Abstract
Incomplete information in web intelligence applications has serious consequences: inaccurate statements predominate, resulting primarily in erroneous annotations and ultimately in inaccurate reasoning on the web. This research work focuses on improving the completeness of extraction results by applying judiciously selected assessment methods to information extraction within the principle of complementarity. On the one hand, this paper discusses several requirements an assessment method must meet in terms of processability and profitability to guarantee effective operation in a complementarity approach. On the other hand, it proposes a recommendation model to guide an IE system designer in selecting the appropriate methods for optimizing web data quality. The paper concludes with an application scenario that supports the theoretical approach.
| Original language | English |
|---|---|
| Title of host publication | Twenty-Fourth International Workshop on Database and Expert Systems Applications |
| Editors | Franck Morvan, A Min Tjoa, Roland R. Wagner |
| Publisher | IEEE Computer Society |
| Pages | 113-117 |
| Number of pages | 5 |
| Publication status | Published - Aug 2013 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
JKU Focus areas
- Computation in Informatics and Mathematics