Difference between revisions of "Semantic Context Similarity"
| Line 8: | Line 8: | ||
==Related Work== | ==Related Work== | ||
| + | ===Semantic Similarity=== | ||
| + | Semantic Similarity measures have been used to calculate the similarity of given two entities(can be words or concepts). Most of the existing semantic similarity measures mainly can be categorized into three areas, | ||
| + | *Text based similarity - Syntactic similarities of words Eg: Levenshtein, Cosine Similarity, Jaccard | ||
| + | *Corpus based similarity - Leverage information obtained from a corpus Eg: PMI, LSA, ESA, Distributional Similarity | ||
| + | *Knowledge based similarity - Leverage a semantic network to calculate the similarity, Similarity is based on the shortest path distance of the two entities and/or information content of the least common subsumer | ||
| + | '''Limitation''': Always consider the abstract unit as an entity or word, but this can be a more complex structure than such as multidimensional model or graph based model | ||
===Semantic Similarity on RDF Graphs=== | ===Semantic Similarity on RDF Graphs=== | ||
| + | Existing work on the similarity of RDF graphs is mainly done in, | ||
| + | * | ||
Revision as of 21:44, 26 April 2015
Contents
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
Related Work
Semantic Similarity
Semantic Similarity measures have been used to calculate the similarity of given two entities(can be words or concepts). Most of the existing semantic similarity measures mainly can be categorized into three areas,
- Text based similarity - Syntactic similarities of words Eg: Levenshtein, Cosine Similarity, Jaccard
- Corpus based similarity - Leverage information obtained from a corpus Eg: PMI, LSA, ESA, Distributional Similarity
- Knowledge based similarity - Leverage a semantic network to calculate the similarity, Similarity is based on the shortest path distance of the two entities and/or information content of the least common subsumer
Limitation: Always consider the abstract unit as an entity or word, but this can be a more complex structure than such as multidimensional model or graph based model
Semantic Similarity on RDF Graphs
Existing work on the similarity of RDF graphs is mainly done in,
