PrOQAW: Probabilistic Ontological Query Answering on the Web
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.
Complexity Results for Preference Aggregation over (m)CP−nets: Pareto and Majority Voting
Thomas Lukasiewicz and Enrico Malizia
In Artificial Intelligence. 2018.
Lightweight Tag−Aware Personalized Recommendation on the Social Web Using Ontological Similarity
Zhenghua Xu‚ Oana Tifrea−Marciuska‚ Thomas Lukasiewicz‚ Maria Vanina Martinez‚ Gerardo I. Simari and Cheng Chen
In IEEE Access. Vol. 6. No. 1. Pages 35590−35610. July, 2018.
Complexity of Approximate Query Answering under Inconsistency in Datalog+⁄−
Thomas Lukasiewicz‚ Enrico Malizia and Cristian Molinaro
In Jérôme Lang, editor, Proceedings of the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence‚ IJCAI−ECAI 2018‚ Stockholm‚ Sweden‚ July 13−19‚ 2018. Pages 1921−1927. IJCAI/AAAI Press. July, 2018.