ITEM: Extract and integrate entities from tabular data to RDF knowledge base
|ITEM: Extract and integrate entities from tabular data to RDF knowledge base|
|Author(s)||Guo X., Chen Y., Chen J., Du X.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Entity Extraction, RDF Knowledge Base, Schema Mapping (Extra: Data source, Entity extractions, High precision, High quality, Knowledge base, Knowledge basis, New system, RDF Knowledge Base, Schema information, Schema Mapping, Schema mappings, Social tagging, Tabular data, Web data, Wikipedia, Wordnet, Data mining, Knowledge based systems)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
ITEM: Extract and integrate entities from tabular data to RDF knowledge base is a 2011 conference paper written in English by Guo X., Chen Y., Chen J., Du X. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Many RDF Knowledge Bases are created and enlarged by mining and extracting web data. Hence their data sources are limited to social tagging networks, such as Wikipedia, WordNet, IMDB, etc., and their precision is not guaranteed. In this paper, we propose a new system, ITEM, for extracting and integrating entities from tabular data to RDF knowledge base. ITEM can efficiently compute the schema mapping between a table and a KB, and inject novel entities into the KB. Therefore, ITEM can enlarge and improve RDF KB by employing tabular data, which is assumed of high quality. ITEM detects the schema mapping between table and RDF KB only by tuples, rather than the table's schema information. Experimental results show that our system has high precision and good performance.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.