{"id":{"repo_id":"freiburg-diss","oai_identifier":"oai:freidok.uni-freiburg.de:28"},"canonical_url":"https://search.dev.ndltd.org/etd/freiburg-diss/oai:freidok.uni-freiburg.de:28","repository":{"repo_id":"freiburg-diss","name":"University of Freiburg","base_url":"https://freidok.uni-freiburg.de/oai/oai2.php"},"display":{"title":"Lernen und Interpretieren Strukturierter Dokumente - ein Qualitativer Ansatz","abstract":"Technical interpretation is usually done on documents having a certain kind of structure. In the overall
layout of such documents components belonging together are grouped and hence geometrically
separated from other parts. The rules used for arranging components on
documents depend on the document's domain, the author's cultural background and many other things.
In order to extract the meaning of such document parts, the creation rules have to be known.
Therefore, a graph representation of structured documents as well as domains of such documents
has been developed, whereby the nodes stand for the components and the edges represent weighted
qualitative spatial relations among nodes. These relations are derived from Allen's qualitative
relations among time intervals. The representation of domains can be derived automatically from
sets of labeled documents.
From a learned models, consistent scenarios can be extracted and then be visualized. This makes it
more easy for the user to examine learned models. Models of domains can then be used to assign
labels to unseen documents by means of a heuristic search for inexact subgraph isomorphisms
between a model graph and a document graph.","abstract_html":"Technical interpretation is usually done on documents having a certain kind of structure. In the overall <br>layout of such documents components belonging together are grouped and hence geometrically <br>separated from other parts. The rules used for arranging components on <br>documents depend on the document's domain, the author's cultural background and many other things. <br>In order to extract the meaning of such document parts, the creation rules have to be known. <br>Therefore, a graph representation of structured documents as well as domains of such documents <br>has been developed, whereby the nodes stand for the components and the edges represent weighted <br>qualitative spatial relations among nodes. These relations are derived from Allen's qualitative <br>relations among time intervals. The representation of domains can be derived automatically from <br>sets of labeled documents. <br>From a learned models, consistent scenarios can be extracted and then be visualized. This makes it <br>more easy for the user to examine learned models. Models of domains can then be used to assign <br>labels to unseen documents by means of a heuristic search for inexact subgraph isomorphisms <br>between a model graph and a document graph.","abstract_has_math":false,"creators":["Walischewski, Hanno"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Nebel, Bernhard"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T02:21:24Z","subjects":["machine learning","qualitative spatial representation","document layout interpretation"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://freidok.uni-freiburg.de/data/28","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Nebel, Bernhard"]},{"key":"dc:creator","label":"Author","values":["Walischewski, Hanno"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["DoctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["machine learning","qualitative spatial representation","document layout interpretation"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Technical interpretation is usually done on documents having a certain kind of structure. In the overall
layout of such documents components belonging together are grouped and hence geometrically
separated from other parts. The rules used for arranging components on
documents depend on the document's domain, the author's cultural background and many other things.
In order to extract the meaning of such document parts, the creation rules have to be known.
Therefore, a graph representation of structured documents as well as domains of such documents
has been developed, whereby the nodes stand for the components and the edges represent weighted
qualitative spatial relations among nodes. These relations are derived from Allen's qualitative
relations among time intervals. The representation of domains can be derived automatically from
sets of labeled documents.
From a learned models, consistent scenarios can be extracted and then be visualized. This makes it
more easy for the user to examine learned models. Models of domains can then be used to assign
labels to unseen documents by means of a heuristic search for inexact subgraph isomorphisms
between a model graph and a document graph."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Lernen und Interpretieren Strukturierter Dokumente - ein Qualitativer Ansatz"]}]}],"canonical_facts":{"dc:contributor":["Nebel, Bernhard"],"dc:creator":["Walischewski, Hanno"],"dc:description.abstract":["Technical interpretation is usually done on documents having a certain kind of structure. In the overall
layout of such documents components belonging together are grouped and hence geometrically
separated from other parts. The rules used for arranging components on
documents depend on the document's domain, the author's cultural background and many other things.
In order to extract the meaning of such document parts, the creation rules have to be known.
Therefore, a graph representation of structured documents as well as domains of such documents
has been developed, whereby the nodes stand for the components and the edges represent weighted
qualitative spatial relations among nodes. These relations are derived from Allen's qualitative
relations among time intervals. The representation of domains can be derived automatically from
sets of labeled documents.
From a learned models, consistent scenarios can be extracted and then be visualized. This makes it
more easy for the user to examine learned models. Models of domains can then be used to assign
labels to unseen documents by means of a heuristic search for inexact subgraph isomorphisms
between a model graph and a document graph."],"dc:format.medium":["application/pdf"],"dc:subject":["machine learning","qualitative spatial representation","document layout interpretation"],"dc:title":["Lernen und Interpretieren Strukturierter Dokumente - ein Qualitativer Ansatz"],"dc:type":["DoctoralThesis"]},"updated_at":"2026-07-24T02:21:24Z"}