In this paper, we are interested in the qualitative knowledge that underlies some given probabilistic information. To represent such qualitative structures, we use ordinal conditional functions, OCFs, (or ranking functions) as a qualitative abstraction of probability functions. The basic idea for transforming probabilities into ordinal rankings is to find well-behaved clusterings of the negative logarithms of the probabilities. We show how popular clustering tools can be used for this, and propose measures for the evaluation of the clustering results in this context. From the so obtained ranking functions, we extract conditionals that may serve as a base for inductive default reasoning.

Augmenting probabilities to conditional logic yields an expressive mechanism for representing uncertainty. The principle of optimum entropy allows one to reason in probabilistic logic in an information-theoretic optimal way by completing the given information as unbiasedly as possible. In this paper, we introduce the MEcore system that realises the core functionalities for an intelligent agent reasoning at optimum entropy and that provides powerful mechanisms for belief management operations like revision, update, diagnosis, or hypothetical what-if-analysis.

This paper introduces an information theoretic approach to verification of modular causal probabilistic models. We assume systems which are gradually extended by adding new functional modules, each having a limited domain knowledge captured by a local Bayesian network. Different modules originate from independent design processes. We assume that the local models are correct, which, however does not guarantee globally coherent inference in composed systems. The introduced method supports discovery of significant inter module dependencies which are ignored in the assembled Bayesian network.

Possibilistic networks are useful tools for reasoning under uncertainty. Uncertain pieces of information can be described by different measures: possibility measures, necessity measures and more recently, guaranteed possibility measures, denoted by Delta. This paper first proposes the use of guaranteed possibility measures to define a so-called Delta-based possibilistic network. This graphical representation tries to express and to deal with the minimal (lower-bound) possibility degree guaranteed for each variable. We then establish relationships between graphical and logical-based representations of uncertain information encoded by guaranteed possibility measures. We show that possibilistic networks based on guaranteed possibility measures can be easily transformed, in a polynomial time, in Delta-based knowledge bases. Then we analyze propagation algorithms in Delta-based possibilistic networks. In fact, standard possibilistic propagation algorithms can be re-used since we show that a simple rewriting of the chain rule allows the transformation of the initial Delta-based possibilistic networks into standard min-based possibilistic networks.

This paper is a contribution to formal ontology study. We propose a new model of knowledge representation by combining ontologies and topology. In order to represent atypical entities in the ontologies, we introduce topological operators of interior, exterior, border and closure. These operators allow us to describe whether an entity, belonging to a class, is typical or not. We define a system of relations of inclusion and membership by adapting the topological operators. We propose to formalize the topological relations of inclusion and membership by using the mathematical properties of topological operators. However, there are properties of combining operators of interior, exterior, border and closure allowing the definition of an algebra (Kuratowski, 1958). We propose to use these mathematical properties as a set of axioms. This set of axioms allows us to establish the properties of topological relations of inclusion and membership.

The paper concentrates on obtaining hidden relationships among individual clauses of complex sentences from the Prague Dependency Treebank. The treebank contains only an information about mutual relationships among individual tokens (words, punctuation marks), not about more complex units (clauses). For the experiments with clauses and their parts (segments) it was therefore necessary to develop an automatic method transforming the original annotation into a scheme describing the syntactic relationships between clauses. The task was complicated by a certain degree of inconsistency in original annotation with regard to clauses and their structure. The paper describes the algorithm of deriving clause-related information from the existing annotation and its evaluation.

Strategies for removal and introduction of combinators are very important to assure an accurate use of combinatory logic and combinators in natural language processing, especially in structural reorganization of expressions that express semantic interpretation. Such a strategy already exists for the elimination of combinators in a combinatory expression to obtain a normal form without combinators, but none existed to automate the inverse process. In our previous work, we addressed this problem by proposing an algorithm for the automation of combinators’ introduction, which finds the introduction level and introduces it at the first available spot.  However, this algorithm shows its limits.  There are some specific cases where a combinator can be introduced at more than one place.  We needed to improve our algorithm so that it can automatically find the exact path to take in order to reach the correct place where we have to introduce the combinator, and then the algorithm would work for any combinatory expression.  This paper presents the enhanced algorithm with an example of its execution.

Arabic Subject Markers are interface phenomena (specifically between morphology and syntax). In this paper, I describe them briefly, I give my linguistic analysis within the framework of the Head-Driven Phrase Structure Grammar and I show how I implement them in the LKB system. I show that this system, despite its strength, does not allow for a proper implementation of these units.

We present a work in the field of formal ontologies, notion taken from the knowledge representation community. What we study is the concept of time and aspect described and conceptualized from linguistics. Our aim is thus to propose a formal ontology of time and aspect considering temporal concepts introduced in a formal way.

In this paper we describe how the annotation methodology adopted in our approach allows us to explain the organization of indexed references in scientific research articles. We identify the semantic values of author judgments in the text segments containing indexed references.  We use an automated semantic annotation platform to annotate our corpora. Exploiting this result, we obtain a representation of the annotation distribution on different scales. Finally, we present two evaluations of the annotation.