scholarly journals DatalogMTL: Computational Complexity and Expressive Power

2019 ◽  
Author(s):  
Przemysław A. Wałęga ◽  
Bernardo Cuenca Grau ◽  
Mark Kaminski ◽  
Egor V. Kostylev
Keyword(s):  
Computational Complexity ◽  
Knowledge Representation ◽  
Data Access ◽  
Expressive Power ◽  
Data Complexity ◽  
Complexity Bounds ◽  
Metric Temporal Logic ◽  
Stream Reasoning ◽  
Knowledge Representation Language ◽  
Representation Language

We study the complexity and expressive power of DatalogMTL - a knowledge representation language that extends Datalog with operators from metric temporal logic (MTL) and which has found applications in ontology-based data access and stream reasoning. We establish tight PSpace data complexity bounds and also show that DatalogMTL extended with negation on input predicates can express all queries in PSpace; this implies that MTL operators add significant expressive power to Datalog. Furthermore, we provide tight combined complexity bounds for the forward-propagating fragment of DatalogMTL, which was proposed in the context of stream reasoning, and show that it is possible to express all PSpace queries in the fragment extended with the falsum predicate.

scholarly journals Reasoning with Contextual Knowledge and Influence Diagrams

2020 ◽  
Author(s):  
Erman Acar ◽  
Rafael Peñaloza
Keyword(s):  
Computational Complexity ◽  
Knowledge Representation ◽  
Bayesian Networks ◽  
Domain Knowledge ◽  
Description Logic ◽  
Influence Diagrams ◽  
Light Weight ◽  
Contextual Knowledge ◽  
Knowledge Representation Language ◽  
Representation Language

Influence diagrams (IDs) are well-known formalisms, which extend Bayesian networks to model decision situations under uncertainty. Although they are convenient as a decision theoretic tool, their knowledge representation ability is limited in capturing other crucial notions such as logical consistency. In this article, we complement IDs with the light-weight description logic (DL) EL to overcome such limitations. We consider a setup where DL axioms hold in some contexts, yet the actual context is uncertain. The framework benefits from the convenience of using DL as a domain knowledge representation language and the modelling strength of IDs to deal with decisions over contexts in the presence of contextual uncertainty. We define related reasoning problems and study their computational complexity.

scholarly journals NKRL, a knowledge representation tool for encoding the ‘meaning’ of complex narrative texts

1997 ◽  
Vol 3 (2) ◽  
pp. 231-253 ◽  
Author(s):  
GIAN PIERO ZARRI
Keyword(s):  
Knowledge Representation ◽  
Data Structures ◽  
Semantic Content ◽  
Narrative Texts ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Original Narrative

In this paper, we describe NKRL (Narrative Knowledge Representation Language), a language designed for representing, in a standardized way, the semantic content (the ‘meaning’) of complex narrative texts. After having introduced informally the four ‘components’ (specialized sub-languages) of NKRL, we will describe (some of) the data structures proper to each of them, trying to show that the NKRL coding retains the main informational elements of the original narrative expressions. We will then focus on an important subset of NKRL, the so-called AECS sub-language, showing in particular that the operators of this sub-language can be used to represent some sorts of ‘plural’ expressions.

scholarly journals Vicious Circle Principle and Logic Programs with Aggregates

2014 ◽  
Vol 14 (4-5) ◽  
pp. 587-601 ◽  
Author(s):  
MICHAEL GELFOND ◽  
YUANLIN ZHANG
Keyword(s):  
Knowledge Representation ◽  
Vicious Circle ◽  
Logic Programs ◽  
Vicious Circle Principle ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Answer Sets ◽  
Mathematical Semantics

AbstractThe paper presents a knowledge representation language $\mathcal{A}log$ which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison with other approaches.

scholarly journals The Seventh Answer Set Programming Competition: Design and Results

2019 ◽  
Vol 20 (2) ◽  
pp. 176-204 ◽  
Author(s):  
MARTIN GEBSER ◽  
MARCO MARATEA ◽  
FRANCESCO RICCA
Keyword(s):  
Knowledge Representation ◽  
Logic Programming ◽  
State Of The Art ◽  
Answer Set Programming ◽  
The State ◽  
International Conference ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
The University ◽  
Answer Set

AbstractAnswer Set Programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial ASP competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report on the design and results of the Seventh ASP Competition, jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2017).

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