网络添麻烦
发表于 2025-3-27 00:59:36
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遣返回国
发表于 2025-3-27 04:59:07
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glamor
发表于 2025-3-27 05:45:43
https://doi.org/10.1007/978-981-10-7359-5knowledge graph; semantic computing; semantics; semantic web; knowledge base; learning systems; knowledge
adipose-tissue
发表于 2025-3-27 13:30:10
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
发表于 2025-3-27 16:09:45
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calorie
发表于 2025-3-27 18:14:27
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现代
发表于 2025-3-28 01:09:40
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DEI
发表于 2025-3-28 04:08:25
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Mendacious
发表于 2025-3-28 07:29:45
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
发表于 2025-3-28 10:56:59
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)