Improving Data Quality Through Effective Use of Data Semantics
Data quality issues have taken on increasing importance in recent years. In our research, we have discovered that many “data quality” problems are actually “data misinterpretation” problems – that is, problems with data semantics. In this paper, we first illustrate some examples of these problems an...
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| Format: | Article |
| Language: | en_US |
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2003
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| Online Access: | http://hdl.handle.net/1721.1/3861 |
| _version_ | 1811068732503490560 |
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| author | Madnick, Stuart E. |
| author_facet | Madnick, Stuart E. |
| author_sort | Madnick, Stuart E. |
| collection | MIT |
| description | Data quality issues have taken on increasing importance in recent years. In our research, we have discovered that many “data quality” problems are actually “data misinterpretation” problems – that is, problems with data semantics. In this paper, we first illustrate some examples of these problems and then introduce a particular semantic problem that we call “corporate householding.” We stress the importance of “context” to get the appropriate answer for each task. Then we propose an approach to handle these tasks using extensions to the COntext INterchange (COIN) technology for knowledge storage and knowledge processing. |
| first_indexed | 2024-09-23T08:00:15Z |
| format | Article |
| id | mit-1721.1/3861 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:00:15Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | mit-1721.1/38612019-04-09T16:03:04Z Improving Data Quality Through Effective Use of Data Semantics Madnick, Stuart E. data quality data semantics corporate householding COntext INterchange knowledge management. Data quality issues have taken on increasing importance in recent years. In our research, we have discovered that many “data quality” problems are actually “data misinterpretation” problems – that is, problems with data semantics. In this paper, we first illustrate some examples of these problems and then introduce a particular semantic problem that we call “corporate householding.” We stress the importance of “context” to get the appropriate answer for each task. Then we propose an approach to handle these tasks using extensions to the COntext INterchange (COIN) technology for knowledge storage and knowledge processing. Singapore-MIT Alliance (SMA) 2003-12-13T19:23:34Z 2003-12-13T19:23:34Z 2004-01 Article http://hdl.handle.net/1721.1/3861 en_US Computer Science (CS); 227013 bytes application/pdf application/pdf |
| spellingShingle | data quality data semantics corporate householding COntext INterchange knowledge management. Madnick, Stuart E. Improving Data Quality Through Effective Use of Data Semantics |
| title | Improving Data Quality Through Effective Use of Data Semantics |
| title_full | Improving Data Quality Through Effective Use of Data Semantics |
| title_fullStr | Improving Data Quality Through Effective Use of Data Semantics |
| title_full_unstemmed | Improving Data Quality Through Effective Use of Data Semantics |
| title_short | Improving Data Quality Through Effective Use of Data Semantics |
| title_sort | improving data quality through effective use of data semantics |
| topic | data quality data semantics corporate householding COntext INterchange knowledge management. |
| url | http://hdl.handle.net/1721.1/3861 |
| work_keys_str_mv | AT madnickstuarte improvingdataqualitythrougheffectiveuseofdatasemantics |