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Titlebook: Web and Big Data. APWeb-WAIM 2023 International Workshops; KGMA 2023 and SemiBD Xiangyu Song,Ruyi Feng,Geyong Min Conference proceedings 20

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樓主: Washington
41#
發(fā)表于 2025-3-28 16:44:54 | 只看該作者
Dong Li,Binghao Han,Ming Wan,Yuqian Gong,Yue Kou,Hairong Liaomb has drawn attention to the political changes that helped to create a context for stability, such as the passage of the Septennial Act of 1716 under which it was necessary to have a general election only every seven years. The Earl of Stair reported from Paris that, ‘the passing of this bill for c
42#
發(fā)表于 2025-3-28 20:50:11 | 只看該作者
43#
發(fā)表于 2025-3-28 23:26:18 | 只看該作者
44#
發(fā)表于 2025-3-29 04:29:43 | 只看該作者
45#
發(fā)表于 2025-3-29 08:17:38 | 只看該作者
Yuefeng Du,Chang Lin,Tingting Liu,Xiaoguang Li,Wei Wei,Shanshan Gaoposes. For biped walking robots with low torque actuators, a control method that enables biped walking with low torque is one of the most important problems. While many humanoids use high-performance motor systems to attain stable walking, such motor systems tend to be very expensive. Motors that ar
46#
發(fā)表于 2025-3-29 14:52:53 | 只看該作者
wever, it is not clear what is a building block in imitation learning and what is the interface of blocks; therefore, it is difficult to apply imitation learning in a constructive way. This paper addresses agents’ intentions as the building block that abstracts local situations of the agent and prop
47#
發(fā)表于 2025-3-29 15:33:20 | 只看該作者
48#
發(fā)表于 2025-3-29 23:47:19 | 只看該作者
49#
發(fā)表于 2025-3-30 00:00:14 | 只看該作者
A Bidirectional Question-Answering System using Large Language Models and Knowledge Graphscessing (NLP). However, existing approaches need help effectively harnessing the complementary strengths of LLMs and KGs. In this paper, we propose a novel system that addresses this gap by enabling bidirectional conversion between LLMs and KGs. We leverage external knowledge to enhance LLMs for dom
50#
發(fā)表于 2025-3-30 07:28:48 | 只看該作者
A Comprehensive Review of?Relation Prediction Techniques in?Knowledge Graphwledge graphs plays a crucial role, aiding inference, latent knowledge discovery, and revealing intricate associations between entities. We present an overview of this field’s development and methods. Initially, we introduce fundamental concepts, relation prediction task definitions, and evaluation
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