Titlebook: Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding; 4th China Conference Xiaoyan Zhu,Bing Qin,Longhua Q

nocturnal

Ambulatory

联想记忆

daredevil

Incorporating Domain and Range of Relations for Knowledge Graph Completion,nowledge graph related tasks like link prediction. Knowledge graph embedding methods embed entities and relations into a continuous vector space and accomplish link prediction via calculation with embeddings. However, some embedding methods only focus on information of triples and ignore individual

嬉耍

REKA: Relation Extraction with Knowledge-Aware Attention,truct labeled data to reduce the manual annotation effort. This method usually results in many instances with incorrect labels. In addition, most of existing relation extraction methods merely rely on the textual content of sentences to extract relation. In fact, many knowledge graphs are off-the-sh

Horizon

容易生皱纹

A Survey of Question Answering over Knowledge Base,y. The core task of KBQA is to understand the real semantics of a natural language question and extract it to match in the whole semantics of a knowledge base. However, it is exactly a big challenge due to variable semantics of natural language questions in a real world. Recently, there are more and

voluble

修正案

A Practical Framework for Evaluating the Quality of Knowledge Graph,hs were built using automated construction tools and via crowdsourcing. The graph may contain significant amount of syntax and semantics errors that great impact its quality. A low quality knowledge graph produce low quality application that is built on it. Therefore, evaluating quality of knowledge

健谈

Entity Subword Encoding for Chinese Long Entity Recognition,re-defined categories. For Chinese NER task, recognition of long entities has not been well addressed yet. When character sequences of entities become longer, Chinese NER becomes more difficult with existing character-based and word-based neural methods. In this paper, we investigate Chinese NER met