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，结合了语义Web的思想，旨在通过以底层本体的形式为Web内容和查询添加意义，将当前的Web搜索转换为某种形式的语义搜索和Web上的查询应答。这还允许更复杂的查询，并且允许通过组合分布在许多网页上的知识来评估查询，即，通过网络推理。通过向当前Web添加本体意义来实现Web上的这种语义搜索和查询应答在概念上意味着注释Web页面及其与该本体相关的内容，即，将网页及其内容与该本体关联，从而也通过该本体关联。从实践的角度来看，实现这一点的最有前途的方法之一是执行数据提取从当前的Web相对于底层本体，存储提取的数据在一个知识库，并实现语义搜索和查询回答这个知识库。在此背景下，一个尚未解决的主要问题是如何处理不确定性：除了Web数据固有的自然不确定性外，还必须处理Web数据自动处理过程中产生的不确定性。前者还包括分别在缺失和详细说明过多的情况下因信息不完整和不一致而产生的不确定性。后者包括不确定性，例如，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数据源的集成等问题，提出了一种基于本体的Web知识库的概率数据模型，沿着可扩展的查询应答算法，它可以作为下一代Web语义搜索和查询应答技术的主干。我们相信，这样的概率数据模型和查询回答算法可以开发集成本体语言，数据库技术，和形式化管理概率不确定性的背景下，在Web上。目标包括开发概率数据模型，开发算法的排名和查询回答，识别有用的可扩展的片段，并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.