1,208,253 research outputs found
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)
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)
The FOLE Table
This paper continues the discussion of the representation of ontologies in
the first-order logical environment FOLE. According to Gruber, an ontology
defines the primitives with which to model the knowledge resources for a
community of discourse. These primitives, consisting of classes, relationships
and properties, are represented by the entity-relationship-attribute ERA data
model of Chen. An ontology uses formal axioms to constrain the interpretation
of these primitives. In short, an ontology specifies a logical theory. A series
of three papers by the author provide a rigorous mathematical representation
for the ERA data model in particular, and ontologies in general, within FOLE.
The first two papers, which provide a foundation and superstructure for FOLE,
represent the formalism and semantics of (many-sorted) first-order logic in a
classification form corresponding to ideas discussed in the Information Flow
Framework (IFF). 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. Two papers will provide a precise mathematical basis for FOLE
interpretation: the current paper develops the notion of a FOLE relational
table following the relational model of Codd, and a follow-up paper will
develop the notion of a FOLE relational database. Both of these papers expand
on material found in the paper (Kent, 2011). Although the classification form
follows the entity-relationship-attribute data model of Chen, the
interpretation form follows the relational data model of Codd. In general, the
FOLE representation uses a conceptual structures approach, that is completely
compatible with formal concept analysis and information flow.Comment: 48 pages, 21 figures, 9 tables, submitted to T.A.C. for review in
August 201
The Mirror MMDBMS architecture
Handling large collections of digitized multimedia data, usually referred to as multimedia digital libraries, is a major challenge for information technology. The Mirror DBMS is a research database system that is developed to better understand the kind of data management that is required in the context of multimedia digital libraries (see also URL http://www.cs.utwente.nl/~arjen/mmdb.html). Its main features are an integrated approach to both content management and (traditional) structured data management, and the implementation of an extensible object-oriented logical data model on a binary relational physical data model. The focus of this work is aimed at design for scalability
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