10 accepted papers
A fundamental use of knowledge bases (KBs) is query answering, i.e., retrieving the information entailed by the KB in response to a user query. When both the KB and the query are specified as logical formulae, the standard form of answer provided to users is the set of all certain answers (CAs): tup
More and more organizations are relying on Machine Learning (ML) models to support internal decision-making processes. To better support such processes, it would be highly beneficial to contextualize the inductively acquired knowledge encoded in these models and enable formal reasoning over it. Desp
Expressing negative conditions is a crucial feature of query languages for knowledge bases (KBs). Answering such queries over ontological KBs, however, is a very challenging task that becomes undecidable even for lightweight Description Logic (DL) ontologies. Such negative results hold even for Conj…
We propose a general framework for assessing the exposure of sensitive knowledge in policy-protected knowledge bases (KBs), where knowledge is represented as logical theories and data protection policies are defined declaratively using epistemic dependencies. The framework models scenarios in which
In this paper, we propose the use of epistemic dependencies to express data protection policies in Controlled Query Evaluation (CQE), which is a form of confidentiality-preserving query answering over ontologies and databases. The resulting policy language goes significantly beyond those proposed in…
Query answering for Knowledge Bases (KBs) amounts to extracting information from the various models of a KB, and presenting the user with an object that represents such information. In the vast majority of cases, this object consists of those tuples of constants that satisfy the query expression eit…
The Datalog query language can express several powerful recursive properties, often crucial in real-world scenarios. While answering such queries is feasible over relational databases, the picture changes dramatically when data is enriched with intensional knowledge. It is indeed well-known that ans…
This paper considers the problem of querying dirty databases, which may contain both erroneous facts and multiple names for the same entity. While both of these data quality issues have been widely studied in isolation, our contribution is a holistic framework for jointly deduplicating and repairing…
In Ontology-Based Data Management (OBDM), an abstraction of a source query q is a query over the ontology capturing the semantics of q in terms of the concepts and the relations available in the ontology. Since a perfect characterization of a source query may not exist, the notions of best sound and…
We study privacy-preserving query answering in Description Logics (DLs). Specifically, we consider the approach of controlled query evaluation (CQE) based on the notion of instance indistinguishability. We derive data complexity results for query answering over DL-LiteR ontologies, through a compari…