PrOQAW: Probabilistic Ontological Query Answering on the Web

作为Web关键技术之一的Web搜索的下一次革命刚刚开始，融合了语义网的思想，旨在通过以底层本体的形式为Web内容和查询添加含义，将当前的Web搜索转换为Web上的某种形式的语义搜索和查询回答。这还允许更复杂的查询，并通过组合分布在多个网页上的知识，即通过在Web上进行推理来评估查询。通过在当前Web上添加本体意义来在Web上实现这种语义搜索和查询回答，概念上意味着相对于该本体来标注网页及其内容，即，将网页及其内容与该本体相关联，从而也经由该本体。从实践的角度来看，最有希望实现这一点的方法之一是从当前Web中相对于底层本体进行数据提取，并将提取的数据存储在知识库中，并在该知识库上实现语义搜索和查询回答。最近在这个方向上有许多强有力的研究活动。在上述背景下，一个尚未解决的主要问题是不确定性的原则性处理：除了作为Web数据固有部分的自然不确定性之外，还必须处理自动处理Web数据所产生的不确定性。前者还包括由于信息不完整而产生的不确定性和在信息缺失和过度指定的情况下的不一致性。后者包括Web页面及其内容的自动标注、Web知识的自动提取、不同相关本体之间的匹配以及分布式Web数据源的集成等方面的不确定性。本研究的中心目标是为从Web中提取的知识库开发一系列相对于底层本体的概率数据模型，以及可扩展的查询回答算法，这些查询回答算法可以作为下一代Web语义搜索和查询回答技术的基础。我们相信，这样的概率数据模型和查询回答算法可以通过集成本体语言、数据库技术和形式化来管理Web上下文中的概率不确定性来开发。目标包括开发概率数据模型，开发排序和查询回答算法，识别有用的可伸缩片段，以及实际评估我们的结果。

The next revolution in Web search as one of the key technologies of the Web has just started with the incorporation of ideas from the Semantic Web, aiming at transforming current Web search into some form of semantic search and query answering on the Web, by adding meaning to Web contents and queries in the form of an underlying ontology. This also allows for more complex queries, and for evaluating queries by combining knowledge that is distributed over many Web pages, i.e., by reasoning over the Web. Realizing such semantic search and query answering on the Web by adding ontological meaning to the current Web conceptually means annotating Web pages and their contents relative to that ontology, i.e., relating Web pages and their contents to and thus also via that ontology. From a practical perspective, one of the most promising ways of realizing this is to perform data extraction from the current Web relative to the underlying ontology, store the extracted data in a knowledge base, and realize semantic search and query answering on this knowledge base. There are recently many strong research activities in this direction.A major unsolved problem in the above context is the principled handling of uncertainty: In addition to natural uncertainty as an inherent part of Web data, one also has to deal with uncertainty resulting from automatically processing Web data. The former also includes uncertainty due to incompleteness and inconsistency in the case of missing and over-specified information, respectively. The latter includes uncertainty due to, e.g., the automatic annotation of Web pages and their contents, the automatic extraction of knowledge from the Web, matching between different related ontologies, and the integration of distributed Web data sources.The central goal of the proposed research is to develop a family of probabilistic data models for knowledge bases extracted from the Web relative to an underlying ontology, along with scalable query answering algorithms, which may serve as the backbone for next-generation technologies for semantic search and query answering on the Web. We believe that such probabilistic data models and query answering algorithms can be developed by integrating ontology languages, database technologies, and formalisms for managing probabilistic uncertainty in the context of the Web. The objectives include developing probabilistic data models, developing algorithms for ranking and query answering, identifying useful scalable fragments, and practically evaluating our results.