Zobrazeno 1 - 10
of 260
pro vyhledávání: '"Chen, Jiaoyan"'
Autor:
Pan, Jeff Z., Razniewski, Simon, Kalo, Jan-Christoph, Singhania, Sneha, Chen, Jiaoyan, Dietze, Stefan, Jabeen, Hajira, Omeliyanenko, Janna, Zhang, Wen, Lissandrini, Matteo, Biswas, Russa, de Melo, Gerard, Bonifati, Angela, Vakaj, Edlira, Dragoni, Mauro, Graux, Damien
Publikováno v:
Transactions on Graph Data and Knowledge, Vol 1, Iss 1, Pp 2:1-2:38 (2023)
Large Language Models (LLMs) have taken Knowledge Representation - and the world - by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and par
Externí odkaz:
https://doaj.org/article/49afd43227ff4798bd12c458ce0158d0
Autor:
Chen, Jiaoyan, Dong, Hang, Hastings, Janna, Jiménez-Ruiz, Ernesto, López, Vanessa, Monnin, Pierre, Pesquita, Catia, Škoda, Petr, Tamma, Valentina
Publikováno v:
Transactions on Graph Data and Knowledge, Vol 1, Iss 1, Pp 5:1-5:33 (2023)
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they pr
Externí odkaz:
https://doaj.org/article/4ff40f5f488842c7a6167ec1eaade3d0
Autor:
Geng, Yuxia, Zhu, Runkai, Chen, Jiaoyan, Chen, Jintai, Chen, Zhuo, Chen, Xiang, Xu, Can, Wang, Yuxiang, Xu, Xiaoliang
Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). However, due to the feature divergence of an attribute (resp. object) when combined with differe
Externí odkaz:
http://arxiv.org/abs/2408.09786
Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec4OA, an extension of the ontology embedding system OWL2Vec*. While OWL
Externí odkaz:
http://arxiv.org/abs/2408.06310
Autor:
Zhang, Wen, Jin, Long, Zhu, Yushan, Chen, Jiaoyan, Huang, Zhiwei, Wang, Junjie, Hua, Yin, Liang, Lei, Chen, Huajun
Natural language question answering (QA) over structured data sources such as tables and knowledge graphs (KGs) have been widely investigated, for example with Large Language Models (LLMs). The main solutions include question to formal query parsing
Externí odkaz:
http://arxiv.org/abs/2406.18916
Autor:
Zhang, Wen, Xu, Yajing, Ye, Peng, Huang, Zhiwei, Xu, Zezhong, Chen, Jiaoyan, Pan, Jeff Z., Chen, Huajun
Knowledge graph (KG) completion aims to find out missing triples in a KG. Some tasks, such as link prediction and instance completion, have been proposed for KG completion. They are triple-level tasks with some elements in a missing triple given to p
Externí odkaz:
http://arxiv.org/abs/2406.18166
Autor:
Chen, Jiaoyan, Mashkova, Olga, Zhapa-Camacho, Fernando, Hoehndorf, Robert, He, Yuan, Horrocks, Ian
Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many other domains. However, logical reasoning that ontologies can directl
Externí odkaz:
http://arxiv.org/abs/2406.10964
Event relation extraction (ERE) is a critical and fundamental challenge for natural language processing. Existing work mainly focuses on directly modeling the entire document, which cannot effectively handle long-range dependencies and information re
Externí odkaz:
http://arxiv.org/abs/2405.06890
Knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations on low-quality KGs that are prevalent in the real world. Some recent
Externí odkaz:
http://arxiv.org/abs/2405.10970
Autor:
Jin, Rihui, Li, Yu, Qi, Guilin, Hu, Nan, Li, Yuan-Fang, Chen, Jiaoyan, Wang, Jianan, Chen, Yongrui, Min, Dehai
Table understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures.To address these challenges, we propose HGT, a framework with a heteroge
Externí odkaz:
http://arxiv.org/abs/2403.19723