Lernen und Interpretieren Strukturierter Dokumente - ein Qualitativer Ansatz
type: CreativeWork
description: 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: 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.
author: Walischewski, Hanno
keywords: machine learning, qualitative spatial representation, document layout interpretation
date modified: 2026-07-24T02:21:24Z
provider: University of Freiburg
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Freiburg