Extraction of information from semi-structured or unstructured documents, such as Web pages, is a useful yet complex task. Research has demonstrated that ontologies may be used to achieve a high degree of accuracy in data extraction while maintaining resiliency in the face of document changes. Ontologies do not, however, diminish the complexity of a data-extraction system. As research in the field progresses, the need for a modular data-extraction system that de-couples the various functional processes involved continues to grow.
In this thesis we propose a framework for such a system. The nature of the framework allows new algorithms and ideas to be incorporated into a data extraction system without requiring wholesale rewrites of a large part of the system’s source code. It also allows researchers to focus their attention on parts of the system relevant to their research without having to worry about introducing incompatibilities with the remaining components. We demonstrate the value of the framework by providing a implementation of it, and we show that our implementation is capable of achieving accuracy in its extraction results comparable to that achieved by the legacy BYU-Ontos data-extraction system. We also suggest alternate ways in which the framework may be extended and implemented, and we supply documentation on the framework for future use by data-extraction researchers.
","abstract_html":"<p>Extraction of information from semi-structured or unstructured documents, such as Web pages, is a useful yet complex task. Research has demonstrated that ontologies may be used to achieve a high degree of accuracy in data extraction while maintaining resiliency in the face of document changes. Ontologies do not, however, diminish the complexity of a data-extraction system. As research in the field progresses, the need for a modular data-extraction system that de-couples the various functional processes involved continues to grow.</p> <p>In this thesis we propose a framework for such a system. The nature of the framework allows new algorithms and ideas to be incorporated into a data extraction system without requiring wholesale rewrites of a large part of the system’s source code. It also allows researchers to focus their attention on parts of the system relevant to their research without having to worry about introducing incompatibilities with the remaining components. We demonstrate the value of the framework by providing a implementation of it, and we show that our implementation is capable of achieving accuracy in its extraction results comparable to that achieved by the legacy BYU-Ontos data-extraction system. We also suggest alternate ways in which the framework may be extended and implemented, and we supply documentation on the framework for future use by data-extraction researchers.</p>","abstract_has_math":false,"creators":["Wessman, Alan E."],"institution":"Brigham Young University - Provo","degree_name":"MS","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T01:27:25Z","subjects":["data extraction","ontology","framework","extraction plan","inference","conceptual modeling","data frame","information extraction","OSMX","OSM","Ontos","OntosEngine","OntologyEditor","Computer Sciences"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsarchive.byu.edu/etd/238","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Wessman, Alan E."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2005-01-26T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["Brigham Young University - Provo"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["data extraction","ontology","framework","extraction plan","inference","conceptual modeling","data frame","information extraction","OSMX","OSM","Ontos","OntosEngine","OntologyEditor","Computer Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsarchive.byu.edu/etd/238","https://scholarsarchive.byu.edu/context/etd/article/1237/viewcontent/ETD_CISOPTR_258.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Physical and Mathematical Sciences; Computer Science"]},{"key":"dc:description.abstract","label":"Abstract","values":["Extraction of information from semi-structured or unstructured documents, such as Web pages, is a useful yet complex task. Research has demonstrated that ontologies may be used to achieve a high degree of accuracy in data extraction while maintaining resiliency in the face of document changes. Ontologies do not, however, diminish the complexity of a data-extraction system. As research in the field progresses, the need for a modular data-extraction system that de-couples the various functional processes involved continues to grow.
In this thesis we propose a framework for such a system. The nature of the framework allows new algorithms and ideas to be incorporated into a data extraction system without requiring wholesale rewrites of a large part of the system’s source code. It also allows researchers to focus their attention on parts of the system relevant to their research without having to worry about introducing incompatibilities with the remaining components. We demonstrate the value of the framework by providing a implementation of it, and we show that our implementation is capable of achieving accuracy in its extraction results comparable to that achieved by the legacy BYU-Ontos data-extraction system. We also suggest alternate ways in which the framework may be extended and implemented, and we supply documentation on the framework for future use by data-extraction researchers.
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In this thesis we propose a framework for such a system. The nature of the framework allows new algorithms and ideas to be incorporated into a data extraction system without requiring wholesale rewrites of a large part of the system’s source code. It also allows researchers to focus their attention on parts of the system relevant to their research without having to worry about introducing incompatibilities with the remaining components. We demonstrate the value of the framework by providing a implementation of it, and we show that our implementation is capable of achieving accuracy in its extraction results comparable to that achieved by the legacy BYU-Ontos data-extraction system. We also suggest alternate ways in which the framework may be extended and implemented, and we supply documentation on the framework for future use by data-extraction researchers.
"],"dc:format":["application:pdf"],"dc:identifier":["https://scholarsarchive.byu.edu/etd/238","https://scholarsarchive.byu.edu/context/etd/article/1237/viewcontent/ETD_CISOPTR_258.pdf"],"dc:language":["English"],"dc:publisher":["Brigham Young University - Provo"],"dc:source":["Brigham Young University - Provo"],"dc:subject":["data extraction","ontology","framework","extraction plan","inference","conceptual modeling","data frame","information extraction","OSMX","OSM","Ontos","OntosEngine","OntologyEditor","Computer Sciences"],"dc:title":["A Framework for Extraction Plans and Heuristics in an Ontology-Based Data-Extraction System"],"dc:type":["Thesis"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T01:27:25Z"}