Difference between revisions of "Semantic Context Similarity"
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==Problem Description== | ==Problem Description== | ||
Develop a measure of semantic relatedness between two or more contexts that represent some state-of-the-world. Context may involve information of various types, including spatial-temporal information such as location, date and time, physical information such as traffic and weather or social information such as the people in a meeting or building, etc. | Develop a measure of semantic relatedness between two or more contexts that represent some state-of-the-world. Context may involve information of various types, including spatial-temporal information such as location, date and time, physical information such as traffic and weather or social information such as the people in a meeting or building, etc. | ||
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| + | ==Progress== | ||
| + | Currently we have identified two possible ways to approach the problem, | ||
| + | *Represent context as a multidimensional model and use similarity techniques to calculate the similarity of two contexts | ||
| + | *Represent context as a graph based data mode and use graph similarity measures to calculate the similarity of two contexts | ||
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| + | ==Related Work== | ||
Revision as of 20:48, 26 April 2015
Problem Description
Develop a measure of semantic relatedness between two or more contexts that represent some state-of-the-world. Context may involve information of various types, including spatial-temporal information such as location, date and time, physical information such as traffic and weather or social information such as the people in a meeting or building, etc.
Progress
Currently we have identified two possible ways to approach the problem,
- Represent context as a multidimensional model and use similarity techniques to calculate the similarity of two contexts
- Represent context as a graph based data mode and use graph similarity measures to calculate the similarity of two contexts
