Automatic Hidden-Web Table Interpretation by Sibling Page Comparison

Tao, Cui; Embley, David W.

doi:10.1007/978-3-540-75563-0_38

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…A user can then modify the form, 9 if desired, and use it to harvest information. We have implemented this reverse-engineering of tables into FOCIH forms based on a system called TISP (Table Interpretation for Sibling Pages) [29,30]. TISP converts tables from sites like hidden-web sites that have machinegenerated sibling pages into FOCIH forms and thus into FOCIH-generated ontologies.…”

Section: Further Reduction Of Labor-intensive Tasksmentioning

confidence: 99%

FOCIH: Form-Based Ontology Creation and Information Harvesting

Tao

Embley

Liddle

2009

Conceptual Modeling - ER 2009

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Abstract.Creating an ontology and populating it with data are both labor-intensive tasks requiring a high degree of expertise. Thus, scaling ontology creation and population to the size of the web in an effort to create a web of data-which some see as Web 3.0-is prohibitive. Can we find ways to streamline these tasks and lower the barrier enough to enable Web 3.0? Toward this end we offer a form-based approach to ontology creation that provides a way to create Web 3.0 ontologies without the need for specialized training. And we offer a way to semi-automatically harvest data from the current web of pages for a Web 3.0 ontology. In addition to harvesting information with respect to an ontology, the approach also annotates web pages and links facts in web pages to ontological concepts, resulting in a web of data superimposed over the web of pages. Experience with our prototype system shows that mappings between conceptual-model-based ontologies and forms are sufficient for creating the kind of ontologies needed for Web 3.0, and experiments with our prototype system show that automatic harvesting, automatic annotation, and automatic superimposition of a web of data over a web of pages work well. Keywords: ontology generation from forms, information harvesting from the web, automatic annotation of web pages, web of data, Web 3.0.

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Section: Further Reduction Of Labor-intensive Tasksmentioning

confidence: 99%

FOCIH: Form-Based Ontology Creation and Information Harvesting

Tao

Embley

Liddle

2009

Conceptual Modeling - ER 2009

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“…There is ample evidence that automating table understanding, or even merely verifying claims to this effect, is very difficult [21,22,23]. As mentioned, our project is the front end of a larger undertaking that endeavors to create narrow-domain ontologies by combining information from web tables [1,24,25]. Suppose, for instance, that we process the left-hand table in Figure 8 and include it into the ontology.…”

Section: Labeled Table Candidates For Which Wang Notation Exists Are mentioning

confidence: 99%

From Tessellations to Table Interpretation

Jandhyala

Krishnamoorthy

Nagy

et al. 2009

Lecture Notes in Computer Science

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Abstract. The extraction of the relations of nested table headers to content cells is automated with a view to constructing narrow domain ontologies of semistructured web data. A taxonomy of tessellations for displaying tabular data is developed. X-Y tessellations that can be obtained by a divide-and-conquer method are asymptotically only an infinitesimal fraction of all partitions of a rectangle into rectangles. Admissible tessellations are the even smaller subset of all partitions that correspond to the structures of published tables and that contain only rectangles produced by successive guillotine cuts. Many of these can be processed automatically. Their structures can be conveniently represented by X-Y trees, which facilitate relating hierarchical row and column headings to content cells. A formal grammar is proposed for characterizing the X-Y trees of layout-equivalent admissible tessellations. Algorithms are presented for transforming a tessellation into an X-Y tree and hence into multidimensional, layoutindependent Category Trees (Wang abstract data types).

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“…We call our table-interpretation system TISP (Table Interpretation with Sibling Pages) [39]. Table from WormBase.…”

Section: Model(s)amino Acids 2 ) → 406 Aa Where Identificationgenementioning

confidence: 99%

Automatic hidden-web table interpretation, conceptualization, and semantic annotation

Tao

Embley

2009

Data & Knowledge Engineering

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The longstanding problem of automatic table interpretation still illudes us. Its solution would not only be an aid to table processing applications such as large volume table conversion, but would also be an aid in solving related problems such as information extraction, semantic annotation, and semi-structured data management. In this paper, we offer a solution for the common special case in which so-called sibling pages are available. The sibling pages we consider are pages on the hidden web, commonly generated from underlying databases. Our system compares them to identify and connect nonvarying components (category labels) and varying components (data values). We tested our solution using more than 2,000 tables in source pages from three different domains -car advertisements, molecular biology, and geopolitical information. Experimental results show that the system can successfully identify sibling tables, generate structure patterns, interpret tables using the generated patterns, and automatically adjust the structure patterns as it processes a sequence of hidden-web pages. For these activities, the system was able to achieve an overall F-measure of 94.5%. Further, given that we can automatically interpret tables, we next show that this leads immediately to a conceptualization of the data in these interpreted tables and thus also to a way to semantically annotate these interpreted tables with respect to the ontological conceptualization. Labels in nested table structures yield ontological concepts and interrelationships among these concepts, and associated data values become annotated information. We further show that semantically annotated data leads immediately to queriable data. Thus, the entire process, which is fully automatic, transform facts embedded within tables into facts accessible by standard query engines.

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Automatic Hidden-Web Table Interpretation by Sibling Page Comparison

Cited by 7 publications

References 18 publications

FOCIH: Form-Based Ontology Creation and Information Harvesting

FOCIH: Form-Based Ontology Creation and Information Harvesting

From Tessellations to Table Interpretation

Automatic hidden-web table interpretation, conceptualization, and semantic annotation

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