Paper Info
Title
Extraction of temporal facts and events from Wikipedia
Abstract
Recently, large-scale knowledge bases have been constructed by automatically extracting relational facts from text. Unfortunately, most of the current knowledge bases focus on static facts and ignore the temporal dimension. However, the vast majority of facts are evolving with time or are valid only during a particular time period. Thus, time is a significant dimension that should be included in knowledge bases. In this paper, we introduce a complete information extraction framework that harvests temporal facts and events from semi-structured data and free text of Wikipedia articles to create a temporal ontology. First, we extend a temporal data representation model by making it aware of events. Second, we develop an information extraction method which harvests temporal facts and events from Wikipedia infoboxes, categories, lists, and article titles in order to build a temporal knowledge base. Third, we show how the system can use its extracted knowledge for further growing the knowledge base. We demonstrate the effectiveness of our proposed methods through several experiments. We extracted more than one million temporal facts with precision over 90% for extraction from semi-structured data and almost 70% for extraction from text.
Year
DOI
Venue
2012
10.1145/2169095.2169101
TempWeb
Keywords
Field
DocType
current knowledge base,temporal knowledge base,large-scale knowledge base,semi-structured data,knowledge base,million temporal fact,temporal ontology,temporal data representation model,temporal fact,temporal dimension,temporal data,semi structured data,ontologies,information extraction
Ontology (information science),Ontology,Information retrieval,Computer science,Temporal database,Information extraction,Knowledge extraction,Knowledge base,Complete information
Conference
Citations 
PageRank 
References 
22
0.83
53
Authors
2
Name
Order
Citations
PageRank
Erdal Kuzey1793.81
Gerhard Weikum2127102146.01