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K-CAP 2023: International Conference on Knowledge Capture

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截稿日期:
2023-08-27 延期
通知日期:
2023-09-25
会议日期:
2023-12-05
会议地点:
Pensacola, Florida, USA
届数:
12
ICORE: B   浏览: 18441   关注: 0   参加: 0
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55.8 / 100
全站第 806 名 / 共 5,695 个会议 · 前 15%
学术认可 (35%)
72
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
69
社区关注 (10%)
8
资料公开度 (15%)
40

用到的输入: 收录等级：ICORE B · 有据可查的届次：12 · 过去 24 个月打开过本页的研究者：2 人

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置信度 80% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-11

征稿

K-CAP 2023 (International Conference on Knowledge Capture) is a ICORE B conference held in Pensacola, Florida, USA on 2023-12-05. The paper submission deadline is 2023-08-27 (extended). Acceptance notifications are sent on 2023-09-25.

Knowledge has played a fundamental role since the inception of Artificial Intelligence. While the forms in which algorithms have leveraged knowledge have evolved over time, the need for efficient representations is ever more critical. Recent advances of AI, such as the stunning performance of large language models, have relied on the large amount of data available on the Web. There is growing agreement among researchers that it's important to look beyond the sheer volume of data, and instead also prioritize the development of methods that are accurate, precise, and efficient for capturing knowledge. The International Conference on Knowledge Capture, K-CAP, aims at bringing together an interdisciplinary group of researchers on a diverse set of topics with interest in the development of knowledge capture. This involves the design and development of formalisms, methods and tools that enable efficient and precise extraction and organization of knowledge from different sources and for different modalities of use including, for example, automated reasoning, machine learning and human-machine teaming. To enable a vibrant and constructive discussion on scalable and precise knowledge capture, K-CAP 2023, calls for the participation of researchers from diverse areas of Artificial Intelligence, including, but not limited to, knowledge representation and reasoning, knowledge acquisition, semantic web, intelligent user interfaces for knowledge acquisition and retrieval, query processing and question answering over heterogeneous knowledge bases, novel evaluation paradigms, problem-solving and reasoning, ethics and AI, explainability, neuro-symbolic AI, agents, information extraction from structured or unstructured data, machine learning and representation learning, information enrichment and visualization, as well as researchers interested in cyber-infrastructures to foster the publication, retrieval, reuse, and integration of data. Topics of interest Areas of interest for submissions to K-CAP 23 include, but are not limited to, the following topics: Knowledge representation Knowledge acquisition Ethical aspects related to knowledge capture and acquisition Knowledge capture for supporting explainability and, vice-versa, leveraging explainability approaches for knowledge capture Intelligent user interfaces for knowledge acquisition and retrieval Innovative query processing and question answering over heterogeneous knowledge bases Novel evaluation paradigms for knowledge capture Problem-solving and reasoning The role of knowledge and knowledge capture in neuro-symbolic AI Compact knowledge representation such as constraint networks and graphical models Knowledge capture in multi-agent systems Intersection of planning and knowledge capture Information extraction from text The role of metadata in knowledge capture processes Multi-modal knowledge capture from text, tables, images, video or sound Machine learning and representation learning Information enrichment and visualization The role of language models in knowledge graph construction and representation Techniques for extracting structured knowledge from large-scale language models Applications of deep learning to knowledge representation and reasoning, such as graph neural networks and graph convolutional networks Advancements in representation learning and deep learning for knowledge capture Utilizing deep learning for information extraction from structured and unstructured data to improve knowledge capture
由 Dou Sun 最后更新于

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