157,321 research outputs found
The ERA of FOLE: Foundation
This paper discusses the representation of ontologies in the first-order
logical environment FOLE (Kent 2013). An ontology defines the primitives with
which to model the knowledge resources for a community of discourse (Gruber
2009). These primitives, consisting of classes, relationships and properties,
are represented by the entity-relationship-attribute ERA data model (Chen
1976). An ontology uses formal axioms to constrain the interpretation of these
primitives. In short, an ontology specifies a logical theory. This paper is the
first in a series of three papers that provide a rigorous mathematical
representation for the ERA data model in particular, and ontologies in general,
within the first-order logical environment FOLE. The first two papers show how
FOLE represents the formalism and semantics of (many-sorted) first-order logic
in a classification form corresponding to ideas discussed in the Information
Flow Framework (IFF). In particular, this first paper provides a foundation
that connects elements of the ERA data model with components of the first-order
logical environment FOLE, and the second paper provides a superstructure that
extends FOLE to the formalisms of first-order logic. The third paper defines an
interpretation of FOLE in terms of the transformational passage, first
described in (Kent 2013), from the classification form of first-order logic to
an equivalent interpretation form, thereby defining the formalism and semantics
of first-order logical/relational database systems (Kent 2011). The FOLE
representation follows a conceptual structures approach, that is completely
compatible with formal concept analysis (Ganter and Wille 1999) and information
flow (Barwise and Seligman 1997)
Ontology-based knowledge representation of experiment metadata in biological data mining
According to the PubMed resource from the U.S. National Library of Medicine,
over 750,000 scientific articles have been published in the ~5000 biomedical journals
worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes
Measuring Software Process: A Systematic Mapping Study
Context: Measurement is essential to reach predictable performance and high capability processes. It provides
support for better understanding, evaluation, management, and control of the development process
and project, as well as the resulting product. It also enables organizations to improve and predict its process’s
performance, which places organizations in better positions to make appropriate decisions. Objective:
This study aims to understand the measurement of the software development process, to identify studies,
create a classification scheme based on the identified studies, and then to map such studies into the scheme
to answer the research questions. Method: Systematic mapping is the selected research methodology for this
study. Results: A total of 462 studies are included and classified into four topics with respect to their focus
and into three groups based on the publishing date. Five abstractions and 64 attributes were identified,
25 methods/models and 17 contexts were distinguished. Conclusion: capability and performance were the
most measured process attributes, while effort and performance were the most measured project attributes.
Goal Question Metric and Capability Maturity Model Integration were the main methods and models used
in the studies, whereas agile/lean development and small/medium-size enterprise were the most frequently
identified research contexts.Ministerio de EconomĂa y Competitividad TIN2013-46928-C3-3-RMinisterio de EconomĂa y Competitividad TIN2016-76956-C3-2- RMinisterio de EconomĂa y Competitividad TIN2015-71938-RED
The ERA of FOLE: Superstructure
This paper discusses the representation of ontologies in the first-order
logical environment FOLE (Kent 2013). An ontology defines the primitives with
which to model the knowledge resources for a community of discourse (Gruber
2009). These primitives, consisting of classes, relationships and properties,
are represented by the ERA (entity-relationship-attribute) data model (Chen
1976). An ontology uses formal axioms to constrain the interpretation of these
primitives. In short, an ontology specifies a logical theory. This paper is the
second in a series of three papers that provide a rigorous mathematical
representation for the ERA data model in particular, and ontologies in general,
within the first-order logical environment FOLE. The first two papers show how
FOLE represents the formalism and semantics of (many-sorted) first-order logic
in a classification form corresponding to ideas discussed in the Information
Flow Framework (IFF). In particular, the first paper (Kent 2015) provided a
"foundation" that connected elements of the ERA data model with components of
the first-order logical environment FOLE, and this second paper provides a
"superstructure" that extends FOLE to the formalisms of first-order logic. The
third paper will define an "interpretation" of FOLE in terms of the
transformational passage, first described in (Kent 2013), from the
classification form of first-order logic to an equivalent interpretation form,
thereby defining the formalism and semantics of first-order logical/relational
database systems (Kent 2011). The FOLE representation follows a conceptual
structures approach, that is completely compatible with Formal Concept Analysis
(Ganter and Wille 1999) and Information Flow (Barwise and Seligman 1997)
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