Using Formal Concept Analysis for Maritime Ontology Building

In the last years, the main orientation of Formal Concept Analysis (FCA) has turned from mathematics towards computer science. This article provides a review of this new orientation and analyzes why and how FCA and computer science attracted each other. It discusses FCA as a knowledge representation formalism using five knowledge representation principles provided by Davis, Shrobe, and Szolovits [15]. It then studies how and why mathematics-based researchers got attracted by computer science. We will argue for continuing this trend by integrating the two research areas FCA and Ontology Engineering.

Off to new shores: conceptual knowledge discovery and processing

The digital medium allows visitors, curators and art historians to gain new insights into their collections through data analysis and rich, interactive visualizations. Motivated by the rise of large-scale cultural heritage collections that have emerged on the Web, we argue that Formal Concept Analysis can be used to highlight the relationships between objects and their features within digital art collections and provide a means for visitors to explore these collections via interactive, narrated pathways. Our work presents four research projects that span 10 years from 2005 - 2015 – ImageSleuth, The Virtual Museum of the Pacific, A Place for Art and a scalability study of Formal Concept Analysis as applied to a data-set from the Brooklyn Museum. Our approach is based on the idea that much of the meaning that can be interpreted from museum collections lies – at least in part – in the way that objects are related to one another. Our work examines how Formal Concept Analysis can drive explorative, narrative-based visitor experiences and reveal new insights into cultural heritage collections.

Pattern extraction is one of the major topics in Knowledge Discovery. Out of numerous data mining techniques we propose to use a new approach: Formal Concept Analysis (FCA) together with Graph Databases with the implementation Neo4j. FCA is a prominent field of applied mathematics using maximal clusters of object-attribute relationships to discover and represent knowledge structures. The use of FCA gained much importance in many research domains in recent years. Despite the similarity of the graph representation between FCA and Neo4j, the structure and relations among the elements are represented differently and can offer a different perspective on the analysis. This paper gives more insight into how patterns can be extracted from medical data and interpreted by means of FCA and Neo4j in order to help medical staff improve the accuracy of diagnoses. We examine the factors related to patients' symptoms and diagnostics and then compare the results with the ones provided by a medical care center. We apply knowledge discovery techniques and conceptual landscapes paradigm in order to obtain an in-depth and high qualitative knowledge representation of medical data. By making use of the effectiveness and the graphical representation of conceptual hierarchies we extract valuable knowledge from medical data sets through which we solve the knowledge discovery, processing and representation task in Electronic Health Record systems.

The paper first offers a parallel between two approaches to conceptual clustering, namely formal concept analysis (augmented with the introduction of new operators) and bipartite graph analysis. It is shown that a formal concept (as defined in formal concept analysis) corresponds to the idea of a maximal bi-clique, while sub-contexts, which correspond to independent “conceptual worlds” that can be characterized by means of the new operators introduced, are disconnected sub-graphs in a bipartite graph. The parallel between formal concept analysis and bipartite graph analysis is further exploited by considering “approximation” methods on both sides. It leads to suggest new ideas for providing simplified views of datasets, taking also inspiration from the search for approximate itemsets in data mining (with relaxed requirements), and the detection of communities in hierarchical small worlds.

The concept lattice, the core data structure in the formal concept analysis, has been used widely in machine learning, data mining, etc. In the formal concept analysis, researching on constructing the concept lattice efficiently is one of the important contents. This paper presents a new algorithm called IETreeCS (Concept Sets based on Intension and Extension Tree) based on the IE-Tree (Intension and Extension Tree) and characteristic space partitions. The IETreeCS algorithm firstly defines an IE-Tree, and then it converts the formal context into the IE-Tree to reduce the storage of data sets. The paper also defines characteristic spaces and describes how to partition a characteristic space on the basis of the IE-Tree, and finally presents an integrated algorithm that generates all concepts efficiently from a binary relation. The experimental results show that the IETreeCS algorithm performs much better than the NextClosure and SSPCG algorithms in the large-scale sparse data sets or distributed data sets.

A key ability of human reasoning is analogical reasoning. In this context, an important notion is that of analogical proportions that have been formalized and analyzed in the last decade. A bridging to Formal Concept Analysis (FCA) has been brought by introducing analogical complexes, i.e. formal concepts that share a maximal analogical relation enabling by this analogies between (formal) concepts. Electronic Health Record (EHR) systems are nowadays widespread and used in different scenarios. In this paper we consider the problem of improving EHR systems by using analogical complexes in an FCA based setting. Moreover, we present a study case of analogical complexes in a medical field. We analyze analogical proportions in Electronic Health Record Systems and prove that EHRs can be improved with an FCA grounded analogical reasoning component. This component offers methods for knowledge discovery and knowledge acquisition for medical experts based on patterns revealed by analogies. We also show that combining analogical reasoning with FCA brings a new perspective on the analyzed data that can improve the understanding of the subsequent knowledge structures and offering a valuable support for decision making.

Evaluation of IPAQ questionnaires supported by formal concept analysis

In traditional formal concept analysis, the attributes in the formal context are considered fixed. However, in the real world data set, attributes always have different levels of granularity, correspondingly, the derived concept lattice may reveal different information and patterns. Therefore, the capability to change the level of granularity of an attribute in formal concept analysis to capture relevant patterns in data is a natural requirement. In this paper, a theoretical study has been undertaken in multi-scale formal contexts, where attributes with different levels of granularity possess different attribute values. Two types of formal concepts, i.e., object-oriented and property-oriented multi-scale concepts, are introduced and studied in detail. The collection of object-oriented concept lattices and property-oriented concept lattices can be obtained at different granularity levels of attributes. It has been shown that the set of extents in the derived concept lattices increases when we choose to use a finer level of granularity. Moreover, a corresponding bidirectional approach to concept construction(i.e., from coarser to finer and from finer to coarser, respectively) is exhibited, and some characterization theorems have been obtained.

Generalized decision directed acyclic graphs for classification tasks

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.

This paper presents results of the finding of the author’s profiles using formal concepts generated from DBLP database. Our main aim was to evaluate the use of formal concept analysis as a method for extracting the author’s profiles. There are several commonly used methods for clustering and for finding experts in a large database. These methods are mainly based on different kinds of clustering and metrics which are sometimes difficult to understand. Finding experts for particular and mainly special areas of research is not an easy task. Formal concept analysis (FCA) is a method with a very strong mathematical background, which makes it easy to understand. Properties of FCA can give us a very strong tool for finding author’s profiles.

We aim to construct a method to apply Formal Concept Analysis to small-scale data and to use the logic of implication obtained from the analysis for statistical analysis. Specifically, we obtain learning history data of e-learning system users in a class of about 40-60 students in a given year, and use Formal Concept Analysis to obtain an implication relation that can be expressed as “a person who answers one question (group) correctly or incorrectly will always answer another question (group) correctly or incorrectly”. We compare the tendency of correct/incorrect answers of the questions (groups) included in the obtained implication relation with the relationships among the questions (groups) obtained from the statistical analysis of the data of multiple years, which is larger than the group to which Formal Concept Analysis was applied.

With the exponential growth of the World Wide Web (WWW), it has become the most popular place to gather information. However the size of the WWW makes it difficult for people to locate relevant information. About 85% of all Web users use search engines of some kind for this purpose. However, existing search engines often do not return relevant information. The main focus of this paper is to improve search performance by using keywords and web pages which have been previously used or visited by other users. The Formal Concept Analysis (FCA) method has been adapted to maintain a concept map of keywords. This paper shows how both precision and recall has improved the specific-domain area, in which users can share the same knowledge.

Query-directed browsing of unstructured Web-texts using Formal Concept Analysis (FCA) confronts two problems. Firstly on-line Web-data is sometimes unstructured and any FCA-system must include additional mechanisms to structure input sources. Secondly many online collections are large and dynamic so a Web-robot must be used to automatically extract data. These issues are addressed in this paper. We report on the construction of a Web-based FCA system for browsing classified advertisements for real-estate properties 1. Real-estate advertisements were chosen because they are typical of semi-structured textual information sources accessible on the Web. Furthermore, the analysis of real-estate data using FCA is a classic example used in introductory courses on FCA. However, unlike the classic FCA real-estate example, whose input is a structure relational database, we automatically mine Web-based texts for their structure.KeywordsConcept LatticeFormal ContextFormal Concept AnalysisRental PriceRental PropertyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.