Ontology augmentation via attribute extraction from multiple types of sources

Xiu Susie Fang*, Xianzhi Wang, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)


A comprehensive ontology can ease the discovery, maintenance and popularization of knowledge in many domains. As a means to enhance existing ontologies, attribute extraction has attracted tremendous research attentions. However, most existing attribute extraction techniques focus on exploring a single type of sources, such as structured (e.g., relational databases), semi-structured (e.g., Extensible Markup Language (XML)) or unstructured sources (e.g., Web texts, images), which leads to the poor coverage of knowledge bases (KBs). This paper presents a framework for ontology augmentation by extracting attributes from four types of sources, namely existing knowledge bases (KBs), query stream, Web texts, and Document Object Model (DOM) trees. In particular, we use query stream and two major KBs, DBpedia and Freebase, to seed the attribute extraction from Web texts and DOM trees. We specially focus on exploring the extraction technique from DOM trees, which is rarely studied in previous works. Algorithms and a series of filters are developed. Experiments show the capability of our approach in augmenting existing KB ontology.

Original languageEnglish
Title of host publicationDatabases Theory and Applications
Subtitle of host publication26th Australasian Database Conference, ADC 2015, Melbourne, VIC, Australia, June 4-7, 2015. Proceedings
EditorsMohamed A. Sharaf, Muhammad Aamir Cheema, Jianzhong Qi
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages12
ISBN (Print)9783319195476
Publication statusPublished - 2015
Externally publishedYes
Event26th Australasian Database Conference, ADC 2015 - Melbourne, Australia
Duration: 4 Jun 20157 Jun 2015

Publication series

NameLecture Notes in Computer Science
ISSN (Print)03029743
ISSN (Electronic)16113349


Other26th Australasian Database Conference, ADC 2015


  • Dom tree
  • Information extraction
  • Knowledge base
  • Web data


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