Hierarchy as a new data type for qualitative variables

Serguei Levachkine, Adolfo Guzmán-Arenas

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

The concept of hierarchy has being explored by the computer science communities during last few decades. Relatively simple hierarchical structures found extensive use in such diverse areas as data modeling, information retrieval, knowledge representation and processing, natural language, pattern recognition, and so on. Recent investigations in information retrieval and data integration have emphasized the use of ontologies and semantic similarity functions as a mechanism for comparing objects that can be retrieved or integrated across heterogeneous repositories. Hierarchies being a simpler, albeit very useful, version of ontologies, can perfectly contribute to model solutions of these problems. Present paper aims to illustrate above thesis by discussing a simple method of information retrieval that uses a hierarchical qualitative data organization. Its main goal is to retrieve objects from any database that are just close to a desired item and control the retrieval process up to a given error, called herein confusion. For doing this, we define a semantic dissimilarity (confusion) between objects to be retrieved as well as introduce a calculus of predicates based on the confusion function. © 2006 Elsevier Ltd. All rights reserved.
Original languageAmerican English
Pages (from-to)899-910
Number of pages807
JournalExpert Systems with Applications
DOIs
StatePublished - 1 Apr 2007

Fingerprint

Information Storage and Retrieval
Information retrieval
Semantics
Ontology
Natural Language Processing
Data integration
Calculi
Knowledge representation
Computer science
Pattern recognition
Data structures
Databases
Processing

Cite this

@article{3fbf558cf6a04aeca67c36a9744f02db,
title = "Hierarchy as a new data type for qualitative variables",
abstract = "The concept of hierarchy has being explored by the computer science communities during last few decades. Relatively simple hierarchical structures found extensive use in such diverse areas as data modeling, information retrieval, knowledge representation and processing, natural language, pattern recognition, and so on. Recent investigations in information retrieval and data integration have emphasized the use of ontologies and semantic similarity functions as a mechanism for comparing objects that can be retrieved or integrated across heterogeneous repositories. Hierarchies being a simpler, albeit very useful, version of ontologies, can perfectly contribute to model solutions of these problems. Present paper aims to illustrate above thesis by discussing a simple method of information retrieval that uses a hierarchical qualitative data organization. Its main goal is to retrieve objects from any database that are just close to a desired item and control the retrieval process up to a given error, called herein confusion. For doing this, we define a semantic dissimilarity (confusion) between objects to be retrieved as well as introduce a calculus of predicates based on the confusion function. {\circledC} 2006 Elsevier Ltd. All rights reserved.",
author = "Serguei Levachkine and Adolfo Guzm{\'a}n-Arenas",
year = "2007",
month = "4",
day = "1",
doi = "10.1016/j.eswa.2006.01.024",
language = "American English",
pages = "899--910",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Ltd",

}

Hierarchy as a new data type for qualitative variables. / Levachkine, Serguei; Guzmán-Arenas, Adolfo.

In: Expert Systems with Applications, 01.04.2007, p. 899-910.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Hierarchy as a new data type for qualitative variables

AU - Levachkine, Serguei

AU - Guzmán-Arenas, Adolfo

PY - 2007/4/1

Y1 - 2007/4/1

N2 - The concept of hierarchy has being explored by the computer science communities during last few decades. Relatively simple hierarchical structures found extensive use in such diverse areas as data modeling, information retrieval, knowledge representation and processing, natural language, pattern recognition, and so on. Recent investigations in information retrieval and data integration have emphasized the use of ontologies and semantic similarity functions as a mechanism for comparing objects that can be retrieved or integrated across heterogeneous repositories. Hierarchies being a simpler, albeit very useful, version of ontologies, can perfectly contribute to model solutions of these problems. Present paper aims to illustrate above thesis by discussing a simple method of information retrieval that uses a hierarchical qualitative data organization. Its main goal is to retrieve objects from any database that are just close to a desired item and control the retrieval process up to a given error, called herein confusion. For doing this, we define a semantic dissimilarity (confusion) between objects to be retrieved as well as introduce a calculus of predicates based on the confusion function. © 2006 Elsevier Ltd. All rights reserved.

AB - The concept of hierarchy has being explored by the computer science communities during last few decades. Relatively simple hierarchical structures found extensive use in such diverse areas as data modeling, information retrieval, knowledge representation and processing, natural language, pattern recognition, and so on. Recent investigations in information retrieval and data integration have emphasized the use of ontologies and semantic similarity functions as a mechanism for comparing objects that can be retrieved or integrated across heterogeneous repositories. Hierarchies being a simpler, albeit very useful, version of ontologies, can perfectly contribute to model solutions of these problems. Present paper aims to illustrate above thesis by discussing a simple method of information retrieval that uses a hierarchical qualitative data organization. Its main goal is to retrieve objects from any database that are just close to a desired item and control the retrieval process up to a given error, called herein confusion. For doing this, we define a semantic dissimilarity (confusion) between objects to be retrieved as well as introduce a calculus of predicates based on the confusion function. © 2006 Elsevier Ltd. All rights reserved.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=33751080615&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=33751080615&origin=inward

U2 - 10.1016/j.eswa.2006.01.024

DO - 10.1016/j.eswa.2006.01.024

M3 - Article

SP - 899

EP - 910

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -