Probabilistic Semantic Query Answering on the Web

For many people, the Web has started to play a fundamental role as a means of providing and searching for information and services. Searching the Web in its current form, however, is not always a joyful experience, since today’s search engines often are not capable of adequately responding to complex queries. Although the information is available, it is necessary to go through the cumbersome process of posing multiple simple queries and combining the answers in order to get the desired response. 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, which aims at transforming current Web search into some form of semantic search on the Web by adding meaning to Web contents and search queries, which also allows for more complex queries, whose evaluation involves reasoning over the Web. The goal of this project is to develop a family of probabilistic data models for knowledge bases extracted from the Web, along with scalable query answering algorithms, which may serve as the backbone for such next-generation technologies for semantic search and query answering on the Web.