Concept Analysis on Structured, Multi-valued and Incomplete Data (original) (raw)
Related papers
Tree-Based Classification Approach for Dealing With Complex Knowledge in Natural Sciences
. In many fields dependant upon complex observation, the structuring, depiction and treatment of knowledge can be of great complexity. For example in Systematics, the scientific discipline that investigates bio-diversity, the descriptions of specimens are often highly structured (composite objects, taxonomic attributes), noisy (erroneous or unknown data), and polymorphous (variable or imprecise data). In this paper, we present IKBS, an Iterative Knowledge Base System for dealing with such complex phenomena. The originality of this system is to implement the scientific method in biology: experimenting (learning rules from examples) and testing (identifying new individuals, improving the initial model and descriptions). This methodology is applied in the following ways in IKBS: 1 - Knowledge is acquired through a descriptive model that suits the semantic demand of experts. 2 - Knowledge is processed with an algorithm derived from C4.5 in order to take into account structured knowledge...
Exploring Temporal Data Using Relational Concept Analysis: An Application to Hydroecology
2016
This paper presents an approach for mining temporal data, based on Relational Concept Analysis (RCA), that has been developed for a real world application. Our data are sequential samples of biological and physico-chemical parameters taken from watercourses. Our aim is to reveal meaningful relations between the two types of parameters. To this end, we propose a comprehensive temporal data mining process starting by using RCA on an ad hoc temporal data model. The results of RCA are converted into closed partially ordered patterns to provide experts with a synthetic representation of the information contained in the lattice family. Patterns can also be filtered with various measures, exploiting the notion of temporal objects. The process is assessed through some quantitative statistics and qualitative interpretations resulting from experiments carried out on hydroecological datasets.
A Proposal to Extend Concept Mapping to Concept Lattices for Representing Biology
Textbook biology knowledge is being represented as triples using concept maps. These triples can be extended to create concept lattices by representing objects and attributes in relation. By focusing on the nature of semantic relations, concept neighbourhood lattices can be generated for dependencies, associations. By representing the changes in the attributes of objects in time, concept lattices of dynamic propositions can be generated. As the knowledge base for the study is textbook biology, this research on using concept lattices in school, college biology education can be further developed with focusing on teaching learning, cognitive assessment
A knowledge representation view on biomedical structure and function
Proceedings Amia Annual Symposium Amia Symposium, 2002
In biomedical ontologies, structural and functional considerations are of outstanding importance, and concepts which belong to these two categories are highly interdependent. At the representational level both axes must be clearly kept separate in order to support disciplined ontology engineering. Furthermore, the biaxial organization ofphysical structure (both by a taxonomic and partonomic order) entails intricate patterns of inference. We here propose a layered encoding of taxonomic, partonomic and functional aspects ofbiomedical concepts using description logics.
2007
A key role for ontologies in bioinformatics is their use as a standardised, structured terminology, partic- ularly to annotate the genes in a genome with func- tional and other properties. Since the output of many genome-scale experiments results in gene sets it is nat- ural to ask if they share common function. A stan- dard approach is to apply a statistical test for over- representation of ontological annotation, often within the Gene Ontology. In this paper we propose an al- ternative to the standard approach that avoids prob- lems in over-representation analysis due to statisti- cal dependencies between ontology categories. We use a feature construction approach to pre-process Gene Ontology annotation of gene sets and incorpo- rate these features as input to a standard supervised machine learning algorithm. Our approach is shown to allow the straightforward use of an ontology in the context of data sourced from multiple experiments to learn a classifier predicting gene function ...
Concept Cell Model for Knowledge Representation
International Journal of Information Acquisition, 2004
This paper proposes a unified knowledge model establishing a core structure of knowledge representation in natural and artificial intelligence systems. The Concept Cell Model proposes the use of acyclic lattices to model a concept formed from a knowledge network of simpler concepts. Declarative and procedural knowledge are explicitly defined as the time-invariant and time-variant relationship of concepts. Examples of a Restaurant Servicing Concept Cell and an extended Shopping Complex Concept Cell are used to demonstrate the functionality of this model. Major existing theoretic and engineering Ontology knowledge schools are compared under this framework.
Formal Concept Analysis for Data Mining: Theoretical and Practical Approaches
2006 IEEE International Conference on Engineering of Intelligent Systems, 2006
Gerais (UFMG)-jpaulogdcc.ufmg.br 2UNA University (UNA)-luiszarateguol.com.br 3Federal University of Minas Gerais (UFMG)-nvieiragdcc.ufmg.br Belo Horizonte, Minas Gerais, Brasil TABLE I Abstract-Knowledge Discovery in Databases (KDD) is the most widely known process with the purpose of knowledge extraction. Formal Concept Analysis (FCA) is proposed here (1) ALLIGATOR; (2) FROG; (3) HUMAN; (4) MONKEY; (5) OWL; (6) SHARK. as an alternative step in KDD process, due to its capacity ATTRIBUTES (COLUMNS HEADER): (A) AQUATIC; (B) TERRESTRIAL; of generating diagrams that facilitate data representation and (C) BRANCHIAL BREATHING; (D) PULMONARY BREATHING; (E) HAIR; analysis. FCA can perform the task of Data Mining (DM), (F)FEATHERS; (G)MAMMARY GLANDS; (H) REASONING. supporting users in knowledge management. Both theoretical and practical aspects are presented here. a b c d e f g h 1_ x _ x
Managing Complex Knowledge in Natural Sciences
Lecture Notes in Computer Science, 1999
In many fields dependant upon complex observation, the structuring, depiction and treatment of knowledge can be of great complexity. For example in Systematics, the scientific discipline that investigates biodiversity , the descriptions of specimens are often highly structured (composite objects, taxonomic attributes), noisy (erroneous or unknown data), and polymorphous (variable or imprecise data). In this paper, we present IKBS, an Iterative Knowledge Base System for dealing with such complex phenomena. The originality of this system is to implement the scientific method in biology: experimenting (learning rules from examples) and testing (identifying new individuals, improving the initial model and descriptions). This methodology is applied in the following ways in IKBS: Knowledge is acquired through a descriptive model that suits the semantic demand of experts, 1. Knowledge is processed with an algorithm derived from C4.5 in order to take into account structured knowledge introduced in the previous descriptive model of the domain, 2. Knowledge is refined through the use of an iterative process to evaluate the robustness of the descriptive model and descriptions. 3. The IKBS system is presented here as a life science application facilitating the identification of coral specimens of the family Pocilloporidae.
A New Model to Compute the Information Content of Concepts from Taxonomic Knowledge
2012
The Information Content (IC) of a concept quantifies the amount of information it provides when appearing in a context. In the past, IC used to be computed as a function of concept appearance probabilities in corpora, but corpora-dependency and data sparseness hampered results. Recently, some authors tried to overcome previous approaches, estimating IC from the knowledge modeled in an ontology. In this paper, we develop this idea, by proposing a new model to compute the IC of a concept exploiting the taxonomic knowledge modeled in an ontology. In comparison with related works, our proposal aims to better capture semantic evidences found in the ontology. To test our approach, we have applied it to well-known semantic similarity measures, which were evaluated using standard benchmarks. Results show that the use of our model produces, in most cases, more accurate similarity estimations than related works.