On decidability and tractability of querying in temporal EL

We study access to temporal data with TEL, a temporal extension of the tractable description logic EL. Our aim is to establish a clear computational complexity landscape for the atomic query answering problem, in terms of both data and combined complexity. Atomic queries in full TEL turn out to be undecidable even in data complexity. Motivated by the negative result, we identify well-behaved yet expressive fragments of TEL. Our main contributions are a semantic and sufficient syntactic conditions for decidability and three orthogonal tractable fragments, which are based on restricted use of rigid roles, temporal operators, and novel acyclicity conditions on the ontologies

Conjunctive queries with negation over DL-Lite: A closer look

While conjunctive query (CQ) answering over DL-Lite has been studied extensively, there have been few attempts to analyse CQs with negated atoms. This paper deepens the study of the problem. Answering CQs with safe negation and CQs with a single inequality over DL-Lite with role inclusions is shown to be undecidable, even for a fixed TBox and query.Without role inclusions, answering CQs with one inequality is P-hard and with two inequalities coNP-hard in data complexity

Two-dimensional description logics for context-based semantic interoperability

Description Logics (DLs) provide a clear and broadly accepted paradigm for modeling and reasoning about terminological knowledge. However, it has been often noted, that although DLs are well-suited for representing a single, global viewpoint on an application domain, they offer no formal grounding for dealing with knowledge pertaining to multiple heterogeneous viewpoints — a scenario ever more often approached in practical applications, e.g. concerned with reasoning over distributed knowledge sources on the Semantic Web. In this paper, we study a natural extension of DLs, in the style of two-dimensional modal logics, which supports declarative modeling of viewpoints as contexts, in the sense of McCarthy, and their semantic interoperability. The formalism is based on two-dimensional semantics, where one dimension represents a usual object domain and the other a (possibly infinite) domain of viewpoints, addressed by additional modal operators and a metalanguage, on the syntactic level. We systematically introduce a number of expressive fragments of the proposed logic, study their computational complexity and connections to related formalisms

ALC_ALC: a Context Description Logic

We develop a novel description logic (DL) for representing and reasoning with contextual knowledge. Our approach descends from McCarthy’s tradition of treating contexts as formal objects over which one can quantify and express first-order properties. As a foundation we consider several common product-like combinations of DLs with multimodal logics and adopt the prominent (Kn)ALC. We then extend it with a second sort of vocabulary for describing contexts, i.e., objects of the second dimension. In this way, we obtain a two-sorted, two-dimensional combination of a pair of DLs ALC, called ALCALC. As our main technical result, we show that the satisfiability problem in this logic, as well as in its proper fragment (Kn)ALC with global TBoxes and local roles, is 2ExpTime- complete. Hence, the surprising conclusion is that the significant increase in the expressiveness of ALCALC due to adding the vocabulary comes for no substantial price in terms of its worst-case complexity

Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System

With recent viruses across the world affecting millions and millions of people, the self-healthcare information systems show an important role in helping individuals to understand the risks, self-assessment, and self-educating to avoid being affected. In addition, self-healthcare information systems can perform more interactive tasks to effectively assist the treatment process and health condition management. Currently, the technologies used in such kind of systems are mostly based on text crawling from website resources such as text-searching and blog-based crowdsourcing applications. In this research paper, we introduce a novel Artificial Intelligence (AI) framework to support interactive and causality reasoning for a Chatbot application. The Chatbot will interact with the user to provide self-healthcare education and self-assessment (condition prediction). The framework is a combination of Natural Language Processing (NLP) and Knowledge Graph (KG) technologies with added causality and probability (uncertainty) properties to original Description Logic. This novel framework can generate causal knowledge probability neural networks to perform question answering and condition prediction tasks. The experimental results from a prototype showed strong positive feedback. The paper also identified remaining limitations and future research directions