Conceptual-model-based data extraction from multiple-record Web pages

Embley, David W.; Campbell, Douglas M.; Jiang, Yanchao; Liddle, Stephen W.; Lonsdale, Deryle; Ng, Yiu‐Kai; Smith, Randy D.

doi:10.1016/s0169-023x(99)00027-0

Cited by 240 publications

(142 citation statements)

References 23 publications

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“…Figure 2 shows a generated ontology for the form in Figure 1. We use OSM [11] as the conceptualmodel basis for an extraction ontology. The advantage of OSM is that it has a high-level graphical representation that directly translates to predicate calculus.…”

Section: Ontology Generationmentioning

confidence: 99%

“…Thus, when appropriately limited, it translates in a straightforward way to OWL [34] and to various description logics [3]. Even more important, however, is OSM's ability to support data extraction from source documents [11].…”

Section: Ontology Generationmentioning

confidence: 99%

“…Particularly for list patterns, but also as context for individual patterns, FOCIH has a default list of delimiters: ",", ";", ":", "|", " /", "\", "(", ")", "[", "]", "{", "}", sos (start of string) and eos (end of string). FOCIH also has a library of regular-expression recognizers for values in common formats, such as numbers, numbers with commas, decimal numbers, positive/negative integers, percentages, dates, times, and currencies [11].…”

Section: Automatic Semantic Annotationmentioning

confidence: 99%

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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: Ontology Generationmentioning

confidence: 99%

Section: Ontology Generationmentioning

confidence: 99%

Section: Automatic Semantic Annotationmentioning

confidence: 99%

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FOCIH: Form-Based Ontology Creation and Information Harvesting

Tao

Embley

Liddle

2009

Conceptual Modeling - ER 2009

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“…We use OSM [4] as the semantic data model for an extraction ontology. Figure 1 shows a graphical view of a sample ontology.…”

Section: Osm Ontologiesmentioning

confidence: 99%

“…In addition, and more important, an OSM ontology can support data extraction from source documents [4].…”

Section: Osm Ontologiesmentioning

confidence: 99%

Seed-Based Generation of Personalized Bio-ontologies for Information Extraction

Tao¹,

Embley²

Advances in Conceptual Modeling – Foundations and Applications

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Abstract. Biologists usually focus on only a small, individualized, subdomain of the huge domain of biology. With respect to their sub-domain, they often need data collected from various different web resources. In this research, we provide a tool with which biologists can generate a sub-domain-size, user-specific ontology that can extract data from web resources. The central idea is to let a user provide a seed, which consists of a single data instance embedded within the concepts of interest. Given a seed, the system can generate an extraction ontology, match information with the user's view based on the seed, and collect information from online repositories. Our initial experimentations indicate that our prototype system can successfully match source data with an ontology seed and gather information from different sources with respect to user-specific, personalized views.

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Data Search Engine

Meng

2007

Wiley Encyclopedia of Computer Science and Engineering

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Conceptual-model-based data extraction from multiple-record Web pages

Cited by 240 publications

References 23 publications

FOCIH: Form-Based Ontology Creation and Information Harvesting

FOCIH: Form-Based Ontology Creation and Information Harvesting

Seed-Based Generation of Personalized Bio-ontologies for Information Extraction

Data Search Engine

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