Name
Abstract | ||
|---|---|---|
Recent years have witnessed a proliferation of large-scale knowledge bases, including Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase the scale even further, we need to explore automatic methods for constructing knowledge bases. Previous approaches have primarily focused on text-based extraction, which can be very noisy. Here we introduce Knowledge Vault, a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories. We employ supervised machine learning methods for fusing these distinct information sources. The Knowledge Vault is substantially bigger than any previously published structured knowledge repository, and features a probabilistic inference system that computes calibrated probabilities of fact correctness. We report the results of multiple studies that explore the relative utility of the different information sources and extraction methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2623330.2623623 | KDD |
Keywords | Field | DocType |
information extraction,knowledge bases,machine learning,probabilistic models,statistical databases,textual databases | Data mining,Computer science,Correctness,Knowledge-based systems,Information extraction,Artificial intelligence,Knowledge extraction,Knowledge base,Probabilistic logic,Web content,Machine learning,Open Knowledge Base Connectivity | Conference |
Citations | PageRank | References |
440 | 11.51 | 39 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Luna Dong | 1 | 2524 | 129.18 |
| Evgeniy Gabrilovich | 2 | 4573 | 224.48 |
| geremy heitz | 3 | 1076 | 52.33 |
| Wilko Horn | 4 | 549 | 14.20 |
| Ni Lao | 5 | 986 | 39.73 |
| Michael Kuperberg | 6 | 7589 | 529.66 |
| Thomas Strohmann | 7 | 440 | 11.51 |
| Shaohua Sun | 8 | 622 | 16.73 |
| Wei Zhang | 9 | 452 | 19.35 |