Querying Probabilistic Ontologies with SPARQL

In recent years a lot of efforts was put into the field of Semantic Web research to specify knowledge as precisely as possible. However, optimizing for precision alone is not sufficient. The handling of uncertain or incomplete information is getting more and more important and it promises to significantly improve the quality of query answering in Semantic Web applications. My plan is to develop a framework that extends the rich semantics offered by ontologies with probabilistic information, stores this in a probabilistic database and provides query answering with the help of query rewriting. In this proposal I describe how these three aspects can be combined. Especially, I am focusing on how uncertainty is incorporated into the ABox and how it is handled by the database and the rewriter during query answering

Aggregated fuzzy answer set programming

Fuzzy Answer Set programming (FASP) is an extension of answer set programming (ASP), based on fuzzy logic. It allows to encode continuous optimization problems in the same concise manner as ASP allows to model combinatorial problems. As a result of its inherent continuity, rules in FASP may be satisfied or violated to certain degrees. Rather than insisting that all rules are fully satisfied, we may only require that they are satisfied partially, to the best extent possible. However, most approaches that feature partial rule satisfaction limit themselves to attaching predefined weights to rules, which is not sufficiently flexible for most real-life applications. In this paper, we develop an alternative, based on aggregator functions that specify which (combination of) rules are most important to satisfy. We extend upon previous work by allowing aggregator expressions to define partially ordered preferences, and by the use of a fixpoint semantics

A Probabilistic Data Model and Its Semantics

As database systems are increasingly being used in advanced applications, it is becoming common that data in these applications contain some elements of uncertainty. These arise from many factors, such as measurement errors and cognitive errors. As such, many researchers have focused on defining comprehensive uncertainty data models of uncertainty database systems. However, existing uncertainty data models do not adequately support some applications. Moreover, very few works address uncertainty tuple calculus. In this paper we advocate a probabilistic data model for representing uncertain information. In particular, we establish a probabilistic tuple calculus language and its semantics to meet the corresponding probabilistic relational algebra