Detecting tables in Web documents

Kim, Yeon-Seok; Lee, Kyong-Ho

doi:10.1016/j.engappai.2005.01.009

Cited by 13 publications

(24 citation statements)

References 4 publications

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“…Its label is typically constrained by a data type and pattern. Examples of types and their corresponding patterns taken from [4] are shown in Table 3. Clearly, a cell is strongly determined as a value cell once its label corresponds to any data patterns in Table 3.…”

Section: Attribute and Valuementioning

confidence: 99%

“…In particular, the row (column) whose cells firstly correspond to the labels learnt by vector space model is considered as attribute row (column), while the rest presents the values. Kim et al [4,5] use visual and semantic coherency checkup to evaluate how to interpret a table. The visual coherency checkup is done based on the number of cells having the major formatting feature and data type, the semantic coherency checks for the semantic correspondence between an attribute and its value.…”

Section: Introductionmentioning

confidence: 99%

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Mining for attributes and values in tables

Harnsamut

Sahavechaphan

2010

Proceedings of the International Conference on Management of Emergent Digital EcoSystems

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Table has been recognized as a simply and widely used data representation scheme. Each table alone typically contains rich and useful information which is valuable for many applications such as information retrieval, question-answering and etc. While all table formats can simply be parsed by human, this parsing is difficult for computer, prohibiting such applications to be done in an automatic manner. In this paper, we thus propose the comprehensive and novel table interpretation technique, namely tInterpreter. Essentially, it transforms a table into its corresponding horizontal 1-dimensional tables. To achieve this, the underlying work is based on (i) the similarity of two given cells with respect to the data type and the semantic correspondence concerns; (ii) the discovery for the boundary of a primitive table residing in a composite table; (iii) the identification of the attribute-value relationship and the value association of cells; and (iv) the integration of two pieces of similar or dissimilar information. The experimental result showed that the overall effectiveness of tInterpreter was higher than Chen, Tengli and Kim.

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Section: Attribute and Valuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining for attributes and values in tables

Harnsamut

Sahavechaphan

2010

Proceedings of the International Conference on Management of Emergent Digital EcoSystems

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“…The number of cells of a base block and its neighbor cell , (3) where the major data type means the data type with the highest frequency in the constituent cells of the base block. Additionally, the length coherency is denoted as the frequency of cells, of which the length is within the range of α (the average length of the constituent cells of a block × 0.5 ≤ α ≤ the average length of the constituent cells of a block × 1.5), and is computed by Length Coherency = The number of cells with a length within the range of α…”

Section: Formatting Coherency =mentioning

confidence: 99%

“…In this case, the syntactic differences between cells are considered. Syntactic coherency is defined as the similarity between data type and length [3].…”

Section: Formatting Coherency =mentioning

confidence: 99%

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Generating Structured Documents from HTML Tables

Lee¹

2006

2006 International Conference on Hybrid Information Technology

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A table is a facility for presenting relational information structurally and concisely. As a prerequisite for extracting information from the Web, This paper presents an efficient method for extracting logical structures from HTML tables and transforming them into XML documents. The proposed method consists of two phases: area segmentation and structure analysis. The area segmentation step cleans up the table and segments the normalized table into attribute and value areas by checking visual and semantic coherency. Particularly, heuristic rules are also proposed to handle complex tables. In the structure analysis phase, the hierarchical structure between attribute and value areas is analyzed and transformed into an XML representation using the proposed table model. Experimental results with a large number of HTML tables show that the proposed method performs better than the conventional method.

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