恶梦
发表于 2025-3-21 17:29:18
书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0543932<br><br> <br><br>书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0543932<br><br> <br><br>书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0543932<br><br> <br><br>书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0543932<br><br> <br><br>书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0543932<br><br> <br><br>书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0543932<br><br> <br><br>书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0543932<br><br> <br><br>书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0543932<br><br> <br><br>书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0543932<br><br> <br><br>书目名称Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0543932<br><br> <br><br>
水槽
发表于 2025-3-21 23:00:57
Incorporating Complete Syntactical Knowledge for Spoken Language Understandingively attach a weight on each dependency arc based on dependency types and word contexts, which avoids encoding redundant features. Extensive experimental results show that our model outperforms strong baselines.
反应
发表于 2025-3-22 02:51:49
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INTER
发表于 2025-3-22 06:48:29
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ureter
发表于 2025-3-22 09:47:09
On Robustness and Bias Analysis of BERT-Based Relation Extractiontness by way of randomizations, adversarial and counterfactual tests, and biases (i.e., selection and semantic). These findings highlight opportunities for future improvements. Our open-sourced testbed . is available in ..
Antecedent
发表于 2025-3-22 15:34:23
KA-NER: Knowledge Augmented Named Entity Recognitioncomponents: a knowledge filtering module to filter domain-relevant entities and a knowledge fushion module to bridge the knowledge gap when incorporating knowledge into a NER model. Experimental results show that our model achieves significant improvements against baseline models on different domain datasets.
Intrepid
发表于 2025-3-22 17:02:08
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Critical
发表于 2025-3-23 00:30:52
Content-Based Open Knowledge Graph Search: A Preliminary Study with OpenKG.CNE supports keyword-based KG search, KG snippet generation, KG profiling and browsing, all computed over KGs’ (large) contents rather than their (small) metadata. We implement a prototype with Chinese KGs crawled from OpenKG.CN and we report some preliminary results about the practicability of such a system.
辫子带来帮助
发表于 2025-3-23 02:43:25
Conference proceedings 2021hina, in November 2021. .The 19 revised full papers and 9 short papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on knowledge extraction: knowledge graph representation and reasoning; knowledge acquisition and knowledge graph c
伸展
发表于 2025-3-23 05:42:01
Federated Knowledge Graph Embeddings with Heterogeneous Dataork FKE for representation learning of knowledge graphs to deal with the problem of privacy protection and heterogeneous data. Experiments show that the FKE can perform well in typical link prediction, overcome the problem of heterogeneous data and have a significant effect.