scale of Linked Data. However, an inevitable problem arises when dealing with data integration from multiple sources. Various sources often provide conflicting objects for a certain predicate of the same real-world entity, thereby causing the so-called object conflict problem. At present, object...

Considerable effort has been made to increase the scale of Linked Data. However, because of the openness of the Semantic Web and the ease of extracting Linked Data from semi-structured sources (e.g., Wikipedia) and unstructured sources, many Linked Data sources often provide conflicting objects for a certain predicate of a real-world entity. Existi...

Considerable effort has been made to increase the scale of Linked Data. However, an inevitable problem when dealing with data integration from multiple sources is that multiple different sources often provide conflicting objects for a certain predicate of the same real-world entity, so-called object conflicts problem. Currently, the object conflict...

Considerable effort has been made to increase the scale of Linked Data. However, because of the openness of the Semantic Web and the ease of extracting Linked Data from semi-structured sources (e.g., Wikipedia) and unstructured sources, many Linked Data sources often provide conflicting objects for a certain predicate of a real-world entity. Existi...

In the community of Linked Data, anyone can publish their data as Linked Data on the web because of the openness of the Semantic Web. As such, RDF (Resource Description Framework) triples described the same real-world entity can be obtained from multiple sources; it inevitably results in conflicting objects for a certain predicate of a real-world e...

The rapidly increasing RDF data in the Linked Open Data (LOD) community project is a valuable resource for obtaining domain knowledge. However, RDF data of specific topics also shows a trend of being more decentralized and fragmented, which makes it difficult and inefficient for the users to get an overview of a specific topic and retrieve the desi...

With the rapid growth of resource description framework (RDF) data, it shows a steady trend of data decentralization and fragmentation. This trend results in two consequences. First, data decentralization and fragmentation easily leads to a narrow understanding of specific topic for users, because of difficulty in obtaining fully-faceted RDF data o...