TY - JOUR
T1 - Hierarchy as a new data type for qualitative variables
AU - Levachkine, Serguei
AU - Guzmán-Arenas, Adolfo
N1 - Funding Information:
Helpful discussions were held with Prof. Victor Alexandrov, SPIIRAS-Russia, Dr.Jesus Olivares, and Mr.Gilberto Martinez, CIC-IPN. Work herein described was partially supported by NSF-CONACYT Grant 32973-A and Project CGPI-IPN 20010778. The authors have a SNI National Scientist Award.
PY - 2007/4
Y1 - 2007/4
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.
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.
KW - Approximate queries
KW - Confusion
KW - Hierarchy
KW - Knowledge representation
KW - Ontology
UR - http://www.scopus.com/inward/record.url?scp=33751080615&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2006.01.024
DO - 10.1016/j.eswa.2006.01.024
M3 - Artículo
SN - 0957-4174
VL - 32
SP - 899
EP - 910
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 3
ER -