Temporal Minimal-World Semantics for Sparse ABoxes

TL;DR

This paper introduces a new minimal-world semantics for temporal and non-temporal ontology query answering with negation, enabling efficient polynomial-time reasoning over sparse, temporal medical data in description logics.

Contribution

It proposes a novel closed-world semantics for conjunctive query answering with negation in ELHb and extends it to temporal logic TELHb, achieving polynomial data complexity.

Findings

Polynomial-time query answering with negation in ELHb.

Extension of semantics to temporal logic TELHb.

Efficient reasoning over sparse, temporal medical data.

Abstract

Ontology-mediated query answering is a popular paradigm for enriching answers to user queries with background knowledge. For querying the absence of information, however, there exist only few ontology-based approaches. Moreover, these proposals conflate the closed-domain and closed-world assumption, and therefore are not suited to deal with the anonymous objects that are common in ontological reasoning. Many real-world applications, like processing electronic health records (EHRs), also contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. Our contribution consists of two main parts: In the first part we introduce a new closed-world semantics for answering conjunctive queries with negation over ontologies formulated in the description logic ELHb, which is based on the minimal canonical model. We propose a rewriting strategy for dealing with negated query atoms, which shows that query answering is possible in polynomial time in data complexity. In the second part, we extend this minimal-world semantics for answering metric temporal conjunctive queries with negation over the lightweight temporal logic TELHb and obtain similar rewritability and complexity results. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).