The Experts below are selected from a list of 548262 Experts worldwide ranked by ideXlab platform
Francisco M Couto - One of the best experts on this subject based on the ideXlab platform.
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Automatic Background Knowledge selection for matching biomedical ontologies
PLoS ONE, 2014Co-Authors: Francisco M Couto, Isabel F. Cruz, Emanuel Santos, Daniel Faria, Catia PesquitaAbstract:Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of Background Knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the Background Knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting Background Knowledge sources for any given ontologies to match. This methodology measures the usefulness of each Background Knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of Background Knowledge that led to the highest improvements over the baseline alignment (i.e., without Background Knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on Background Knowledge.
Daniel Faria - One of the best experts on this subject based on the ideXlab platform.
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Automatic Background Knowledge selection for matching biomedical ontologies
PLoS ONE, 2014Co-Authors: Francisco M Couto, Isabel F. Cruz, Emanuel Santos, Daniel Faria, Catia PesquitaAbstract:Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of Background Knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the Background Knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting Background Knowledge sources for any given ontologies to match. This methodology measures the usefulness of each Background Knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of Background Knowledge that led to the highest improvements over the baseline alignment (i.e., without Background Knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on Background Knowledge.
Lawrence B. Holder - One of the best experts on this subject based on the ideXlab platform.
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Substructure Discovery Using Minimum Description Length and Background Knowledge
arXiv: Artificial Intelligence, 1994Co-Authors: Diane J. Cook, Lawrence B. HolderAbstract:The ability to identify interesting and repetitive substructures is an essential component to discovering Knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description length principle, other Background Knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered substructures with their corresponding value.
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substructure discovery using minimum description length and Background Knowledge
Journal of Artificial Intelligence Research, 1993Co-Authors: Diane J. Cook, Lawrence B. HolderAbstract:The ability to identify interesting and repetitive substructures is an essential component to discovering Knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimumdescription length principle, other Background Knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain.
Danielle S Mcnamara - One of the best experts on this subject based on the ideXlab platform.
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comprehension in a scenario based assessment domain and topic specific Background Knowledge
Grantee Submission, 2018Co-Authors: Kathryn S Mccarthy, Tricia A Guerrero, Kevin M Kent, Laura K Allen, Danielle S Mcnamara, Szufu Chao, Jonathan Steinberg, Tenaha Oreilly, John SabatiniAbstract:Background Knowledge is a strong predictor of reading comprehension, yet little is known about how different types of Background Knowledge affect comprehension. The study investigated the impacts o...
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are good texts always better interactions of text coherence Background Knowledge and levels of understanding in learning from text
Cognition and Instruction, 1996Co-Authors: Danielle S Mcnamara, Eileen Kintsch, Nancy Butler Songer, Walter KintschAbstract:Two experiments, theoretically motivated by the construction-integration model of text comprehension (W. Kintsch, 1988), investigated the role of text coherence in the comprehension of science texts. In Experiment 1, junior high school students' comprehension of one of three versions of a biology text was examined via free recall, written questions, and a key-word sorting task. This study demonstrates advantages for globally coherent text and for more explanatory text. In Experiment 2, interactions among local and global text coherence, readers' Background Knowledge, and levels of understanding were examined. Using the same methods as in Experiment 1, we examined students' comprehension of one of four versions of a text, orthogonally varying local and global coherence. We found that readers who know little about the domain of the text benefit from a coherent text, whereas high-Knowledge readers benefit from a minimally coherent text. We argue that the poorly written text forces the Knowledgeable readers t...
Catia Pesquita - One of the best experts on this subject based on the ideXlab platform.
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Automatic Background Knowledge selection for matching biomedical ontologies
PLoS ONE, 2014Co-Authors: Francisco M Couto, Isabel F. Cruz, Emanuel Santos, Daniel Faria, Catia PesquitaAbstract:Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of Background Knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the Background Knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting Background Knowledge sources for any given ontologies to match. This methodology measures the usefulness of each Background Knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of Background Knowledge that led to the highest improvements over the baseline alignment (i.e., without Background Knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on Background Knowledge.
