Constantinos Orphanides

FcaBedrock, a formal context creator

FcaBedrock employs user-guided automation to convert c.s.v. data sets into Burmeister .cxt and FIMI .dat context files for FCA.

Visualising Computational Intelligence Through Converting Data into Formal Concepts

Formal Concept Analysis (FCA) is an emerging data technology that complements collective intelligence such as that identified in the Semantic Web by visualising the hidden meaning in disparate and distributed data. The paper demonstrates the discovery of these novel semantics through a set of FCA open source software tools FcaBedrock and In-Close that were developed by the authors. These tools add computational intelligence by converting data into a Boolean form called a Formal Context, prepare this data for analysis by creating focused and noise-free sub-Contexts and then analyse the prepared data using a visualisation called a Concept Lattice. The Formal Concepts thus visualised highlight how data itself contains meaning, and how FCA tools thereby extract data’s inherent semantics. The paper describes how this will be further developed in a project called CUBIST, to provide in-data-warehouse visual analytics for RDF-based triple stores.

A conceptual approach to gene expression analysis enhanced by visual analytics

The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts.

Knowledge discovery through creating formal contexts

Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called formal concept analysis (FCA). This paper describes a tool called FcaBedrock that converts data into formal contexts for FCA. This paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Examples are given of how formal contexts can be created using FcaBedrock and then analysed for knowledge discovery, using real datasets. Creating formal contexts using FcaBedrock is shown to be straightforward and versatile. Large datasets are easily converted into a standard FCA format.

Visual Analysis of a Large and Noisy Dataset

Visual analysis has witnessed a growing acceptance as a method of scientific inquiry in the research community. It is used in qualitative and mixed research methods. Even so, visual data analysis is likely to produce biased results when used in analysing a large and noisy dataset. This can be evident when a data analyst is not able to holistically explore, all the values associated with the objects of interest in a dataset. Consequently, the data analyst may assess inconsistent data as consistent when contradiction associated with the data is not visualised. This work identifies incomplete analysis as a challenge in the visual data analysis of a large and noisy dataset. It considers Formal Concept Analysis FCA tools and techniques and prescribes the mining and visualisation of Incomplete or Inconsistent Data IID when dealing with a large and noisy dataset. It presents an automated approach for transforming IID from a noisy context whose objects are associated with mutually exclusive many-valued attributes, to a formal context.

Discovering Knowledge in Data Using Formal Concept Analysis

Formal Concept Analysis FCA has been successfully applied to data in a number of problem domains. However, its use has tended to be on an ad hoc, bespoke basis, relying on FCA experts working closely with domain experts and requiring the production of specialised FCA software for the data analysis. The availability of generalised tools and techniques, that might allow FCA to be applied to data more widely, is limited. Two important issues provide barriers: raw data is not normally in a form suitable for FCA and requires undergoing a process of transformation to make it suitable, and even when converted into a suitable form for FCA, real data sets tend to produce a large number of results that can be difficult to manage and interpret. This article describes how some open-source tools and techniques have been developed and used to address these issues and make FCA more widely available and applicable. Three examples of real data sets, and real problems related to them, are used to illustrate the application of the tools and techniques and demonstrate how FCA can be used as a semantic technology to discover knowledge. Furthermore, it is shown how these tools and techniques enable FCA to deliver a visual and intuitive means of mining large data sets for association and implication rules that complements the semantic analysis. In fact, it transpires that FCA reveals hidden meaning in data that can then be examined in more detail using an FCA approach to traditional data mining methods.

Knowledge Discovery through Creating Formal Contexts

Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called Formal Concept Analysis (FCA). This paper describes a tool called FcaBedrock that converts data into formal contexts for FCA. The paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Creating formal contexts using FcaBedrock is shown to be straightforward and versatile. Large data sets are easily converted into a standard FCA format.

Analyzing Crowd-Sourced Information and Social Media for Crisis Management

The analysis of potentially large volumes of crowd-sourced and social media data is central to meeting the requirements of the ATHENA project. Here, we discuss the various stages of the pipeline process we have developed, including acquisition of the data, analysis, aggregation, filtering, and structuring. We highlight the challenges involved when working with unstructured, noisy data from sources such as Twitter, and describe the crisis taxonomies that have been developed to support the tasks and enable concept extraction. State-of-the-art techniques such as formal concept analysis and machine learning are used to create a range of capabilities including concept drill down, sentiment analysis, credibility assessment, and assignment of priority. We ground many of these techniques using results obtained from a set of tweets which emerged from the Colorado wildfires of 2012 in order to demonstrate the applicability of our work to real crisis scenarios.

Discovering Knowledge in Online Drug Transactions Using Conceptual Graphs and Formal Concept Analysis

In this short paper, we describe a conceptual approach in which Conceptual Graphs (CGs) and Formal Concept Analysis (FCA) are employed towards knowledge discovery in online drug transactions. The transactions are acquired by performing Named-Entity Recognition (NER) on documents crawled from online public sources such as Twitter and Instagram, and are structured based on a CG ontology created to model such transactions. The drug transactions are then visualized using FCA as the knowledge discovery method.