Sylvie Despres

TERMINAE method and integration process for legal ontology building

This paper describes the contruction method of a legal application ontology. This method is based on the merging of micro-ontologies built from European community directives. The terminae construction method from texts enhanced by an alignment process with a core legal ontology is used for building micro-ontologies. A merging process allows constructing the legal ontology.

A semantic case-based reasoning framework for text categorization

This paper presents a semantic case-based reasoning framework for text categorization. Text categorization is the task of classifying text documents under predefined categories. Accidentology is our application field and the goal of our framework is to classify documents describing real road accidents under predefined road accident prototypes, which also are described by text documents. Accidents are described by accident reports while accident prototypes are described by accident scenarios. Thus, text categorization is done by assigning each accident report to an accident scenario, which highlights particular mechanisms leading to accident. We propose a textual case-based reasoning approach (TCBR), which allows us to integrate both textual and domain knowledge aspects in order to carry out this categorization. CBR solves a new problem (target case) by identifying its similarity to one or several previously solved problems (source cases) stored in a case base and by adapting their known solutions. Cases of our framework are created from text. Most of TCBR applications create cases from text by using Information Retrieval techniques, which leads to knowledge-poor descriptions of cases.We show that using semantic resources (two ontologies of accidentology) makes possible to overcome this difficulty, and allows us to enrich cases by using formal knowledge. In this paper, we argue that semantic resources are likely to improve the quality of cases created from text, and, therefore, such resources can support the reasoning cycle. We illustrate this claim with our framework developed to classify documents in the accidentology domain.

Building a Schistosomiasis Process Ontology for an Epidemiological Monitoring System

This chapter describes the design of an ontology that aims to support a monitoring system for schistosomiasis. On one hand, a domain ontology for the schistosomiasis is built to support communication and collaborative work between domain experts along the monitoring steps. The domain ontology also supports data and application integration and allows some reasoning capabilities. On the other hand, a process ontology of the schistosomiasis spreading is built for explanations and decision-making. Furthermore, the possibilities of using this process ontology for prediction are also worth considering. Here, we have focused on the design of the process ontology of infectious disease spreading and its extension to schistosomiasis. We aim to provide a formal theory in the health domain to conceptualize the processes of the infectious disease spreading and to present reasoning capabilities on the disease occurrences within a population. We emphasize on the basic entities and their relations within the complex process of infectious disease spreading. A multi-level analysis of the global dynamics is provided, taking into account biomedical, clinical and epidemiological dependences. We then propose a formalization of the infectious disease spreading process. Finally, we extend the process ontology for schistosomiasis spreading in Senegal.

On Personalization in IoT

In recent years, a large number of connected objects for health and well-being have emerged. The attractiveness of these objects for several categories of people and their decreasing cost make it an interesting opportunity especially in well-being and health applications. Despite the popularity of these objects, many people still have some concerns about their use due to various considerations such as lack of security of the collected data, %the feeling of being watched, lack of personalization, particularly considering preferences, constraints and needs of the user, etc. In this paper we first briefly survey IoT oriented towards health and well-being. Then we highlight in particular some hot-issues regarding personalization. We propose a model for IoT-based intelligent and personalized system for improving sleep quality.

An ontology supported approach to learn term to concept mapping

We propose in this paper an approach to learn term to concept mapping with the joint utilization of an existing ontology and verb relations. This is a non-supervised solution that can be applied to any field for which an ontology modeling verbs as relations holding between the concepts was already created. Conceptual graphs are learned from a natural language corpus by using part-of-speech information and statistic measures. Labeling strategies are proposed to assign terms of the corpus to concepts of the ontology by taking into account the structure of the ontology and the extracted conceptual graphs. This paper presents the approach proposed to learn the conceptual graphs from the corpus and the labeling strategies. A first experimentation in the field of accidentology was done and its results are also presented.

Propagation of uncertainties and inaccuracies in knowledge-based systems

We consider the representation of data through linguistic variables characterized by means of attributes, eventually transformed by so-called modifiers. We study several fuzzy implications and combination operators used to define a generalized modus ponens in the case where observations are slightly different from the premise of rules of the knowledge base, but a conclusion is required. We compare the properties of these tools to provide indications on their behavior in different situations. We study the stability of particular modifiers when used to describe the difference between an observation and the fact involved in the antecedent of a rule.

Towards a case-based reasoning approach to analyze road accidents

In this paper the prototype of a system designed to analyze road accidents is presented. The analysis is carried out in order to recognize within accident reports general mechanisms of road accidents that represent prototypes of road accidents. Case Based Reasoning (CBR) is the chosen problem solving paradigm. Natural language documents and semi-structured documents are used to build the cases of our sys-tem, which creates a difficulty. To cope with this difficulty we propose approaches integrating semantic resources. Hence, an ontology of accidentology and a terminology of road accidents are used to build cases. The alignment of two resources supports the retrieval process. A data processing model, based on models of accidentology, is proposed to rep-resent the cases of the system. This paper presents the architecture of ACCTOS (ACCident TO Scenarios), a case based reasoning system prototype. The model to represent the cases is introduced and the phases of the case based reasoning cycle are detailed.