Semantic Context Similarity
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
In general, various graph similarity measures have been proposed in graph theory such as Graph Edit Distance and Subgraph isomorphism, but these techniques do not consider the semantics of the graph stricture. There are couple of existing work which uses graph similarity for the RDF data model. Of them most common areas are,
- Ontology Alignment
- Graph Matching Approaches for ontology alignments which looks at the structural similarity of the two graphs [1][2]
- Matching two graph patterns for querying
- Semantic Search via mapping query graph and resource graph [3]
- Path Alignment of the query graph and resource graph [4]
Other Related Areas
Context Aware Recommender Systems [5]
Dataset with context: http://212.235.187.145/spletnastran/raziskave/um/comoda/comoda.php
Graph Matching for Context Recognition [6]
References
[1] Hu, Wei, et al. "Gmo: A graph matching for ontologies." Proceedings of K-CAP Workshop on Integrating Ontologies. 2005.
[2]Tous, Rubén, and Jaime Delgado. "A vector space model for semantic similarity calculation and OWL ontology alignment." Database and Expert Systems Applications. Springer Berlin Heidelberg, 2006.
[3] Jiwei, Haiping Zhu, et al. "An approach for semantic search by matching rdf graphs." In Proceedings of the Special Track on Semantic Web at the 15th International FLAIRS Conference (sponsored by AAAI. 2002.
[4]De Virgilio, Roberto, Antonio Maccioni, and Riccardo Torlone. "A similarity measure for approximate querying over RDF data." Proceedings of the Joint EDBT/ICDT 2013 Workshops. ACM, 2013.
[5]Adomavicius, Gediminas, and Alexander Tuzhilin. "Context-aware recommender systems." Recommender systems handbook. Springer US, 2011. 217-253
[6] Dobrescu, Adrian, and Andrei Olaru. "Graph matching for context recognition." Control Systems and Computer Science (CSCS), 2013 19th International Conference on. IEEE, 2013.
