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We present a novel argumentation-based method for finding and analyzing communities in social media on the Web, where a community is regarded as a set of supported opinions that might be in conflict. Based on their stance, we identify argumentative coalitions to define them; then, we apply a similarity-based evaluation method over the set of arguments in the coalition to determine the level of cohesion inherent to each community, classifying them appropriately. Introducing conflict points and attacks between coalitions based on argumentative (dis)similarities to model the interaction between communities leads to considering a meta-argumentation framework where the set of coalitions plays the role of the set of arguments and where the attack relation between the coalitions is assigned a particular strength which is inherited from the arguments belonging to the coalition. Various semantics are introduced to consider attacks’ strength to particularize the effect of the new perspective. Finally, we analyze a case study where all the elements of the formal construction of the formalism are exercised.

Querying inconsistent knowledge bases is an intriguing problem that gave rise to a flourishing research activity in the knowledge representation and reasoning community during the last years. It has been extensively studied in the context of description logics (DLs), and its computational complexity is rather well-understood. Although DLs are popular formalisms for modeling ontologies, it is generally agreed that rule-based ontologies are well-suited for data-intensive applications, since they allow us to conveniently deal with higher-arity relations, which naturally occur in standard relational databases. The goal of this work is to perform an in-depth complexity analysis of querying inconsistent knowledge bases in the case of the main decidable classes of existential rules, based on the notions of guardedness, linearity, acyclicity, and stickiness, enriched with negative (a.k.a. denial) constraints. Our investigation concentrates on three central inconsistency-tolerant semantics: the ABox repair (AR) semantics, considered as the standard one, and its main sound approximations, the intersection of repairs (IAR) semantics and the intersection of closed repairs (ICR) semantics.

The problem of managing spatio-temporal data arises in many applications, such as location-based services, environmental monitoring, geographic information systems, and many others. Often spatio-temporal data arising from such applications turn out to be inconsistent, i.e., representing an impossible situation in the real world. Though several inconsistency measures have been proposed to quantify in a principled way inconsistency in propositional knowledge bases, little effort has been done so far on inconsistency measures tailored for the spatio-temporal setting. In this paper, we define and investigate new measures that are particularly suitable for dealing with inconsistent spatio-temporal information, because they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions and thus define “dimension-aware” counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.

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. Fil: Deagustini, Cristhian Ariel David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias de la Administración; Argentina Fil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias de la Administración; Argentina Fil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina

The Web has recently been changing more and more to what is called the Social Semantic Web. As a consequence, the ranking of search results no longer depends solely on the structure of the interconnections among Web pages. In this paper, we argue that such rankings can be based on user preferences from the Social Web and on ontological background knowledge from the Semantic Web. We propose an approach to top-k query answering under user preferences in Datalog+/– ontologies, where the queries are unions of conjunctive queries with safe negation, and the preferences are defined via numerical values. To this end, we also generalize the previous RankJoin algorithm to our framework. Furthermore, we explore the generalization to the preferences of a group of users. Finally, we provide experimental results on the performance and quality of our algorithms. Fil: Betinna Fazzinga. National Research Council. ICAR-CNR; Italia Fil: Thomas Lukasiewicz. University of Oxford. Department of Computer Science. The Alan Turing Institute; Reino Unido Fil: Martinez, Maria Vanina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina Fil: Simari, Gerardo. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina Fil: Oana Tifrea-Marciuska. University of Oxford. Department of Computer Science. The Alan Turing Institute; Reino Unido

The incorporation of preferences into information systems, such as databases, has recently seen a surge in interest, mainly fueled by the revolution in Web data availability. Modeling the preferences of a user on the Web has also increasingly become appealing to many companies since the explosion of popularity of social media. The other surge in interest is in modeling uncertainty in these domains, since uncertainty can arise due to many uncontrollable factors. In this paper, we propose an extension of the Datalog+/- family of ontology languages with two models: one representing user preferences and one representing the (probabilistic) uncertainty with which inferences are made. Assuming that more probable answers are in general more preferable, one asks how to rank answers to a user´s queries, since the preference model may be in conflict with the preferences induced by the probabilistic model, the need thus arises for preference combination operators. We propose four specific operators and study their semantic and computational properties. We also provide an algorithm for ranking answers based on the iteration of the well-known skyline answers to a query and show that, under certain conditions, it runs in polynomial time in the data complexity. Furthermore, we report on an implementation and experimental results. Fil: Lukasiewicz, Thomas. University of Oxford; Reino Unido Fil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. University of Oxford; Reino Unido Fil: Simari, Gerardo I.. University of Oxford; Reino Unido Fil: Tifrea Marciuska, Oana. University of Oxford; Reino Unido

Though inconsistency management in databases and AI has been studied extensively for years, it does not allow the user to specify how he wants to resolve inconsistencies. In real-world applications, users may want to manage or resolve inconsistencies based not only on the data, but their own knowledge of the risks involved in decision making based on faulty data. Each user should be empowered to use reasonable policies to deal with his data and his mission needs. In this paper, we start by providing an axiomatic definition of inconsistency management policies (IMPs) that puts this power in the hands of users. Any function satisfying these axioms is an IMP. We then define three broad families of IMPs, and derive several results that show (i) how these policies relate to postulates for the revision of belief bases and to recent research in the area of consistent query answering, and (ii) how they interact with standard relational algebra operators. Finally, we present several approaches to efficiently implement an IMP-based framework.

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. Fil: Deagustini, Cristhian Ariel David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina

The "Most Probable World" (MPW) problem in probabilistic logic programming (PLPs) is that of finding a possible world with the highest probability. Past work has shown that this problem is computationally intractable and involves solving exponentially many linear programs, each of which is of exponential size. In this paper, we study what happens when the user focuses his interest on a set of atoms in such a PLP. We show that we can significantly reduce the number of worlds to be considered by defining a "reduced" linear program whose solution is in one-one correspondence with the exact solution to the MPW problem. However, the problem is still intractable. We develop a Monte Carlo sampling approach that enables us to build a quick approximation of the reduced linear program that allows us to estimate (inexactly) the solution to the MPW problem. We show experimentally that our approach works well in practice, scaling well to problems where the exact solution is intractable to compute.

There's a constant need to reason about diverse cultures all over the world. Past cultural-reasoning research has focused primarily on techniques to organize, catalog, and reason about cultural and historical artifacts of the kind typically stored in a museum. This is extremely valuable. However, the term "cultural reasoning" as we use it in the previous examples (and in this article) focuses on understanding how different cultural groups today make decisions and what factors those decisions are based on. An architecture that supports cultural reasoning should, for example, be able to pinpoint characteristics that differentiate organizations engaging political action within legitimate frameworks from those engaging in violence and terror. Key in all this is that cultural reasoning must go hand in hand with environmental reasoning. We believe that any architecture to support cultural reasoning about a given group, political entity, business, or religious organization should contain these components: 1) a semantic Web extraction engine to elicit data about the organization, 2) an opinion-mining engine that captures the organization's opinions, 3) an algorithm to correlate environmental variables with actions that the organization takes, and 4) a simulation or game environment within which analysts and users can see what the organization has done and what it might do in hypothetical situations

Representing and reasoning about preferences is a key issue in many real-world scenarios in which personalized access to information is required. Many approaches have been proposed and studied in the literature that allow a system to work with qualitative or quantitative preferences; among the qualitative models, one of the most prominent are CP-nets. Their clear graphical structure unifies an easy representation of user preferences with good computational properties when computing the best outcome. In this paper, we show how to reason with CP-nets when the attributes modeling the knowledge domain are structured via an underlying domain ontology. We show how the computation of all undominated feasible outcomes of an ontological CP-net can be reduced to the solution of a constraint satisfaction problem, and study the computational complexity of the basic reasoning problems in ontological CP-nets.

The recently introduced Datalog+/- family of ontology languages is especially useful for representing and reasoning over lightweight ontologies, and is set to play a central role in the context of query answering and information extraction for the Semantic Web. Recently, it has become apparent that it is necessary to develop a principled way to handle uncertainty in this domain. In addition to uncertainty as an inherent aspect of the Web, one must also deal with forms of uncertainty due to inconsistency and incompleteness, uncertainty resulting from automatically processing Web data, as well as uncertainty stemming from the integration of multiple heterogeneous data sources. In this paper, we take an important step in this direction by developing a probabilistic extension of Datalog+/-. This extension uses Markov logic networks as the underlying probabilistic semantics. Here, we focus especially on scalable algorithms for answering threshold queries, which correspond to the question \"what is the set of all ground atoms that are inferred from a given probabilistic ontology with a probability of at least p?\". These queries are especially relevant to Web information extraction, since uncertain rules lead to uncertain facts, and only information with a certain minimum confidence is desired. We present several algorithms, namely a basic approach, an anytime one, and one based on heuristics, which is guaranteed to return sound results. Furthermore, we also study inconsistency in probabilistic Datalog+/- ontologies. We propose two approaches for computing preferred repairs based on two different notions of distance between repairs, namely symmetric and score-based distance. We also study the complexity of the decision problems corresponding to computing such repairs, which turn out to be polynomial and NP-complete in the data complexity, respectively.

News sources are reliably unreliable. Different news sources may provide significantly differing reports about the same event. Often times, even the same news source may provide widely varying data over a period of time about the same event. Past work on inconsistency management and paraconsistent logics assume that we have "clean" definitions of inconsistency. However, when reasoning about events reported in the news, we need to deal with two unique problems: (i) are two events being reported on the same or are they different? and (ii) what does it mean for two event descriptions to be mutually inconsistent, given that these events are often described using linguistic terms that do not always have a uniquely accepted formal semantics? The answers to these two questions turn out to be closely interlinked. In this paper, we propose a probabilistic logic programming language called PLINI (Probabilistic Logic for Inconsistent News Information) within which users can write rules specifying what they mean by inconsistency in situation (ii) above. We show that PLINI rules can be learned automatically from training data using standard machine learning algorithms. PLINI is a variant of the well known generalized annotated program framework that accounts for similarity of numeric, temporal, and spatial terms occurring in news. We develop a syntax, model theoretic semantics, and fixpoint semantics for PLINI rules, and show how PLINI rules can be used to detect inconsistent news reports.

The notion of similarity has been studied in many areas of Computer Science; in a general sense, this concept is defined to provide a measure of the semantic equivalence between two pieces of knowledge, expressing how 'close' their meaning can be regarded. In this work, we study similarity as a tool useful to improve the representation of arguments, the interpretation of the relations between arguments, and the semantic evaluation associated with the arguments in the argumentative process. In this direction, we present a novel mechanism to determine the similarity between two arguments based on descriptors representing particular aspects associated with these arguments. This mechanism involves a comparison process influenced by the context in which the process develops, where this context provides the relevant aspects that need to be analyzed in the application domain. Then, we use this similarity measure as a quantity to compute the result of attacks and supports in the argumentation process. These valuations, applied to a Bipolar Argumentation Frameworks, allowed us to refine the argument relations, providing the tools to establish a family of new argumentation semantics that considers the similarity between arguments as a crucial part for the argumentation process. Fil: Budán, Paola Daniela. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y Tecnologías; Argentina. Universidad Nacional del Sur; Argentina Fil: Escañuela Gonzalez, Melisa Gisselle. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y Tecnologías; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Budan, Maximiliano Celmo David. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y Tecnologías; Argentina Fil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina Fil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad Nacional del Sur; Argentina

International audience; There has been a significant amount of interest in recent years on how to reason about inconsistent knowledge bases. However, with exception of three papers by Lozinskii, Hunter and Konieczny and by Grant and Hunter, there has been almost no work on characterizing the degree of dirtiness of a database. One can conceive of many reasonable ways of characterizing how dirty a database is. Rather than choose one of many possible measures, we present a set of axioms that any dirtiness measure must satisfy. We then present several plausible candidate dirtiness measures from the literature (including those of Hunter-Konieczny and Grant-Hunter) and identify which of these satisfy our axioms and which do not. Moreover, we define a new dirtiness measure which satisfies all of our axioms