Titlebook: Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence; Second China Confere Juanzi Li,Ming Zhou,Jianfeng Du Confere

网络添麻烦

遣返回国

glamor

https://doi.org/10.1007/978-981-10-7359-5knowledge graph; semantic computing; semantics; semantic web; knowledge base; learning systems; knowledge

adipose-tissue

Knowledge Base Completion by Learning to Rank Model,rt approaches is Path Ranking Algorithm (PRA), which predicts new facts based on path types connecting entities. PRA treats the relation prediction as a classification problem, and logistic regression is used as the classification model. In this work, we consider the relation prediction as a ranking

被诅咒的人

calorie

现代

DEI

Mendacious

A Survey on Relation Extraction,opulation. To thoroughly comprehend relation extraction, the paper reviews it mainly concentrating on its mainstream methods. Besides, open information extraction (OIE), as a different relation extraction paradigm, is introduced as well. Also, we exploit the challenges and directions for relation ex

使乳化

A Sentiment and Topic Model with Timeslice, User and Hashtag for Posts on Social Media, Joint sentiment/topic models are widely applied in detecting sentiment-aware topics on the lengthy documents. However, the characteristics of posts, i.e., short texts, on social media pose new challenges: (1) context sparsity problem of posts makes traditional sentiment-topic models infeasible; (2)