| P06-2086 | predicate instances extracted by | KnowItAll | for the relation seeds needed |
| H05-2017 | of relations of inter - est , | KNOWITALL | instantiates relation-specific |
| P06-2086 | Hence , for extracting relations | KnowItAll | currently uses only the generic |
| H05-1043 | set of relations of interest , | KnowItAll | instantiates relation-specific |
| P07-1073 | extraction patterns from the web is | KNOWITALL | ( Etzioni et al. , 2005 ) . For |
| P06-2086 | different URES setups , and for the | KnowItAll | manually built patterns . Three |
| H05-2017 | Overview OPINE is built on top of | KNOWITALL | , a Web-based , domain-independent |
| H05-1043 | System . OPINE is built on top of | KnowItAll | , a Web-based , domain-independent |
| P06-2086 | unsupervised extraction system , such as | KnowItAll | . The seeds for our experiments |
| H05-1043 | Acknowledgments We would like to thank the | KnowItAll | project and the anonymous reviewers |
| H05-1043 | rules which find candidate facts . | KnowItAll | 's Assessor then assigns a probability |
| P06-2086 | dataset used in our experiments . | KnowItAll | system is a direct predecessor |
| D11-1142 | Luke Zettlemoyer , members of the | KnowItAll | group , and the anonymous reviewers |
| H05-2017 | ) using an extended version of | KNOWITALL | 's extract-and-assess strategy |
| P06-2086 | together with discriminator phrases . | KnowItAll | has also a pattern learning module |
| P06-2086 | URES significantly outperform | KnowItAll | in recall ( number of ex - tractions |
| P06-2086 | entities extracted from the Web . | KnowItAll | uses a set of manually-built |
| D09-1099 | contexts around them for learning . | KnowItAll | ( Et - zioni et al. , 2005 ) |
| P06-2086 | predi - cates ) . The input to | KnowItAll | is a set of entity classes to |
| P06-2086 | InventorOf ) are entities collected by | KnowItAll | , half of them are frequent entities |
