TDN: An Integrated Representation Learning Model of Knowledge Graphs
نویسندگان
چکیده
منابع مشابه
Representation Learning for Visual-Relational Knowledge Graphs
Much progress has been made towards the goal of developing ML systems that are able to recognize and interpret visual scenes. With this paper, we propose query answering in visual-relational knowledge graphs (KGs) as a novel and important reasoning problem. A visual-relational KG is a KG whose entities are associated with image data. We introduce IMAGEGRAPH, a publicly available KG with 1330 re...
متن کاملType-Constrained Representation Learning in Knowledge Graphs
Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides s...
متن کاملRepresentation Learning of Knowledge Graphs with Hierarchical Types
Representation learning of knowledge graphs aims to encode both entities and relations into a continuous low-dimensional vector space. Most existing methods only concentrate on learning representations with structured information located in triples, regardless of the rich information located in hierarchical types of entities, which could be collected in most knowledge graphs. In this paper, we ...
متن کاملRepresentation Learning of Knowledge Graphs with Entity Descriptions
Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low-dimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between entities. In fact, in most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods. In this pa...
متن کاملNeuro-symbolic representation learning on biological knowledge graphs
Motivation Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We deve...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2913086