A Quantitative Comparison of Semantic Web Page Segmentation Approaches

Kreuzer, Robert; Hage, Jurriaan; Feelders, Ad

doi:10.1007/978-3-319-19890-3_24

Cited by 7 publications

(12 citation statements)

References 13 publications

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“…Some provide access to the tools but it is not the general case. An interesting experience is the work of [11]. They present a method for quantitative comparison of semantic Web page segmentation algorithms.…”

Section: Segmentation Correctness Evaluationmentioning

confidence: 98%

Web page segmentation evaluation

Sanoja

Gançarski

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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In this paper, we present a framework for evaluating segmentation algorithms for Web pages. Web page segmentation consists in dividing a Web page into coherent fragments, called blocks. Each block represents one distinct information element in the page. We define an evaluation model that includes different metrics to evaluate the quality of a segmentation obtained with a given algorithm. Those metrics compute the distance between the obtained segmentation and a manually built segmentation that serves as a ground truth. We apply our framework to four state-of-the-art segmentation algorithms (BOM, Block Fusion, VIPS and JVIPS) on several categories (types) of Web pages. Results show that the tested algorithms usually perform rather well for text extraction, but may have serious problems for the extraction of geometry. They also show that the relative quality of a segmentation algorithm depends on the category of the segmented page.

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Section: Segmentation Correctness Evaluationmentioning

confidence: 98%

Web page segmentation evaluation

Sanoja

Gançarski

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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“…Algorithms use structural features based on the DOM tree and the textual content, and visual features extracted from renderings of individual nodes as well as the entire web page. Most algorithms use the DOM tree structure in some way, for example to identify headings [26], block nodes [1,6], or regularities [16], and to compute the tree depth [24] or the tree distance [18] of nodes. Other algorithms use the text density [22] or visual appearance of DOM nodes when rendered (e.g., their size or color; Baluja [4], Zeleny et al [37]).…”

Section: Related Workmentioning

confidence: 99%

“…Nine of the 19 publications listed in Table 1 give-explicitly or implicitly-a definition of what a web page segment is. The most common one (though used in only four publications) is that of a visual "block" with coherent content [9,24,26,37]. Other definitions characterize segments by their edges [12,13], as being semantically self-contained [16], as distinct [30], or as labeled with a heading [28].…”

Section: Concept Formation: Page Segmentmentioning

confidence: 99%

“…Other definitions characterize segments by their edges [12,13], as being semantically self-contained [16], as distinct [30], or as labeled with a heading [28]. Only two papers resort to HTML/DOM elements or sub-trees as segment building blocks [9,24]. Seven of the nine definitions require a segment to be cohesive, and two define a segment as being "different" to other parts of a web page.…”

Section: Concept Formation: Page Segmentmentioning

confidence: 99%

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Web Page Segmentation Revisited

Kiesel

Kneist

Meyer

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Each web page can be segmented into semantically coherent units that fulfill specific purposes. Though the task of automatic web page segmentation was introduced two decades ago, along with several applications in web content analysis, its foundations are still lacking. Specifically, the developed evaluation methods and datasets presume a certain downstream task, which led to a variety of incompatible datasets and evaluation methods. To address this shortcoming, we contribute two resources: (1) An evaluation framework which can be adjusted to downstream tasks by measuring the segmentation similarity regarding visual, structural, and textual elements, and which includes measures for annotator agreement, segmentation quality, and an algorithm for segmentation fusion. (2) The Webis-WebSeg-20 dataset, comprising 42,450 crowdsourced segmentations for 8,490 web pages, outranging existing sources by an order of magnitude. Our results help to better understand the "mental segmentation model" of human annotators: Among other things we find that annotators mostly agree on segmentations for all kinds of web page elements (visual, structural, and textual). Disagreement exists mostly regarding the right level of granularity, indicating a general agreement on the visual structure of web pages.

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“…et al [26] proposed an algorithm which combines a plain structural approach with a rendering-based approach. They make use of DOM tree with addition to the information of visibility and dimensions of each of the tree.…”

Section: Hybrid Approachmentioning

confidence: 99%

Web Document Segmentation for Better Extraction of Information: A Review

Eldirdiery¹,

Ahmed²

2015

IJCA

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This paper reviews the problem of web page segmentation. According to the recent studies, there exist different approaches used to segment the web page into multiple blocks. Segmentation of web document is an essential step for many applications, such as text classifications, clustering, extraction of information and searching. The study provided full description for each approach and showed its contribution to the work area of research. Also the paper discusses the variance between these approaches, explaining the benefits and limitations of each one. In addition to that it explores most of the effective algorithms those based on these approaches and explains the application area of each algorithm.

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A Quantitative Comparison of Semantic Web Page Segmentation Approaches

Cited by 7 publications

References 13 publications

Web page segmentation evaluation

Web page segmentation evaluation

Web Page Segmentation Revisited

Web Document Segmentation for Better Extraction of Information: A Review

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