Name
Abstract | ||
|---|---|---|
The Web and digitized text sources contain a wealth of information about named entities such as politicians, actors, companies, or cultural landmarks. Extracting this information has enabled the automated construction of large knowledge bases, containing hundred millions of binary relationships or attribute values about these named entities. However, in reality most knowledge is transient, i.e. changes over time, requiring a temporal dimension in fact extraction. In this paper we develop a methodology that combines label propagation with constraint reasoning for temporal fact extraction. Label propagation aggressively gathers fact candidates, and an Integer Linear Program is used to clean out false hypotheses that violate temporal constraints. Our method is able to improve on recall while keeping up with precision, which we demonstrate by experiments with biography-style Wikipedia pages and a large corpus of news articles. |
Year | Venue | Keywords |
|---|---|---|
2012 | ACL | temporal fact extraction,attribute value,label propagation,integer linear program,large knowledge base,fact extraction,large corpus,temporal constraint,fact candidate,temporal dimension,coupling label propagation |
Field | DocType | Volume |
Integer,Data mining,Coupling,Label propagation,Computer science,Artificial intelligence,Linear programming,Natural language processing,Fact extraction,Machine learning,Binary number,Constraint reasoning | Conference | P12-2 |
Citations | PageRank | References |
14 | 0.64 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yafang Wang | 1 | 134 | 13.56 |
| Maximilian Dylla | 2 | 109 | 5.93 |
| Marc Spaniol | 3 | 897 | 61.13 |
| Gerhard Weikum | 4 | 12710 | 2146.01 |