Marie-Aude Aufaure

ONTO-EVO A L an ontology evolution approach guided by pattern modeling and quality evaluation

In this paper, we present a generic ontology evolution approach guided by a pattern-oriented process and a quality evaluation activity. Pattern modeling aims to drive and control change application while maintaining consistency of the evolved ontology. Evaluation activity ---supported by an ontology quality model--- is used to guide inconsistency resolution by assessing the impact of resolution alternatives ---proposed by the evolution process- on ontology quality and selecting the resolution that preserves the quality of the evolved ontology.

Semantic mappings in description logics for spatio-temporal database schema integration

The interoperability problem arises in heterogeneous systems where different data sources coexist and there is a need for meaningful information sharing. One of the most representive realms of diversity of data representation is the spatio-temporal domain. Spatio-temporal data are most often described according to multiple and greatly diverse perceptions or viewpoints, using different terms and with heterogeneous levels of detail. Reconciling this heterogeneity to build a fully integrated database is known to be a complex and currently unresolved problem, and few formal approaches exist for the integration of spatio-temporal databases. The paper discusses the interoperation issue in the context of conceptual schema integration. Our proposal relies on two well-known formalisms: conceptual models and description logics. The MADS conceptual model with its multiple representation capabilities allows to fully describe semantics of the initial and integrated spatio-temporal schemas. Description logics are used to express the set of inter-schema mappings. Inference mechanisms of description logics allow us to check the compatibility of the semantic mappings and to propose different structural solutions for the integrated schema.

A visual language for querying spatio-temporal databases

In recent years, citizen oriented applications have been developed with Geographic Information Systems (GIS). This is the reason why visual querying appears to be crucial. In the past, we developed a visual language Lvis based on a query-by-example philosophy for spatial data. A query is formulated by means of icons which map spatial objects and operators. But, this language needs also to integrate temporal data handling because geographic databases represent a spatio-temporal continuum. This paper presents the extension of the Lvis language, which was previously only developed for spatial data, to temporal data. New visual metaphors such as balloons and anchors are proposed in order to express spatial and temporal criteria. After a state of the art of visual querying for spatial databases, we introduce a spatiotemporal model. The visual language and its user interface are then explained. Samples are proposed upon a road risk management application. In this sample database both discrete and continuous temporal data are taken into account: moving points such as trucks, and life-cycle objects such as rivers. We then conclude about our future work.

A buzz and e-reputation monitoring tool for twitter based on galois lattices

In the actual interconnected world, the speed of broadcasting of information leads the formation of opinions towards more and more immediacy. Big social networks, by allowing distribution, and therefore broadcasting of information in a almost instantaneous way, also speed up the formation of opinions concerning actuality. Then, these networks are great observatories of opinions and e-reputation. In this e-reputation monitoring task, it is easy to get a set of information (web pages, blog pages, tweets,...) containing a chosen word or a set of words ( a company name, a domain of interest,...), and then we can easily search for the most used words. But a harder, but more interesting task, is to track the set of jointly used words in this dataset, because this latter contains the more shared advice about the initial searched set of words. Precisely, the exhaustive discovering of the shared properties of a collection of objects is the main task of the Galois lattices used in the Formal Concept Analysis. In this article we state clearly the characteristics, advantages and constraints of one of the more successful online social networks: Twitter. Then we detail the difficult task of tracking, on Twitter, the most forwarded information about a chosen subject. We also explain how the characteristics of Galois lattices permit to solve elegantly and efficiently this problem. But, retrieving the most used corpus of words is not enough, we have to show the results in an informative and readable manner, which is not easy when the result is a Galois Lattice. Then we propose a visualisation called topigraphic network of tags, which represent a tag cloud in a network of concepts with a topographic allegory, which permits to visualise the more important concepts found about a given search on Twitter.

Good location, terrible food: detecting feature sentiment in user-generated reviews

A growing corpus of online informal reviews is generated every day by non-experts, on social networks and blogs, about an unlimited range of products and services. Users do not only express holistic opinions, but often focus on specific features of their interest. The automatic understanding of “what people think” at the feature level can greatly support decision making, both for consumers and producers. In this paper, we present an approach to feature-level sentiment detection that integrates natural language processing with statistical techniques, in order to extract users’ opinions about specific features of products and services from user-generated reviews. First, we extract domain features, and each review is modelled as a lexical dependency graph. Second, for each review, we estimate the polarity relative to the features by leveraging the syntactic dependencies between the terms. The approach is evaluated against a ground truth consisting of set of user-generated reviews, manually annotated by 39 human subjects and available online, showing its human-like ability to capture feature-level opinions.

A contextual user model for web personalization

Over the past years, information personalization has provided several valuable achievements on the improvement and optimization of Web searching and recommendation taking into account users interests, preferences and contextual information. The main objective of a personalization system is to perform an information retrieval process taking into account the perception and the interest of the end-users. This paper focuses on how to model the user and his context in an extensible way that can be interpreted and used for personalization. We describe the architecture that provides personalization facilities based on the contextual user model for tourism usage.

A natural language interface for data warehouse question answering

Business Intelligence (BI) aims at providing methods and tools that lead to quick decisions from trusted data. Such advanced tools require some technical knowledge on how to formulate the queries. We propose a natural language (NL) interface for a Data Warehouse based Question Answering system. This system allows users to query with questions expressed in natural language. The proposed system is fully automated, resulting low Total Cost of Ownership. We aim at demonstrating the importance of identifying already existing semantics and using Text Mining techniques on the Web to move toward the userss need.

Join Indices as a Tool for Spatial Data Mining

The growing production of maps is generating huge volume of data stored in large spatial databases. This huge volume of data exceeds the human analysis capabilities. Spatial data mining methods, derived from data mining methods, allow the extraction of knowledge from these large spatial databases, taking into account the essential notion of spatial dependency. This paper focuses on this specificity of spatial data mining by showing the suitability of join indices to this context. It describes the join index structure and shows how it could be used as a tool for spatial data mining. Thus, this solution brings spatial criteria support to non-spatial information systems.

Extracting and Visualising Tree-like Structures from Concept Lattices

Traditional software in Formal Concept Analysis makes little use of visualization techniques, producing poorly readable concept lattice representations when the number of concepts exceeds a few dozens. This is problematic as the number of concepts in such lattices grows significantly with the size of the data and the number of its dimensions. In this work we propose several methods to enhance the readability of concept lattices firstly though colouring and distortion techniques, and secondly by extracting and visualizing trees derived from concept lattice structures. These contributions represent an important step in the visual analysis of conceptual structures, as domain experts may visually explore larger datasets that traditional visualizations of concept lattice cannot represent effectively.

Predicting Your Next OLAP Query Based on Recent Analytical Sessions

In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multidimensional data cubes. Being able to predict the most likely next queries would provide a way to recommend interesting queries to users on the one hand, and could improve the efficiency of OLAP sessions on the other. In particular, query recommendation would proactively guide users in data exploration and improve the quality of their interactive experience. In this paper, we propose a framework to predict the most likely next query and recommend this to the user. Our framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric. To gain insight in the recommendation precision and on what parameters it depends, we evaluate our approach using different quality assessments.