280,310 research outputs found
Curbing domestic violence: instantiating C-K theory with formal concept analysis and emergent self organizing maps.
In this paper we propose a human-centered process for knowledge discovery from unstructured text that makes use of Formal Concept Analysis and Emergent Self Organizing Maps. The knowledge discovery process is conceptualized and interpreted as successive iterations through the Concept-Knowledge (C-K) theory design square. To illustrate its effectiveness, we report on a real-life case study of using the process at the Amsterdam-Amstelland police in the Netherlands aimed at distilling concepts to identify domestic violence from the unstructured text in actual police reports. The case study allows us to show how the process was not only able to uncover the nature of a phenomenon such as domestic violence, but also enabled analysts to identify many types of anomalies in the practice of policing. We will illustrate how the insights obtained from this exercise resulted in major improvements in the management of domestic violence cases.Formal concept analysis; Emergent self organizing map; C-K theory; Text mining; Actionable knowledge discovery; Domestic violence;
Hidden Citations Obscure True Impact in Science
References, the mechanism scientists rely on to signal previous knowledge,
lately have turned into widely used and misused measures of scientific impact.
Yet, when a discovery becomes common knowledge, citations suffer from
obliteration by incorporation. This leads to the concept of hidden citation,
representing a clear textual credit to a discovery without a reference to the
publication embodying it. Here, we rely on unsupervised interpretable machine
learning applied to the full text of each paper to systematically identify
hidden citations. We find that for influential discoveries hidden citations
outnumber citation counts, emerging regardless of publishing venue and
discipline. We show that the prevalence of hidden citations is not driven by
citation counts, but rather by the degree of the discourse on the topic within
the text of the manuscripts, indicating that the more discussed is a discovery,
the less visible it is to standard bibliometric analysis. Hidden citations
indicate that bibliometric measures offer a limited perspective on quantifying
the true impact of a discovery, raising the need to extract knowledge from the
full text of the scientific corpus
Quantum Structure in Cognition, Origins, Developments, Successes and Expectations
We provide an overview of the results we have attained in the last decade on
the identification of quantum structures in cognition and, more specifically,
in the formalization and representation of natural concepts. We firstly discuss
the quantum foundational reasons that led us to investigate the mechanisms of
formation and combination of concepts in human reasoning, starting from the
empirically observed deviations from classical logical and probabilistic
structures. We then develop our quantum-theoretic perspective in Fock space
which allows successful modeling of various sets of cognitive experiments
collected by different scientists, including ourselves. In addition, we
formulate a unified explanatory hypothesis for the presence of quantum
structures in cognitive processes, and discuss our recent discovery of further
quantum aspects in concept combinations, namely, 'entanglement' and
'indistinguishability'. We finally illustrate perspectives for future research.Comment: 25 pages. arXiv admin note: text overlap with arXiv:1412.870
Advancing FCA Workflow in FCART System for Knowledge Discovery in Quantitative Data
AbstractWe describe new features in FCART software system, an integrated environment for knowledge and data engineers with a set of research tools based on Formal Concept Analysis. The system is intended for knowledge discovery from various data sources, including structured quantitative data and text collections. Final version of data transformation from external data source into concept lattice is considered. We introduce new version of local data storage, query language for conceptual scaling of data snapshots as multi-valued contexts, and new tools for working with formal concepts
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
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