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EKAW 2026: International Conference on Knowledge Engineering and Knowledge Management

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截稿日期:
2026-05-15 Extended
通知日期:
2026-07-10
会议日期:
2026-09-29
会议地点:
Turin, Italy
届数:
25
ICORE: B   浏览: 1035   关注: 0   参加: 0

会伴指数 (CP-I)

59.0 / 100
全站第 634 名 / 共 5,683 个会议 · 前 12%

信息系统与 Web 第 28 / 253 人工智能与机器学习 第 52 / 740

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%)
88
社区关注 (10%)
10
资料公开度 (15%)
35

用到的输入: 收录等级：ICORE B · 有据可查的届次：25 · 过去 24 个月打开过本页的研究者：3 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

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 2026-09-21

征稿

EKAW 2026 (International Conference on Knowledge Engineering and Knowledge Management) is a ICORE B conference held in Turin, Italy on 2026-09-29. The paper submission deadline is 2026-05-15 (extended). Acceptance notifications are sent on 2026-07-10.

The 25th International Conference on Knowledge Engineering and Knowledge Management (EKAW-26) encompasses the diverse realms of eliciting, acquiring, modeling, and managing knowledge in a variety of information objects ranging from taxonomies, to ontologies and knowledge graphs. The conference addresses the pivotal role of knowledge in constructing systems and services for the semantic web, knowledge management, knowledge discovery, information integration, natural language processing, intelligent systems, AI systems in e-business, e-health, humanities, cultural heritage, sustainability and beyond. This year’s special theme is investigating “New Frontiers in Knowledge Engineering”. Indeed, the current AI technological landscape is marked by major new trends and technologies emerging at an unprecedented pace. Generative AI systems, neuro-symbolic AI, agentic AI, AI regulations are just a few of the ground-breaking, ongoing trends. In such a setting, it is natural for each community to embark in a “soul-searching” and strategic positioning activity: What is our role in AI? What are major current and long-term developments in the field? What are new challenges and opportunities brought about by this context? In this year’s EKAW, we invite the community to reflect on how this extraordinary backdrop could affect current ways to engineer and manage knowledge, including: what are limitations of generative AI systems and how can knowledge engineering be used to alleviate those? What are novel requirements for “high-quality” knowledge in neuro-symbolic architectures? What are emerging neuro-symbolic system patterns for performing knowledge engineering? All submissions, including those related to the technologies mentioned on the special theme, should establish a clear connection to Knowledge Engineering and Knowledge Management or demonstrate a significant impact on the field. While acknowledging the interdisciplinary nature of knowledge and its interplay with other disciplines and technologies, such as Machine Learning, Natural Language Processing, and Computer Vision, contributions lacking direct relevance to Knowledge Engineering and Knowledge Management will not be considered pertinent to the EKAW conference. Topics of interest Knowledge Engineering Methods, techniques, and tools for knowledge engineering Evaluation methods and metrics for ensuring knowledge quality Collaborative knowledge engineering Ontology mapping and alignment Ontology design patterns Multimodal knowledge engineering Methods for benchmarking/comparing Language Models for KE tasks Uncertainty and vagueness in knowledge representation Dealing with dynamic, distributed and emerging knowledge Neuro-symbolic, GenAI and AI agent-based methodologies and architectures for knowledge engineering Engineering of complex types of knowledge (e.g., causality, workflows, procedures) (Ontological) knowledge memorization in LMs Translating between explicitly represented (symbolic) knowledge and knowledge captured in machine learning models (parametric knowledge) or embeddings Knowledge Management and Governance Methods, techniques, and tools for knowledge management and governance Knowledge evolution, maintenance, and preservation Knowledge sharing and distribution Methods for accelerating take-up of knowledge management technologies Question answering over knowledge graphs via LMs Robust and scalable knowledge management Conversational AI and dialogue systems for knowledge management Ethical and Trustworthy KE Ethics, trust, and privacy in knowledge representation and reasoning Explainable AI Provenance, trust, and transparency in knowledge management FAIR data and FAIR knowledge Inclusivity and diversity in knowledge representation Ontologies for trust and ethics Policies for ownership, management and usage of knowledge Social and Cognitive Aspects of KE Knowledge representation inspired by cognitive science Synergies between humans and machines Knowledge emerging from user interaction and (social) networks Knowledge ecosystems Collaborative and social approaches to knowledge management and acquisition Hybrid Humani-AI approaches to KE Knowledge Discovery and Acquisition Data and text mining for knowledge construction Classification and clustering for knowledge management Mining patterns and association rules Formal Concept Analysis and extensions Neuro-symbolic, GenAI and AI agent-based methodologies and architectures for knowledge discovery and acquisition Knowledge graph extension, link prediction Domain-specific Applications eGovernment and public administration Life sciences, health, and medicine Humanities and Social Sciences Cultural Heritage, Media and Digital Libraries ICT4D (Knowledge in the developing world) Manufacturing and automotive industry (Industry 4.0/5.0)
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