Marie-Christine Rousset

Bio: Marie-Christine Rousset is an academic researcher from Institut Universitaire de France. The author has contributed to research in topics: Description logic & Ontology (information science). The author has an hindex of 29, co-authored 116 publications receiving 3143 citations. Previous affiliations of Marie-Christine Rousset include University of Paris & University of Grenoble.

TreeFinder: a first step towards XML data mining

Abstract: In this paper we consider the problem of searching frequent trees from a collection of tree-structured data modeling XML data. The TreeFinder algorithm aims at finding trees, such that their exact or perturbed copies are frequent in a collection of labelled trees. To cope with complexity issues, TreeFinder is correct but not complete: it finds a subset of actually frequent trees. The default of completeness is experimentally investigated on artificial medium size datasets; it is shown that TreeFinder reaches completeness or falls short for a range of experimental settings.

CARIN: A Representation Language Combining Horn Rules and Description Logics.

Abstract: We describe CARIN, a novel family of representation languages, which integrate the expressive power of Horn rules and of description logics. We address the key issue in designing such a language, namely, providing a sound and complete inference procedure. We identify existential entailment as a core problem in reasoning in CARIN, and describe an existential entailment algorithm for CARIN languages whose description logic component is ALCNR. This algorithm entails several important results for reasoning in CARIN, most notably: (1) a sound and complete inference procedure for non recursive CARIN-ALCNR, and (2) an algorithm for determining rule subsumption overALCNR.

The use of carin language and algorithms for information integration: the picsel system

Abstract: PICSEL is an information integration system over sources that are distributed and possibly heterogeneous. The approach which has been chosen in PICSEL is to define an information server as a knowledge-based mediator in which CARIN is used as the core logical formalism to represent both the domain of application and the contents of information sources relevant to that domain. In this paper, we describe the way the expressive power of the CARIN language is exploited in the PICSEL information integration system, while maintaining the decidability of query answering. We illustrate it on examples coming from the tourism domain, which is the first real case that we have to consider in PICSEL, in collaboration with the travel agency Degriftour. see

Rewriting queries using views in description logics

Abstract: The problem of rewriting queries using views is to iind a query expression that uses only a set of views V and is equivalent to (or maximally contained in) a given query Q. Rewriting queries using views is important for query optimization and for applications such as information integration and data warehousing. Description logics are a family of logics that were developed for modeling complex hierarchical structures, and can also be viewed as a query language with an interesting tradeoff between complexity and expressive power. We consider the problem of rewriting queries using views expressed in description logics and conjunctive queries over description logics. We show that if the view definitions do not contain existential variables, then it is always possible to find a rewriting that is a union of conjunctive queries, and furthermore, this rewriting produces the maximal set of answers possible from the views. If the views have existential variables, the rewriting may be recursive. We present an algorithm for producing a recursive rewriting, that is guaranteed to be a maximal one when the underlying database forms a tree of constants. We show that in general, it is not always be possible to find a maximal rewriting.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Data integration: a theoretical perspective

Abstract: Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

Ontology Matching

Abstract: Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.