Answer Set Programming: A Primer

Eiter, Thomas; Ianni, Giovambattista; Krennwallner, Thomas

doi:10.1007/978-3-642-03754-2_2

Cited by 138 publications

(81 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Q 2 Is it possible to take advantage of recent progress in the field of computational logic by adopting an Answer Set Programming (ASP) [2] framework for modeling and solving? Q 3 Would such a system be computationally feasible?…”

Section: Introductionmentioning

confidence: 99%

An Exercise in Declarative Modeling for Relational Query Mining

Paramonov

Leeuwen

Denecker

et al. 2016

Inductive Logic Programming

View full text Add to dashboard Cite

Abstract. Motivated by the declarative modeling paradigm for data mining, we report on our experience in modeling and solving relational query and graph mining problems with the IDP system, a variation of the answer set programming paradigm. Using IDP or other ASP languages for modeling appears to be natural given that they provide rich logical languages for modeling and solving many search problems and that relational query mining (and ILP) employs the same type of representation. Nevertheless, our results indicate that second order extensions to these languages are necessary for expressing the model as well as for efficient solving, especially for what concerns subsumption testing. We propose such second order extensions and evaluate their potential effectiveness with a number of experiments in subsumption as well as in query mining.

show abstract

Section: Introductionmentioning

confidence: 99%

An Exercise in Declarative Modeling for Relational Query Mining

Paramonov

Leeuwen

Denecker

et al. 2016

Inductive Logic Programming

View full text Add to dashboard Cite

show abstract

“…In the following we introduce ASP, thereby restricting ourselves to the syntax and semantics relevant in this work. For a more detailed introduction see, e.g., [12,14].…”

Section: Answer-set Programmingmentioning

confidence: 99%

“…What remains is to compute the number of votes that disagree on C 1 being better-ranked than C 2 (rule 11). In rule (12), the sum over all N in gwrankC is computed (Kemeny score) and minimized (Kemeny consensus). A candidate ranked first in a Kemeny consensus is a winner (rule 13).…”

Section: Asp Encodings For Voting Rulesmentioning

confidence: 99%

Democratix: A Declarative Approach to Winner Determination

Charwat

Pfandler

2015

Algorithmic Decision Theory

View full text Add to dashboard Cite

Computing the winners of an election is an important subtask in voting and preference aggregation. The declarative nature of answer-set programming (ASP) and the performance of state-of-the-art solvers render ASP very well-suited to tackle this problem. In this work we present a novel, reduction-based approach for a variety of voting rules, ranging from tractable cases to problems harder than NP. In addition, we discuss how encodings of voting rules can be optimized and combined in our approach. The encoded voting rules are put together in the extensible tool DEMOCRATIX, which handles the computation of the winners and is also available as a web application.To learn more about the capabilities and limits of the approach, the encodings are evaluated thoroughly on real-world data as well as on random instances.

show abstract

“…Formal semantics for ASP and further details are given in [9]. Let C, P, X be mutually disjoint sets whose elements are called constant, predicate, and variable symbols, respectively.…”

Section: Answer Set Programmingmentioning

confidence: 99%

“…An example of an answer set of program Π given in Example 1 would be {p(c), q(c)}. Finally, we recall the notion of cautious and brave entailment for ASPs [9].…”

Section:   mentioning

confidence: 99%

Minimizing Human Effort in Reconciling Match Networks

Nguyen

Wijaya

Miklós

et al. 2013

Conceptual Modeling

View full text Add to dashboard Cite

Abstract. Schema and ontology matching is a process of establishing correspondences between schema attributes and ontology concepts, for the purpose of data integration. Various commercial and academic tools have been developed to support this task. These tools provide impressive results on some datasets. However, as the matching is inherently uncertain, the developed heuristic techniques give rise to results that are not completely correct. In practice, post-matching human expert effort is needed to obtain a correct set of correspondences. We study this post-matching phase with the goal of reducing the costly human effort. We formally model this human-assisted phase and introduce a process of matching reconciliation that incrementally leads to identifying the correct correspondences. We achieve the goal of reducing the involved human effort by exploiting a network of schemas that are matched against each other. We express the fundamental matching constraints present in the network in a declarative formalism, Answer Set Programming that in turn enables to reason about necessary user input. We demonstrate empirically that our reasoning and heuristic techniques can indeed substantially reduce the necessary human involvement.

show abstract

Answer Set Programming: A Primer

Cited by 138 publications

References 87 publications

An Exercise in Declarative Modeling for Relational Query Mining

An Exercise in Declarative Modeling for Relational Query Mining

Democratix: A Declarative Approach to Winner Determination

Minimizing Human Effort in Reconciling Match Networks

Contact Info

Product

Resources

About