Extracting Assumptions from Missing Data
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Citation: Honglei Zeng and Richard Fikes. (2005) Extracting Assumptions from Missing Data. In Context representation and reasoning 2005, proceedings of the first international workshop, July,2005.
| Publication inproceedings ( Edit ) | |
| type | InProceedings |
| bibtype | inproceedings |
| Bibtex basics | |
| author | Honglei Zeng and Richard Fikes |
| title | Extracting Assumptions from Missing Data |
| booktitle | Context representation and reasoning 2005, proceedings of the first international workshop |
| address | Paris |
| year | 2005 |
| month | July |
| Bibtex more | |
| Access Paper | |
| abstract | Information integration is the task of aggregating data from multiple heterogeneous data sources. The understandings of context knowledge of data sources are often the keys to challenging problems in information integration such as handling missing and inconsistent data. Context logic provides a unified framework for the modeling of data sources; nevertheless, the acquisition of large amounts of context knowledge is difficult. In this paper, we study the importance of a special type of context knowledge, namely assumption knowledge. Assumption knowledge refers to a set of implicit rules about assumptions on which a data source is based. We develop a decision tree classifier to extract assumption knowledge from missing data and formalize the knowledge in context logic. Finally, we build an information aggregator with assumption knowledge reasoning, which is capable of explaining incomplete data aggregated from heterogeneous sources. |
| KSL Technical Report ID: KSL-05-07 |
Facts about Extracting Assumptions from Missing DataRDF feed
| Abstract | Information integration is the task of agg … Information integration is the task of aggregating data from multiple heterogeneous data sources. The understandings of context knowledge of data sources are often the keys to challenging problems in information integration such as handling missing and inconsistent data. Context logic provides a unified framework for the modeling of data sources; nevertheless, the acquisition of large amounts of context knowledge is difficult. In this paper, we study the importance of a special type of context knowledge, namely assumption knowledge. Assumption knowledge refers to a set of implicit rules about assumptions on which a data source is based. We develop a decision tree classifier to extract assumption knowledge from missing data and formalize the knowledge in context logic. Finally, we build an information aggregator with assumption knowledge reasoning, which is capable of explaining incomplete data aggregated from heterogeneous sources. ata aggregated from heterogeneous sources. |
| Address | Paris + |
| Author | Honglei Zeng and Richard Fikes + |
| Bibtype | inproceedings + |
| Booktitle | Context representation and reasoning 2005, proceedings of the first international workshop + |
| Has author | Honglei Zeng and Richard Fikes + |
| Has identifier | KSL-05-07 + |
| Has publishing details | July,2005 + |
| Has title | Extracting Assumptions from Missing Data + |
| Has where published | Context representation and reasoning 2005, proceedings of the first international workshop + |
| Has year | 2005 + |
| Ksl tr id | KSL-05-07 + |
| Month | July + |
| Process note | NO + |
| Title | Extracting Assumptions from Missing Data + |
| Year | 2005 + |
