Is automatic detection of hidden knowledge an anomaly?
Abstract Background The quantity of documents being published requires researchers to specialize to a narrower field, meaning that inferable connections between publications (particularly from different domains) can be missed. This has given rise to automatic literature based discovery (LBD). Howeve...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-05-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2815-4 |
| _version_ | 1818503286186049536 |
|---|---|
| author | Judita Preiss |
| author_facet | Judita Preiss |
| author_sort | Judita Preiss |
| collection | DOAJ |
| description | Abstract Background The quantity of documents being published requires researchers to specialize to a narrower field, meaning that inferable connections between publications (particularly from different domains) can be missed. This has given rise to automatic literature based discovery (LBD). However, unless heavily filtered, LBD generates more potential new knowledge than can be manually verified and another form of selection is required before the results can be passed onto a user. Since a large proportion of the automatically generated hidden knowledge is valid but generally known, we investigate the hypothesis that non trivial, interesting, hidden knowledge can be treated as an anomaly and identified using anomaly detection approaches. Results Two experiments are conducted: (1) to avoid errors arising from incorrect extraction of relations, the hypothesis is validated using manually annotated relations appearing in a thesaurus, and (2) automatically extracted relations are used to investigate the hypothesis on publication abstracts. These allow an investigation of a potential upper bound and the detection of limitations yielded by automatic relation extraction. Conclusion We apply one-class SVM and isolation forest anomaly detection algorithms to a set of hidden connections to rank connections by identifying outlying (interesting) ones and show that the approach increases the F 1 measure by a factor of 10 while greatly reducing the quantity of hidden knowledge to manually verify. We also demonstrate the statistical significance of this result. |
| first_indexed | 2024-12-10T21:22:01Z |
| format | Article |
| id | doaj.art-58618d737e5641a3892fd8f544b67620 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T21:22:01Z |
| publishDate | 2019-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-58618d737e5641a3892fd8f544b676202022-12-22T01:33:06ZengBMCBMC Bioinformatics1471-21052019-05-0120S10758010.1186/s12859-019-2815-4Is automatic detection of hidden knowledge an anomaly?Judita Preiss0University of Salford, The School of Computing, Science & EngineeringAbstract Background The quantity of documents being published requires researchers to specialize to a narrower field, meaning that inferable connections between publications (particularly from different domains) can be missed. This has given rise to automatic literature based discovery (LBD). However, unless heavily filtered, LBD generates more potential new knowledge than can be manually verified and another form of selection is required before the results can be passed onto a user. Since a large proportion of the automatically generated hidden knowledge is valid but generally known, we investigate the hypothesis that non trivial, interesting, hidden knowledge can be treated as an anomaly and identified using anomaly detection approaches. Results Two experiments are conducted: (1) to avoid errors arising from incorrect extraction of relations, the hypothesis is validated using manually annotated relations appearing in a thesaurus, and (2) automatically extracted relations are used to investigate the hypothesis on publication abstracts. These allow an investigation of a potential upper bound and the detection of limitations yielded by automatic relation extraction. Conclusion We apply one-class SVM and isolation forest anomaly detection algorithms to a set of hidden connections to rank connections by identifying outlying (interesting) ones and show that the approach increases the F 1 measure by a factor of 10 while greatly reducing the quantity of hidden knowledge to manually verify. We also demonstrate the statistical significance of this result.http://link.springer.com/article/10.1186/s12859-019-2815-4Literature based discoveryAnomaly detectionUnified medical language system |
| spellingShingle | Judita Preiss Is automatic detection of hidden knowledge an anomaly? BMC Bioinformatics Literature based discovery Anomaly detection Unified medical language system |
| title | Is automatic detection of hidden knowledge an anomaly? |
| title_full | Is automatic detection of hidden knowledge an anomaly? |
| title_fullStr | Is automatic detection of hidden knowledge an anomaly? |
| title_full_unstemmed | Is automatic detection of hidden knowledge an anomaly? |
| title_short | Is automatic detection of hidden knowledge an anomaly? |
| title_sort | is automatic detection of hidden knowledge an anomaly |
| topic | Literature based discovery Anomaly detection Unified medical language system |
| url | http://link.springer.com/article/10.1186/s12859-019-2815-4 |
| work_keys_str_mv | AT juditapreiss isautomaticdetectionofhiddenknowledgeananomaly |