Attribute Exploration of Discrete Temporal Transitions

Discrete temporal transitions occur in a variety of domains, but this work is mainly motivated by applications in molecular biology: explaining and analyzing observed transcriptome and proteome time series by literature and database knowledge. The starting point of a formal concept analysis model is presented. The objects of a formal context are states of the interesting entities, and the attributes are the variable properties defining the current state (e.g. observed presence or absence of proteins). Temporal transitions assign a relation to the objects, defined by deterministic or non-deterministic transition rules between sets of pre- and postconditions. This relation can be generalized to its transitive closure, i.e. states are related if one results from the other by a transition sequence of arbitrary length. The focus of the work is the adaptation of the attribute exploration algorithm to such a relational context, so that questions concerning temporal dependencies can be asked during the exploration process and be answered from the computed stem base. Results are given for the abstract example of a game and a small gene regulatory network relevant to a biomedical question.Comment: Only the email address and reference have been replace

Terrorist threat assessment with formal concept analysis.

The National Police Service Agency of the Netherlands developed a model to classify (potential) jihadists in four sequential phases of radicalism. The goal of the model is to signal the potential jihadist as early as possible to prevent him or her to enter the next phase. This model has up till now, never been used to actively find new subjects. In this paper, we use Formal Concept Analysis to extract and visualize potential jihadists in the different phases of radicalism from a large set of reports describing police observations. We employ Temporal Concept Analysis to visualize how a possible jihadist radicalizes over time. The combination of these instruments allows for easy decisionmaking on where and when to act.Formal concept analysis; Temporal concept analysis; Contextual attribute logic; Text mining; Terrorist threat assesment;

Concept discovery innovations in law enforcement: a perspective.

In the past decades, the amount of information available to law enforcement agencies has increased significantly. Most of this information is in textual form, however analyses have mainly focused on the structured data. In this paper, we give an overview of the concept discovery projects at the Amsterdam-Amstelland police where Formal Concept Analysis (FCA) is being used as text mining instrument. FCA is combined with statistical techniques such as Hidden Markov Models (HMM) and Emergent Self Organizing Maps (ESOM). The combination of this concept discovery and refinement technique with statistical techniques for analyzing high-dimensional data not only resulted in new insights but often in actual improvements of the investigation procedures.Formal concept analysis; Intelligence led policing; Knowledge discovery;

Time Aware Knowledge Extraction for Microblog Summarization on Twitter

Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.Comment: 33 pages, 10 figure

Concept Relation Discovery and Innovation Enabling Technology (CORDIET)

Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and Hidden Markov Models (HMM) as main artifacts in the analysis process. The user can define temporal, text mining and compound attributes. The text mining attributes are used to analyze the unstructured text in documents, the temporal attributes use these document's timestamps for analysis. The compound attributes are XML rules based on text mining and temporal attributes. The user can cluster objects with object-cluster rules and can chop the data in pieces with segmentation rules. The artifacts are optimized for efficient data analysis; object labels in the FCA lattice and ESOM map contain an URL on which the user can click to open the selected document

Adaptive Peircean decision aid project summary assessments.

This efforts objective was to identify and hybridize a suite of technologies enabling the development of predictive decision aids for use principally in combat environments but also in any complex information terrain. The technologies required included formal concept analysis for knowledge representation and information operations, Peircean reasoning to support hypothesis generation, Mill's's canons to begin defining information operators that support the first two technologies and co-evolutionary game theory to provide the environment/domain to assess predictions from the reasoning engines. The intended application domain is the IED problem because of its inherent evolutionary nature. While a fully functioning integrated algorithm was not achieved the hybridization and demonstration of the technologies was accomplished and demonstration of utility provided for a number of ancillary queries