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

Concept coupling learning for improving concept lattice-based document retrieval

© 2017 Elsevier Ltd The semantic information in any document collection is critical for query understanding in information retrieval. Existing concept lattice-based retrieval systems mainly rely on the partial order relation of formal concepts to index documents. However, the methods used by these systems often ignore the explicit semantic information between the formal concepts extracted from the collection. In this paper, a concept coupling relationship analysis model is proposed to learn and aggregate the intra- and inter-concept coupling relationships. The intra-concept coupling relationship employs the common terms of formal concepts to describe the explicit semantics of formal concepts. The inter-concept coupling relationship adopts the partial order relation of formal concepts to capture the implicit dependency of formal concepts. Based on the concept coupling relationship analysis model, we propose a concept lattice-based retrieval framework. This framework represents user queries and documents in a concept space based on fuzzy formal concept analysis, utilizes a concept lattice as a semantic index to organize documents, and ranks documents with respect to the learned concept coupling relationships. Experiments are performed on the text collections acquired from the SMART information retrieval system. Compared with classic concept lattice-based retrieval methods, our proposed method achieves at least 9%, 8% and 15% improvement in terms of average MAP, IAP@11 and P@10 respectively on all the collections

On quantum statistics in data analysis

Originally, quantum probability theory was developed to analyze statistical phenomena in quantum systems, where classical probability theory does not apply, because the lattice of measurable sets is not necessarily distributive. On the other hand, it is well known that the lattices of concepts, that arise in data analysis, are in general also non-distributive, albeit for completely different reasons. In his recent book, van Rijsbergen argues that many of the logical tools developed for quantum systems are also suitable for applications in information retrieval. I explore the mathematical support for this idea on an abstract vector space model, covering several forms of data analysis (information retrieval, data mining, collaborative filtering, formal concept analysis...), and roughly based on an idea from categorical quantum mechanics. It turns out that quantum (i.e., noncommutative) probability distributions arise already in this rudimentary mathematical framework. We show that a Bell-type inequality must be satisfied by the standard similarity measures, if they are used for preference predictions. The fact that already a very general, abstract version of the vector space model yields simple counterexamples for such inequalities seems to be an indicator of a genuine need for quantum statistics in data analysis.Comment: 7 pages, Quantum Interaction 2008 (Oxford, April 2008) v3: added two diagrams, changed some wording

Single-Focus Broadening Navigation in Concept Lattices

Formal concept analysis has been used to support information retrieval tasks in many domains, in particular the traditional "by keyword" document search with a conjunctive query interpretation. However, support for exploratory search or browsing needs new navigation algorithms that allow users (i) to continuously update the current query and (ii) to broaden as well as refine the result set. In this paper we investigate a step-wise navigation algorithm that supports both broadening and refinement operations. Our navigation operations maintain some useful algebraic properties. We motivate our approach on a dataset of wine reviews, which contains different facets of information

Formal Concept Analysis and Information Retrieval – A Survey

International audienceOne of the first models to be proposed as a document index for retrieval purposes was a lattice structure, decades before the introduction of Formal Concept Analysis. Nevertheless, the main notions that we consider so familiar within the community (" extension " , " intension " , " closure operators " , " order ") were already an important part of it. In the '90s, as FCA was starting to settle as an epistemic community, lattice-based Information Retrieval (IR) systems smoothly transitioned towards FCA-based IR systems. Currently, FCA theory supports dozens of different retrieval applications, ranging from traditional document indices to file systems, recommendation, multi-media and more recently, semantic linked data. In this paper we present a comprehensive study on how FCA has been used to support IR systems. We try to be as exhaustive as possible by reviewing the last 25 years of research as chronicles of the domain, yet we are also concise in relating works by its theoretical foundations. We think that this survey can help future endeavours of establishing FCA as a valuable alternative for modern IR systems

RV-Xplorer: A Way to Navigate Lattice-Based Views over RDF Graphs

International audienceMore and more data are being published in the form of machine readable RDF graphs over Linked Open Data (LOD) Cloud accessible through SPARQL queries. This study provides interactive navigation of RDF graphs obtained by SPARQL queries using Formal Concept Analysis. With the help of this {\tt View By} clause a concept lattice is created as an answer to the SPARQL query which can then be visualized and navigated using RV-Xplorer (Rdf View eXplorer). Accordingly, this paper discusses the support provided to the expert for answering certain questions through the navigation strategies provided by RV-Xplorer. Moreover, the paper also provides a comparison existing state of the art approaches

Steps Towards Interactive Formal Concept Analysis with LatViz

International audienceWith the increase in Web of Data (WOD) many new challenges regarding exploration, interaction, analysis and discovery have surfaced. One of the basic building blocks of data analysis is classification. Many studies have been conducted concerning Formal Concept Analysis (FCA) and its variants over WOD. But one fundamental question is, after these concept lattices are obtained on top of WOD, how the user can interactively explore and analyze this data through concept lattices. To achieve this goal, we introduce a new tool called LatViz, which implements several algorithms for constructing concept lattices and allows further navigation over lattice structure. LatViz proposes some remarkable improvements over existing tools and introduces various new functionalities such as interaction with expert, visualization of Pattern Structures, AOC posets, concept annotations, filtering concept lattice based on several criteria and finally, an intuitive visualization of implications. This way the user can effectively perform an interactive exploration over a concept lattice which is a basis for a strong user interaction with WOD for data analysis