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.
Selected Publications
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Tag−Aware Personalized Recommendation Using a Deep−Semantic Similarity Model with Negative Sampling
Zhenghua Xu‚ Cheng Chen‚ Thomas Lukasiewicz‚ Yishu Miao and Xiangwu Meng
In Elisa Bertino‚ Fabio Crestani‚ Javed Mostafa‚ Jie Tang‚ Luo Si and Xiaofang Zhou, editors, Proceedings of the 25th ACM International Conference on Information and Knowledge Management‚ CIKM 2016‚ Indianapolis‚ USA‚ October 24−28‚ 2016. Pages 1921−1924. ACM Press. October, 2016.
Details about Tag−Aware Personalized Recommendation Using a Deep−Semantic Similarity Model with Negative Sampling | BibTeX data for Tag−Aware Personalized Recommendation Using a Deep−Semantic Similarity Model with Negative Sampling | Link to Tag−Aware Personalized Recommendation Using a Deep−Semantic Similarity Model with Negative Sampling
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Information Integration with Provenance on the Semantic Web via Probabilistic Datalog+⁄−
Thomas Lukasiewicz and Livia Predoiu
In Fernando Bobillo‚ Rommel N. Carvalho‚ Paulo Cesar G. da Costa‚ Claudia d'Amato‚ Nicola Fanizzi‚ Kathryn B. Laskey‚ Kenneth J. Laskey‚ Thomas Lukasiewicz‚ Trevor Martin‚ Matthias Nickles and Michael Pool, editors, Proceedings of the 9th International Workshop on Uncertainty Reasoning for the Semantic Web‚ URSW 2013‚ Sydney‚ Australia‚ October 21‚ 2013. Vol. 1073 of CEUR Workshop Proceedings. Pages 3−14. CEUR−WS.org. 2013.
Details about Information Integration with Provenance on the Semantic Web via Probabilistic Datalog+⁄− | BibTeX data for Information Integration with Provenance on the Semantic Web via Probabilistic Datalog+⁄− | Download (pdf) of Information Integration with Provenance on the Semantic Web via Probabilistic Datalog+⁄−
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Query Answering in the Semantic Social Web: An Argumentation−Based Approach
Maria Vanina Martinez and Sebastián Gottifredi
In Encyclopedia of Social Network Analysis and Mining (ESNAM). Pages 1441−1455. Springer. 2014.
Details about Query Answering in the Semantic Social Web: An Argumentation−Based Approach | BibTeX data for Query Answering in the Semantic Social Web: An Argumentation−Based Approach
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