Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity
In this paper, we first identify semantic heterogeneities that, when not resolved, often cause serious data quality problems. We discuss the especially challenging problems of temporal and aggregational ontological heterogeneity, which concerns how complex entities and their relationships are aggreg...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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2005
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| Online Access: | http://hdl.handle.net/1721.1/30208 |
| _version_ | 1826190428770140160 |
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| author | Zhu, Hongwei Madnick, Stuart E. |
| author_facet | Zhu, Hongwei Madnick, Stuart E. |
| author_sort | Zhu, Hongwei |
| collection | MIT |
| description | In this paper, we first identify semantic heterogeneities that, when not resolved, often cause serious data quality problems. We discuss the especially challenging problems of temporal and aggregational ontological heterogeneity, which concerns how complex entities and their relationships are aggregated and reinterpreted over time. Then we illustrate how the COntext INterchange (COIN) technology can be used to capture data semantics and reconcile semantic heterogeneities in a scalable manner, thereby improving data quality. |
| first_indexed | 2024-09-23T08:40:07Z |
| format | Article |
| id | mit-1721.1/30208 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:40:07Z |
| publishDate | 2005 |
| record_format | dspace |
| spelling | mit-1721.1/302082019-04-09T19:07:11Z Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity Zhu, Hongwei Madnick, Stuart E. Data Semantics Semantic Heterogeneity Aggregation Temporal Ontology Context In this paper, we first identify semantic heterogeneities that, when not resolved, often cause serious data quality problems. We discuss the especially challenging problems of temporal and aggregational ontological heterogeneity, which concerns how complex entities and their relationships are aggregated and reinterpreted over time. Then we illustrate how the COntext INterchange (COIN) technology can be used to capture data semantics and reconcile semantic heterogeneities in a scalable manner, thereby improving data quality. Singapore-MIT Alliance (SMA) 2005-12-14T18:57:13Z 2005-12-14T18:57:13Z 2006-01 Article http://hdl.handle.net/1721.1/30208 en Computer Science (CS) 399345 bytes application/pdf application/pdf |
| spellingShingle | Data Semantics Semantic Heterogeneity Aggregation Temporal Ontology Context Zhu, Hongwei Madnick, Stuart E. Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity |
| title | Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity |
| title_full | Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity |
| title_fullStr | Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity |
| title_full_unstemmed | Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity |
| title_short | Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity |
| title_sort | addressing the challenges of aggregational and temporal ontological heterogeneity |
| topic | Data Semantics Semantic Heterogeneity Aggregation Temporal Ontology Context |
| url | http://hdl.handle.net/1721.1/30208 |
| work_keys_str_mv | AT zhuhongwei addressingthechallengesofaggregationalandtemporalontologicalheterogeneity AT madnickstuarte addressingthechallengesofaggregationalandtemporalontologicalheterogeneity |