Towards Ontology Generation from Tables

Tijerino, Yuri A.; Embley, David W.; Lonsdale, Deryle; Ding, Yihong; Nagy, George

doi:10.1007/s11280-005-0360-8

Cited by 109 publications

(47 citation statements)

References 25 publications

(42 reference statements)

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“…It is possible, however, that such a knowledge base can be assembled from studying a large collection of diverse but related tables. This, in fact, is one of our long term research objectives [9][10][11].…”

Section: External Informationmentioning

confidence: 97%

Notes on Contemporary Table Recognition

Embley

Lopresti

Nagy

2006

Document Analysis Systems VII

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Abstract. The shift of interest to web tables in HTML and PDF files, coupled with the incorporation of table analysis and conversion routines in commercial desktop document processing software, are likely to turn table recognition into more of a systems than an algorithmic issue. We illustrate the transition by some actual examples of web table conversion. We then suggest that the appropriate target format for table analysis, whether performed by conventional customized programs or by off-theshelf software, is a representation based on the abstract table introduced by X. Wang in 1996. We show that the Wang model is adequate for some useful tasks that prove elusive for less explicit representations, and outline our plans to develop a semi-automated table processing system to demonstrate this approach. Screen-snaphots of a prototype tool to allow table mark-up in the style of Wang are also presented.

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Section: External Informationmentioning

confidence: 97%

Notes on Contemporary Table Recognition

Embley

Lopresti

Nagy

2006

Document Analysis Systems VII

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“…Since then mainstream table recognition has progressed from scanned paper tables to computer-generated HTML and PDF tables. All of this work has been part of a long-standing and most enjoyable collaboration with Dave Embley (BYU), Sharad Seth (UNL), Moorthy Krishnamoorthy (RPI) and Dan Lopresti (Lehigh), often under the aegis of TANGO [27]. We have written far too many surveys and reports, especially considering how often our views have shifted, so rather than reciting progressive steps I just list some articles of faith (for which I take sole responsibility and which I may retract next year).…”

Section: Tablesmentioning

confidence: 99%

Estimation, Learning, and Adaptation: Systems That Improve with Use

Nagy¹

2012

Lecture Notes in Computer Science

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Abstract. The accuracy of automated classification (labeling) of single patterns, especially printed, hand-printed, or handwritten characters, has leveled off. Further gains in accuracy require classifying sequences of patterns. Linguistic context, already widely used, relies on 1-D lexical and syntactic constraints. Style-constrained classification exploits the shape-similarity of sets of same-source (isogenous) characters of either the same or different classes. For understanding tables and forms, 2-D structural and relational constraints are necessary. Applications of pattern recognition that do not exceed the limits of human senses and cognition can benefit from green interaction wherein operator corrections are recycled to the classifier.

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“…The notion of meaning appears to require some kind of shared understanding of a topic. Since many current attempts to formalize meaning are focused on ontologies, ontological engineering may play a part in the extraction of concepts from documents [153].…”

Section: Interoperabilitymentioning

confidence: 99%

Adaptive and Interactive Approaches to Document Analysis

Nagy¹,

Veeramachaneni

2008

Machine Learning in Document Analysis and Recognition

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Summary. This chapter explores three aspects of learning in document analysis: (1) field classification, (2) interactive recognition, and (3) portable and networked applications. Context in document classification conventionally refers to language context, i.e., deterministic or statistical constraints on the sequence of letters in syllables or words, and on the sequence of words in phrases or sentences. We show how to exploit other types of statistical dependence, specifically the dependence between the shape features of several patterns due to the common source of the patterns within a field or a document. This type of dependence leads to field classification, where the features of some patterns may reveal useful information about the features of other patterns from the same source but not necessarily from the same class. We explore the relationship between field classification and the older concepts of unsupervised learning and adaptation. Human interaction is often more effective interspersed with algorithmic processes than only before or after the automated parts of the process. We develop a taxonomy for interaction during training and testing, and show how either human-initiated and machine-initiated interaction can lead to human and machine learning. In a section on new technologies, we discuss how new cameras and displays, web-wide access, interoperability, and essentially unlimited storage provide fertile new approaches to document analysis.

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Towards Ontology Generation from Tables

Cited by 109 publications

References 25 publications

Notes on Contemporary Table Recognition

Notes on Contemporary Table Recognition

Estimation, Learning, and Adaptation: Systems That Improve with Use

Adaptive and Interactive Approaches to Document Analysis

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