Matteo Cristani

A Survey on Ontology Creation Methodologies

In the current literature of knowledge management and artificial intelligence, several different approaches to the problem have been carried out of developing domain ontologies from scratch. All these approaches deal fundamentally with three problems: (1) providing a collection of general terms describing classes and relations to be employed in the description of the domain itself; (2) organizing the terms into a taxonomy of the classes by the ISA relation; and (3) expressing in an explicit way the constraints that make the ISA pairs meaningful. Though a number of such approaches can be found, no systematic analysis of them exists which can be used to understand the inspiring motivation, the applicability context, and the structure of the approaches. In this paper, we provide a framework for analyzing the existing methodologies that compares them to a set of general criteria. In particular, we obtain a classification based upon the direction of ontology construction; bottom-up are those methodologies that start with some descriptions of the domain and obtain a classification, while top-down ones start with an abstract view of the domain itself, which is given a priori. The resulting classification is useful not only for theoretical purposes but also in the practice of deployment of ontologies in Information Systems, since it provides a framework for choosing the right methodology to be applied in the specific context, depending also on the needs of the application itself.

The complexity of reasoning about spatial congruence

In the recent literature of Artificial Intelligence, an intensive research effort has been spent, for various algebras of qualitative relations used in the representation of temporal and spatial knowledge, on the problem of classifying the computational complexity of reasoning problems for subsets of algebras. The main purpose of these researches is to describe a restricted set of maximal tractable subalgebras, ideally in an exhaustive fashion with respect to the hosting algebras. In this paper we introduce a novel algebra for reasoning about Spatial Congruence, show that the satisfiability problem in the spatial algebra MC-4 is NP-complete, and present a complete classification of tractability in the algebra, based on the individuation of three maximal tractable subclasses, one containing the basic relations. The three algebras are formed by 14, 10 and 9 relations out of 16 which form the full algebra.

Superiority based revision of defeasible theories

We propose a systematic investigation on how to modify a preference relation in a defeasible logic theory to change the conclusions of the theory itself. We argue that the approach we adopt is applicable to legal reasoning, where users, in general, cannot change facts and rules, but can propose their preferences about the relative strength of the rules. We provide a comprehensive study of the possible combinatorial cases and we identify and analyse the cases where the revision process is successful.

Compliant Business Process Design by Declarative Specifications

We propose algorithms to synthesise the specifications modelling the capabilities of an agent, the environment she acts in, and the governing norms, into a process graph. This process graph corresponds to a collection of courses of action and represents all the licit alternatives the agent may choose to meet her outcomes. The starting point is a compliant situation, i.e., a situation where an agent is capable of reaching all her outcomes without violating the norms. In this case, the resulting process will be compliant by design.

Strategic Argumentation is NP-complete

We study the complexity of the Strategic Argumentation Problem for 2-player dialogue games where a player should decide what move (set of rules) to play at each turn in order to prove (disprove) a given thesis. We show that this is an NP-complete problem.

Improving Energy Saving Techniques by Ambient Intelligence Scheduling

Energy saving is one of the most challenging aspects of modern ambient intelligence technologies, for both domestic and business usages. In this paper we show how to combine Ambient Intelligence and Artificial Intelligence techniques to solve the problem of scheduling a set of devices under a given set of constraints, like limits to the maximal energy usage (Energy Span) and maximal energy absorption (Energy Peak). We provide a method that can be used to schedule the usage of devices in a given environment in a way that respects the input constraints. We adapt an existent approach to scheduling for Ambient Intelligence to a specific framework and exhibit a sample usage for a real life system, Elettra, that is in use in an industrial context.

SpaceML: A Mark-up Language for Spatial Knowledge

Abstract We introduce the syntax of a new mark-up language, paceML, an evolution of the eo–ML Language, previously developed by one of the authors, for marking up Geographic Data on the Web. The language is specified in ML by means of tag definitions. It allows the description of three aspects of spatial knowledge: (1) the category attribution to space regions: for instance, whether the region is self-connected, bounded or regular; (2) the ascription of regions to topological, distance, morphological and orientation relations; (3) the definition of a region in terms of its boundary. We provide the grammar of paceML, define some constraints on the behaviour of browsers reading paceML pages, and specify the tags of SpaceML by an ML DTD which can be easily translated into an ML Schema.

Energy Saving by Ambient Intelligence Techniques

Nowadays the problem of energy consumption is becoming a pressing problem. We present an innovative system named Elettra able to allow people to monitor and control energy consumption in one or more buildings. For improving Elettra we introduce different methods taken from ambient intelligence. Through these methods we can infer energy consumption, construct a plan for decreasing energy consumption, improve this plan and adopt it to the system. The implementation of these methods to Elettra helps its automation and increases its efficiency.

Defeasible Reasoning about Electric Consumptions

Conflicting rules and rules with exceptions are very common in natural language specification to describe the behaviour of devices operating in a real-world context. This is common exactly because those specifications are processed by humans, and humans apply common sense and strategic reasoning about those rules. In this paper, we deal with the challenge of providing, step by step, a model of energy saving rule specification and processing methods that are used to reduce the consumptions of a system of devices. We argue that a very promising non-monotonic approach to such a problem can lie upon Defeasible Logic. Starting with rules specified at an abstract level, but compatibly with the natural aspects of such a specification (including temporal and power absorption constraints), we provide a formalism that generates the extension of a basic defeasible logic, which corresponds to turned on or off devices.

Learning as Meaning Negotiation: A Model Based on English Auction

In this paper we focus upon a specific process of knowledge sharing, the meaning negotiation one, in presence of a number of negotiating subjects greater than two. Current approaches of the literature are based upon argumentation techniques, persuasion dialogue and game theory and all of them are specific of the negotiation subject and typically limited to two agents. In this paper we present a formalisation of the problem based upon a specific game, the English Auction, that results independent of the number of involved agents and of the negotiation subject. In our framework, each agents viewpoint is denoted by a logical theory. We show that the problem as posed in the framework is decidable, under a very general assumption and that the computational complexity of the algorithm is polynomial in the number of agents and in the number of preferred representations of the agents.