III: Small: Statistical Knowledge Translation and Knowledge Integration Using Markov Logic

III：小：使用马尔可夫逻辑进行统计知识翻译和知识整合

• 批准号: 1118050
• 负责人: Dejing Dou
• 金额: $ 49.47万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118050&HistoricalAwards=false
• 关键词: III; Small; Statistical; Knowledge; Translation

With the rapid proliferation of knowledge bases and their use in automated inference in a variety of applications, there is a growing need for effective approaches for (i) knowledge translation, i.e., the task of applying knowledge learned or developed in one domain to another semantically different domain, and (ii) knowledge integration, i.e., the task of building a unified knowledge base from disparate sources. While the problem of data translation and integration has received a great deal of attention in the literature, the problem of knowledge integration remains relatively under-explored. Existing work on this topic has focused on primarily logical frameworks which make it difficult to take into account uncertainty in knowledge and in semantic mappings used for knowledge translation and integration. This project aims to use Markov Logic Networks to express both knowledge and semantic mappings to obtain a unified probabilistic model for jointly translating knowledge and refining semantic mappings. This provides a basis for addressing the challenges of integrating heterogeneous knowledge with uncertain mappings, assessing the correctness of translated knowledge, simplifying the resulting models to obtain more compact approximate translations, and evaluating the methods in realistic application scenarios. The results of the proposed work are likely to be applicable in a number of application domains that require integration or translation of knowledge across disparate knowledge sources. The work strengthens and facilitates interdisciplinary collaborations, provides enhanced research-based training opportunities for graduate and undergraduate students. The knowledge translation and integration software, and the benchmark data will be made available to the broader community through the project web site: http://aimlab.cs.uoregon.edu/SKTI/

随着知识库的快速增长以及它们在各种应用中的自动推理中的使用，越来越需要用于（i）知识翻译，即，将在一个领域中学习或开发的知识应用到另一个语义上不同的领域的任务，以及（ii）知识集成，即，从不同来源建立统一的知识库的任务。虽然数据翻译和集成的问题在文献中得到了大量的关注，但知识集成的问题仍然相对不足。关于这一主题的现有工作主要集中在逻辑框架上，这使得很难考虑到知识和用于知识翻译和集成的语义映射中的不确定性。本计画旨在利用马尔可夫逻辑网路来表达知识与语意的映射，以获得一个统一的机率模型来共同翻译知识与提炼语意的映射。这提供了一个基础，以解决集成异构知识与不确定映射的挑战，评估翻译知识的正确性，简化所产生的模型，以获得更紧凑的近似翻译，并在现实的应用场景中评估的方法。拟议工作的结果可能适用于一些应用领域，需要整合或翻译的知识在不同的知识来源。这项工作加强和促进跨学科合作，为研究生和本科生提供了更好的研究培训机会。知识转换和整合软件以及基准数据将通过项目网站http://aimlab.cs.uoregon.edu/SKTI/提供给更广泛的社区。