Cristhian A. D. Deagustini

Argument-based mixed recommenders and their application to movie suggestion

Recommender systems have become prevalent in recent years as they help users to access relevant items from the vast universe of possibilities available these days. Most existing research in this area is based purely on quantitative aspects such as indices of popularity or measures of similarity between items or users. This work introduces a novel perspective on movie recommendation that combines a basic quantitative method with a qualitative approach, resulting in a family of mixed character recommender systems. The proposed framework incorporates the use of arguments in favor or against recommendations to determine if a suggestion should be presented or not to a user. In order to accomplish this, Defeasible Logic Programming (DeLP) is adopted as the underlying formalism to model facts and rules about the recommendation domain and to compute the argumentation process. This approach has a number features that could be proven useful in recommendation settings. In particular, recommendations can account for several different aspects (e.g., the cast, the genre or the rating of a movie), considering them all together through a dialectical analysis. Moreover, the approach can stem for both content-based or collaborative filtering techniques, or mix them in any arbitrary way. Most importantly, explanations supporting each recommendation can be provided in a way that can be easily understood by the user, by means of the computed arguments. In this work the proposed approach is evaluated obtaining very positive results. This suggests a great opportunity to exploit the benefits of transparent explanations and justifications in recommendations, sometimes unrealized by quantitative methods.

Relational databases as a massive information source for defeasible argumentation

Argumentation provides a sophisticated yet powerful mechanism for the formalization of commonsense reasoning in knowledge-based systems, with application in many areas of Artificial Intelligence. Nowadays, most argumentation systems build their arguments on the basis of a single, fixed knowledge base, often under the form of a logic program as in Defeasible Logic Programming or in Assumption-Based Argumentation. Currently, adding new information to such programs requires a manual encoding, which is not feasible for many real-world environments which involve large amounts of data, usually conceptualized as relational databases. This paper presents a novel approach to compute arguments from premises obtained from relational databases, identifying several relevant aspects. In our setting, different databases can be updated by external, independent applications, leading to changes in the spectrum of available arguments. We present algorithms for integrating a database management system with an argument-based inference engine. Empirical results and running-time analysis associated with our approach show that it provides a powerful alternative for efficiently achieving massive argumentation, taking advantage of modern DBMS technologies. We contend that our proposal is significant for developing new architectures for knowledge-based applications, such as Decision Support Systems and Recommender Systems, using argumentation as the underlying inference model.

Inconsistency-Tolerant Reasoning in Datalog ± Ontologies via an Argumentative Semantics

The Semantic Web provides an effective infrastructure that allows data to be easily shared and reused across applications. At its core is the description of ontological knowledge using ontological languages which are powerful knowledge representation tools with good decidability and tractability properties; Datalog\(^{\pm }\) is one of these tools.The problem of inconsistency has been acknowledged in both the Semantic Web and Database Theory communities. Here we introduce elements of defeasible argumentative reasoning in Datalog\(^{\pm }\), consequences to represent statements whose truth can be challenged leading to a better handling of inconsistency in ontological languages.

Consistent Query Answering Using Relational Databases through Argumentation

This paper introduces a framework that integrates a reasoner based on defeasible argumentation with a large information repository backed by one or several relational databases. In our scenario, we assume that the databases involved are updated by external independent applications, possibly introducing inconsistencies in a particular database, or leading to inconsistency among the subset of databases that refer to the same data. Argumentation reasoning will contribute with the possibility of obtaining consistent answers from the information repository with the properties described. We present the Database Integration for Defeasible Logic Programming (DBI-DeLP) framework, which enables commonsense reasoning based on Defeasible Logic Programming (DeLP) by extending the system capabilities to handle large amounts of data and providing consistent answers for queries posed to it.

Improving Inconsistency Resolution by Considering Global Conflicts

Over the years, inconsistency management has caught the attention of researchers of different areas. Inconsistency is a problem that arises in many different scenarios, for instance, ontology development or knowledge integration. In such settings, it is important to have adequate automatic tools for handling potential conflicts. Here we propose a novel approach to belief base consolidation based on a refinement of kernel contraction that accounts for the relation among kernels using clusters. We define cluster contraction based consolidation operators as the contraction by falsum on a belief base using cluster incision functions, a refinement of smooth kernel incision functions. A cluster contraction-based approach to belief bases consolidation can successfully obtain a belief base satisfying the expected consistency requirement. Also, we show that the application of cluster contraction-based consolidation operators satisfy minimality regarding loss of information and are equivalent to operators based on maxichoice contraction.

Inconsistency resolution and global conflicts

Over the years, inconsistency management has caught the attention of researchers of different areas. Inconsistency is a problem that arises in many different scenarios, for instance, ontology development or knowledge integration. In such settings, it is important to have adequate automatic tools for handling conflicts that may appear in a knowledge base. We introduce an approach to consolidation of belief bases based on a refinement of kernel contraction that accounts for the relation among kernels using clusters instead. We define cluster contraction-based consolidation operators contraction by falsum on a belief base using cluster incision functions, a refinement of kernel incision functions.

Datalog± ontology consolidation

Knowledge bases in the form of ontologies are receiving increasing attention as they allow to clearly represent both the available knowledge, which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalog± ontologies are attractive because of their property of decidability and the possibility of dealing with the massive amounts of data in real world environments; however, as it is the case with many other ontological languages, their application in collaborative environments often lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog± ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog± ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog± ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog± ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.

How does incoherence affect inconsistency-tolerant semantics for Datalog±?

The concept of incoherence naturally arises in ontological settings, specially when integrating knowledge. In the Datalog± literature, however, this is an issue that is yet to be studied more deeply. The main focus of our work is to show how classical inconsistency-tolerant semantics for query answering behaves when dealing with atoms that are relevant to unsatisfiable sets of existential rules, which may hamper the quality of answers and any reasoning task based on those semantics. We also propose a notion of incoherency-tolerant semantics for query answering in Datalog±, and exemplify this notion with a particular semantics based on the transformation of classic Datalog± ontologies into defeasible Datalog± ones, which use argumentation as its reasoning machinery.

Defeasible argumentation over relational databases

Fil: Deagustini, Cristhian Ariel David. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Bahia Blanca. Instituto de Ciencias e Ingenieria de la Computacion. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Instituto de Ciencias e Ingenieria de la Computacion; Argentina. Universidad Nacional de Entre Rios. Facultad de Ciencias Economicas; Argentina