Working on the Argument Pipeline: Through Flow Issues between Natural Language Argument, Instantiated Arguments, and Argumentation Frameworks

In many domains of public discourse such as arguments about public policy, there is an abundance of knowledge to store, query, and reason with. To use this knowledge, we must address two key general problems: first, the problem of the knowledge acquisition bottleneck between forms in which the knowledge is usually expressed, e.g., natural language, and forms which can be automatically processed; second, reasoning with the uncertainties and inconsistencies of the knowledge. Given such complexities, it is labour and knowledge intensive to conduct policy consultations, where participants contribute statements to the policy discourse. Yet, from such a consultation, we want to derive policy positions, where each position is a set of consistent statements, but where positions may be mutually inconsistent. To address these problems and support policy-making consultations, we consider recent automated techniques in natural language processing, instantiating arguments, and reasoning with the arguments in argumentation frameworks. We discuss application and “bridge” issues between these techniques, outlining a pipeline of technologies whereby: expressions in a controlled natural language are parsed and translated into a logic (a literals and rules knowledge base), from which we generate instantiated arguments and their relationships using a logic-based formalism (an argument knowledge base), which is then input to an implemented argumentation framework that calculates extensions of arguments (an argument extensions knowledge base), and finally, we extract consistent sets of expressions (policy positions). The paper reports progress towards reasoning with web-based, distributed, collaborative, incomplete, and inconsistent knowledge bases expressed in natural language

Belief Revision in Structured Probabilistic Argumentation

In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates -- based on well-known ones developed for classical knowledge bases -- that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates

How Can You Mend a Broken Inconsistent KBs in Existential Rules Using Argumentation

International audienceArgumentation is a reasoning method in presence of inconsistencies that is based on con- structing and evaluating arguments. In his seminal paper [6], Dung introduced the most abstract argumentation framework which consists of a set of arguments, a binary relation between arguments (called attack) and an extension-based semantics to extract subsets of arguments, representing consistent viewpoints, called extensions. Recently, another way of evaluating some arguments was proposed: ranking-based semantics, which ranks arguments based on their controversy with respect to attacks [3], i.e. arguments that are attacked “more severely” are ranked lower than others. Extension-based semantics and ranking-based semantics are the two main approaches that I plan to focus on in my future works.Logic-based argumentation [1] consists in instantiating argumentation framework with an inconsistent knowledge base expressed using a given logic that can be used in order to handle the underlying inconsistencies. It has been extensively studied and many frameworks have been proposed (assumption-based argumentation frameworks, DeLP, deductive argumentation or ASPIC/ASPIC+, etc.). In my current work, I chose to work with a logic that contains existential rules and to instantiate a deductive argumentation framework already available in the literature [5] with it. I made the choice of existential rules logic because of its expressivity and practical interest for the Semantic Web. Work- ing with existential-rules instantiated argumentation frameworks is challenging because of the presence of special features (n-ary conflicts or existential variables in rules) and undecidability problems for query answering in certain cases.Reasoning with an inconsistent knowledge base needs special techniques as every- thing can be entailed from falsum. Some techniques such as repair semantics [4] are based on the set of all maximal consistent subsets (repairs) of the knowledge base but usually do not give a lot of answers to queries. We propose to use argumentation in a general workflow for selecting the best repairs (mendings) of the knowledge base.The research question of my thesis is: “How can a non expert mend an inconsistent knowledge base expressed in existential-rules using argumentation?”In a first work, I addressed the lack of consideration of the existing tools for han- dling existential rules with inconsistencies by introducing the first application workflow for reasoning with inconsistencies in the framework of existential rules using argumen- tation (i.e. instantiating ASPIC+ with existential rules [9]). The significance of the study was demonstrated by the equivalence of extension-based semantics outputs between the ASPIC+ instantiation and the one in [5].Then, I focused on the practical generation of arguments from existential knowledge bases but soon realised that such a generating tool was nonexistent and that the current argumentation community did only possess randomly generated or very small argumen- tation graphs for benchmarking purposes [7]. I thus created a tool, called DAGGER, that generates argumentation graphs from existential knowledge bases [12]. The DAGGER tool was a significant contribution because it enabled me to conduct a study of theoret- ical structural properties [11] of the graphs induced by existential-rules-instantiated ar- gumentation frameworks as defined in [5], but also to analyse the behaviour of several solvers from an argumentation competition [16] regarding the generated graphs, and I studied whether their ranking (with respect to performance) was modified in the context of existential knowledge bases.It is worth noticing that the number of arguments in [5] is exponential with respect to the size of the knowledge base. Thus, I extended the structure of arguments in [5] with minimality, studied notions of core [2] and other efficient optimisations for reduc- ing the size of the produced argumentation frameworks [13]. What was surprising was that applying ranking-based semantics on a core of an argumentation framework gives different rankings than the rankings obtained from the original argumentation framework [10]. The salient point of this paper was the formal characterisation of these changes with respect to the proposed properties defined in [3].In my first two years of PhD, I made an analysis of the argumentation framework instantiated with existential rules and made several optimisations for managing the size of the argumentation graph. I also introduced a workflow for mending knowledge bases using argumentation [15]. In this workflow, subsets of arguments are extracted (view- points) and the ranking on arguments is “lifted” to these viewpoints to select the best mending. It is worth noticing that we also provided different desirable principles that the workflow should satisfy.In the last year, I plan to first study the following question: “In which ways do argu- mentation methods perform better than classical methods for knowledge bases mending ?” Indeed, I expect argumentation to work well for mending knowledge bases because of the following reasons: (1) ranking-based semantics are generally easy to compute and follow several desirable principles [3], (2) argumentation represents pieces of consistent knowledge as nodes and the inconsistencies as attacks. The ability of using argumenta- tion paths (sequence of attacks) is often neglected or ignored in traditional logic.Lastly, I plan on comparing argumentation methods with more logical methods [14] based on inconsistency measures and export all of my results by applying them on previously studied real world use-cases obtained in the framework of the agronomy Pack4Fresh project [8]

A Plausibility Semantics for Abstract Argumentation Frameworks

We propose and investigate a simple ranking-measure-based extension semantics for abstract argumentation frameworks based on their generic instantiation by default knowledge bases and the ranking construction semantics for default reasoning. In this context, we consider the path from structured to logical to shallow semantic instantiations. The resulting well-justified JZ-extension semantics diverges from more traditional approaches.Comment: Proceedings of the 15th International Workshop on Non-Monotonic Reasoning (NMR 2014). This is an improved and extended version of the author's ECSQARU 2013 pape