Visualizing and Interacting with Concept Hierarchies

Concept Hierarchies and Formal Concept Analysis are theoretically well grounded and largely experimented methods. They rely on line diagrams called Galois lattices for visualizing and analysing object-attribute sets. Galois lattices are visually seducing and conceptually rich for experts. However they present important drawbacks due to their concept oriented overall structure: analysing what they show is difficult for non experts, navigation is cumbersome, interaction is poor, and scalability is a deep bottleneck for visual interpretation even for experts. In this paper we introduce semantic probes as a means to overcome many of these problems and extend usability and application possibilities of traditional FCA visualization methods. Semantic probes are visual user centred objects which extract and organize reduced Galois sub-hierarchies. They are simpler, clearer, and they provide a better navigation support through a rich set of interaction possibilities. Since probe driven sub-hierarchies are limited to users focus, scalability is under control and interpretation is facilitated. After some successful experiments, several applications are being developed with the remaining problem of finding a compromise between simplicity and conceptual expressivity

Knowledge organisation and information retrieval using Galois lattices

Colloque avec actes et comité de lecture. internationale.International audienceIn this paper we investigate the application of Galois (or concept) lattices on different data sources (e.g. web documents or bibliographical items) in order to organise knowledge that can be extracted from the data. This knowledge organisation can serve a number of purposes (e.g. knowledge management in an organisation, document retrieval on the Web, etc.). Galois lattices can be considered as classification tools for knowledge units in concept hierarchies that can be used within a knowledge-based system. Moreover, Galois lattices can be used in parallel with domain ontologies for building more precise and more concise concept ontologies, and for guiding the knowledge discovery process

Exploring a Geographical Dataset with GEOLIS

International audienceGeographical data are mainly structured in layers of information. However, this model of organisation is not convenient for navigation inside a dataset, and so limits geographical data exploration to querying. We think information retrieval could be made easier in GIS by the introduction of a navigation based on geographical object properties. For this purpose, we propose a prototype, GEOLIS1, which tightly combines querying and navigation in the search process of geographical data. GEOLIS relies on Logical Information Systems (LIS), which are based on Formal Concept Analysis (FCA) and logics. In this paper, we detail data organisation and navigation process in GEOLIS. We also present the results of an experimentation led on a real dataset

Classification conceptuelle d'une collection documentaire. Intertextualité et Recherche d'Information

National audienceUne collection documentaire est généralement représentée comme un ensemble de documents mais cette modélisation ne permet pas de rendre compte des relations intertextuelles et du contexte d'interprétation d'un document. Le modèle documentaire classique trouve ses limites dans les domaines spécialisés où les besoins d'accès à l'information correspondent à des usages spécifiques et où les documents sont liés par de nombreux types de relations. Cet article propose un modèle permettant de rendre compte de cette complexité des collections documentaire dans les outils d'accès à l'information. En se basant sur l'analyse formelle et relationnelle de concepts appliquée sur des objets documentaires ce modèle permet de représenter et d'interroger de manière unifiée les descripteurs de contenu des documents et les relations intertextuelles qu'ils entretiennent

Handling Spatial Relations in Logical Concept Analysis to Explore Geographical Data ⋆

Abstract. Because of the expansion of geo-positioning tools and the democratization of geographical information, the amount of geo-localized data that is available around the world keeps increasing. So, the ability to efficiently retrieve informations in function of their geographical facet is an important issue. In addition to individual properties such as position and shape, spatial relations between objects are an important criteria for selecting and reaching objects of interest: e.g., given a set of touristic points, selecting those having a nearby hotel or reaching the nearby hotels. In this paper, we propose Logical Concept Analysis (LCA) and its handling of relations for representing and reasoning on various kinds of spatial relations: e.g., Euclidean distance, topological relations. Furthermore, we present an original way of navigating in geolocalized data, and compare the benefits of our approach with traditional Geographical Information Systems (GIS).

A Hybrid Classification Method for Database Contents Analysis

Colloque avec actes et comité de lecture. internationale.International audienceThe hybridisation of different classification and mining techniques coming from different areas such as the numeric and the symbolic worlds can produce a significant enhancement of the overall classification and retrieval performance in a Data Mining or Information Retrieval context.This paper introduces an experimental methodology to match an explicative structure issued from a symbolic classification to a numerical classification. The classification models used in the experiment are a boolean lattice on the symbolic side and a Kohonen Self Organising Map model (SOM) on the numerical side

Formal concept matching and reinforcement learning in adaptive information retrieval

The superiority of the human brain in information retrieval (IR) tasks seems to come firstly from its ability to read and understand the concepts, ideas or meanings central to documents, in order to reason out the usefulness of documents to information needs, and secondly from its ability to learn from experience and be adaptive to the environment. In this work we attempt to incorporate these properties into the development of an IR model to improve document retrieval. We investigate the applicability of concept lattices, which are based on the theory of Formal Concept Analysis (FCA), to the representation of documents. This allows the use of more elegant representation units, as opposed to keywords, in order to better capture concepts/ideas expressed in natural language text. We also investigate the use of a reinforcement leaming strategy to learn and improve document representations, based on the information present in query statements and user relevance feedback. Features or concepts of each document/query, formulated using FCA, are weighted separately with respect to the documents they are in, and organised into separate concept lattices according to a subsumption relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the concepts in the lattice representation. This avoids implementation drawbacks faced by other FCA-based approaches. Retrieval of a document for an information need is based on concept matching between concept lattice representations of a document and a query. The learning strategy works by making the similarity of relevant documents stronger and non-relevant documents weaker for each query, depending on the relevance judgements of the users on retrieved documents. Our approach is radically different to existing FCA-based approaches in the following respects: concept formulation; weight assignment to object-attribute pairs; the representation of each document in a separate concept lattice; and encoding concept lattices in BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our learning strategy makes use of relevance feedback information to enhance document representations, thus making the document representations dynamic and adaptive to the user interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are presented and compared with published results. In particular, the performance of the system is shown to improve significantly as the system learns from experience.The School of Computing, University of Plymouth, UK

Visualizing Social Photos on a Hasse Diagram for Eliciting Relations and Indexing New Photos

International audienceSocial photos, which are taken during family events or parties, represent individuals or groups of people. We show in this paper how a Hasse diagram is an efficient visualization strategy for eliciting different groups and navigating through them. However, we do not limit this strategy to these traditional uses. Instead we show how it can also be used for assisting in indexing new photos. Indexing consists of identifying the event and people in photos. It is an integral phase that takes place before searching and sharing. In our method we use existing indexed photos to index new photos. This is performed through a manual drag and drop procedure followed by a content fusion process that we call ‘propagation'. At the core of this process is the necessity to organize and visualize the photos that will be used for indexing in a manner that is easily recognizable and accessible by the user. In this respect we make use of an Object Galois Sub-Hierarchy and display it using a Hasse diagram. The need for an incremental display that maintains the user's mental map also leads us to propose a novel way of building the Hasse diagram. To validate the approach, we present some tests conducted with a sample of users that confirm the interest of this organization, visualization and indexation approach. Finally, we conclude by considering scalability, the possibility to extract social networks and automatically create personalised albums

Graph Structures for Knowledge Representation and Reasoning

This open access book constitutes the thoroughly refereed post-conference proceedings of the 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020, held virtually in September 2020, associated with ECAI 2020, the 24th European Conference on Artificial Intelligence. The 7 revised full papers presented together with 2 invited contributions were reviewed and selected from 9 submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which allows to bridge the gap between the different communities