OPPCAT: Ontology population from tabular data

Abstract

In order to present large amount of information on the Web to both users and machines, it is urgently needed to structure Web data. E-commerce is one of the areas where increasing data bottlenecks on the Web inhibit data access. Ontological display of the product information enables better product comparison and search applications using the semantics of the product specifications and their corresponding values. In this article, we present a framework called OPPCAT, which is used for semi-automatic ontology population from tabular data in e-commerce stores and product catalogues. As a result, OPPCAT allows tabular data to be used for mass production of ontology content. First, we present the common patterns in tabular data which obstruct semi-automatic production of ontologies. Then, we suggest solutions which automatically fix these errors. Finally, we define an algorithm to build ontology content semi-automatically.

A method of semi-automated ontology population from multiple semi-structured data sources

“…This structure, however, is rarely applicable to real-world spreadsheets [18]. Several approaches have tried to overcome this assumption by importing cells as separate entities or blocks [19–23] or by adding semantic structure to the table [3,18,24–26]. Systems for tabular data integration can be divided into four groups based on the table’s content-provided output [27]: (1) RDF graph (RDF 123, XLWrap, Google Refine); (2) OWL ontology instances (Anzo suit, MappingMaster); (3) any part of the Semantic Web environment, including RDF graph or ontology (TopBraid Composer); and (4) don’t convert data into RDF or OWL but provide a simple spreadsheet-like user interface (Populous).…”

Petrol, Gaz ve Madencilik Endüstrisinde Bilgi Gösterimi için Ontoloji Temelli bir Yaklaşım

“…The 458 drill bits with 6 attributes are collected by IRIS tool using our template in Figure 5. The second approach involves extracting data out of tables in PDF product catalogs and importing it into spreadsheets with the aim of building product ontologies automatically from spreadsheets in a fast and effective way [25]. We extracted 1544 DTH drill bits designed by Bulroc (http://www.bulroc.com/Brochures/buttonBits.pdf), Halco (www.halco.uk), Mincon (http://www.mincon.com/) and Sandvik (http://tebyc.com/wordpress/wp-content /uploads/ 2012/09/sandvik_dth_tools_product_catalogue.pdf).…”