Difference between revisions of "Semantic Context Similarity"
From Knoesis wiki
(→Progress) |
|||
| Line 2: | Line 2: | ||
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. | ||
| − | == | + | ==Notes== |
Currently we have identified two possible ways to approach the problem, | Currently we have identified two possible ways to approach the problem, | ||
*Represent context as a multidimensional model and use semantic similarity techniques to calculate the similarity of two contexts | *Represent context as a multidimensional model and use semantic similarity techniques to calculate the similarity of two contexts | ||
Revision as of 21:22, 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.
Notes
Currently we have identified two possible ways to approach the problem,
- Represent context as a multidimensional model and use semantic 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
