Workshop on Research Challenges and Opportunities in Knowledge Representation and Reasoning

知识表示和推理研究挑战与机遇研讨会

• 批准号: 1217638
• 负责人: Natasha Noy
• 金额: $ 5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217638&HistoricalAwards=false
• 关键词: Workshop; Research; Challenges; Opportunities; Knowledge

Modern information systems in every area of science and engineering rely critically on some form of knowledge representation and inference. Established fields such as Artificial Intelligence and Software Engineering as well as emerging fields such as Semantic Web, Computational Biology, Software Agents, Social Computing, Bioinformatics, Discovery Informatics, Cyberlearning and many others both rely on and contribute to advances in knowledge representation. Formal representations of knowledge and the associated inference mechanisms provide the basis for encoding, sharing, analyzing, interpreting vast amounts of data from disparate sources as well as deciding and acting upon information in virtually every area of human endeavor. This workshop aims to bring together scientists from all areas of knowledge-representation research and to discuss the new challenges and opportunities that this area faces in addressing the explosion of data and knowledge, increased reliance of scientists on computational data, its heterogeneity, and new modes of delivering, storing, and representing knowledge. This radical shift in the amount of data, in the way that scientists distribute, store, and aggregate this data, presents new challenges for knowledge representation (KR). KR researchers must consider the applicability of their methods for representation and reasoning on data and knowledge at unprecedented complexity, quantity, and heterogeneity. The distributed and open nature of the data-intensive science presents additional challenges and opportunities in KR, with regard to provenance, security, trustworthiness, and privacy. The increased adoption of Semantic Web technologies and the rapid increase in the amounts of structured knowledge that is becoming available on the Semantic Web presents additional challenges (e.g., need for coping with information with different degrees of reliability, information that holds in different contexts, information that changes over time, information that can be conflicting, information that represents beliefs and opinions, etc. The increased use of KR methods in Computer Vision, Robotics, Natural-Language Processing, Discovery Informatics, and others presents an opportunity for fruitful interdisciplinary collaborations that could dramatically alter the KR research landscape. As scientists, as well as laypersons get accustomed to social mechanisms for creating and sharing data and knowledge, it is incumbent upon the researchers in knowledge representation to develop KR mechanisms that support collaborative creation, sharing, and use of knowledge. Against this background, the workshop brings together a diverse group of researchers and practitioners in KR and related areas to identify new KR research challenges and opportunities presented by the recent developments in the world wide web, social networks and social media, collaborative and data-intensive e-science, among others.Broader Impacts: The workshop will identify new areas of research for the Information and the Intelligent Systems at the intersection Knowledge Representation and Inference, Information Integration and Informatics. Given the increasingly central role of formal representations of knowledge and associated tools for inference in virtually every domain of human endeavor, the identification of KR the results of the workshop are likely to impact multiple disciplines. The workshop results, including a report summarizing new KR research challenges and opportunities, as well as publications by workshop participants will be broadly disseminated to the larger scientific community.

现代信息系统在科学和工程的每个领域都严重依赖于某种形式的知识表示和推理。成熟的领域，如人工智能和软件工程，以及新兴领域，如语义网，计算生物学，软件代理，社会计算，生物信息学，发现信息学，网络学习和许多其他都依赖于知识表示的进步，并作出贡献。知识的形式化表示和相关的推理机制为编码、共享、分析、解释来自不同来源的大量数据以及在人类奋进的几乎每个领域中对信息进行决策和采取行动提供了基础。本次研讨会旨在汇集来自知识表征研究各个领域的科学家，并讨论该领域在解决数据和知识爆炸、科学家对计算数据的日益依赖、其异质性以及交付、存储和表示知识的新模式方面所面临的新挑战和机遇。数据量的这种根本性变化，以及科学家分发、存储和聚合这些数据的方式，为知识表示（KR）提出了新的挑战。KR研究人员必须考虑他们的方法在前所未有的复杂性，数量和异质性的数据和知识的表示和推理的适用性。数据密集型科学的分布式和开放性在来源、安全性、可信度和隐私方面为KR带来了额外的挑战和机遇。语义Web技术的日益采用和语义Web上可用的结构化知识量的快速增加提出了额外的挑战（例如，需要处理具有不同可靠性程度的信息、在不同上下文中保持的信息、随时间变化的信息、可能冲突的信息、代表信念和观点的信息等。KR方法在计算机视觉、机器人技术、自然语言处理、发现信息学、和其他人提出了一个富有成效的跨学科合作的机会，可以大大改变KR的研究景观。随着科学家和外行习惯于创建和共享数据和知识的社会机制，知识表示的研究人员有责任开发支持协作创建，共享和使用知识的KR机制。 在此背景下，本次研讨会汇集了知识产权及相关领域的各类研究人员和从业人员，以确定万维网、社交网络和社交媒体、协作和数据密集型电子科学等领域的最新发展所带来的新知识产权研究挑战和机遇。研讨会将确定新的研究领域的信息和智能系统在交叉知识表示和推理，信息集成和信息学。鉴于知识的正式表示和相关的推理工具在人类奋进的几乎每个领域中的作用越来越重要，KR的识别研讨会的结果可能会影响多个学科。讲习班的成果，包括一份总结KR研究新挑战和机遇的报告，以及讲习班与会者的出版物，将广泛传播给广大科学界。