Topological Semantic Similarity Analysis System
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A Topological Semantic Similarity Analysis System is as Semantic Similarity Analysis System that calculates a topological semantic similarity measure between two ontological items.
- Context:
- It can solve a Topological Semantic Similarity Analysis Task by implementing a Topological Semantic Similarity Analysis Algorithm.
- It can be benchmarked by Topological Semantic Similarity Analysis System Benchmark Task.
- Example(s):
- Counter-Example(s):
- See: Semantic Word Similarity Measure, Gene Semantic Similarity Measure, Knowledge-based Semantic Similarity, Corpus-based Similarity, Taxonomy-based Semantic Similarity Measure, Ontology-based Semantic Similarity Measure, Deep Semantic Similarity Neural Network, Path Distance Similarity Measure.
References
2021
- (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Semantic_similarity#Topological_similarity Retrieved:2021-8-7.
- There are essentially two types of approaches that calculate topological similarity between ontological concepts:
- Edge-based: which use the edges and their types as the data source;
- Node-based: in which the main data sources are the nodes and their properties.
- Other measures calculate the similarity between ontological instances:
- Pairwise: measure functional similarity between two instances by combining the semantic similarities of the concepts they represent
- Groupwise: calculate the similarity directly not combining the semantic similarities of the concepts they represent
- There are essentially two types of approaches that calculate topological similarity between ontological concepts:
2011
- (Dong et al., 2011) ⇒ Hai Dong, Farookh Khadeer Hussain, and Elizabeth Chang (2011). "A Context-Aware Semantic Similarity Model for Ontology Environments". In: Concurrency and Computation: Practice and Experience 23(5).
2010
- (Benabderrahmane et al., 2010 ) ⇒ Sidahmed Benabderrahmane, Malika Smail-Tabbone, Olivier Poch, Amedeo Napoli, and Marie-Dominique Devignes (2010). "IntelliGO: a New Vector-based Semantic Similarity Measure Including Annotation Origin. BMC bioinformatics, 11(1), 1-16.
- QUOTE: Concerning the comparison between individual ontology terms, the two types of approaches reviewed by Pesquita et al. (2009) are similar to those proposed by Blanchard et al. (2008), namely the edge-based measures which rely on counting edges in the graph, and node-based measures which exploit information contained in the considered term, its descendants and its parents.
In most edge-based measures, the Shortest Path-Length (SPL) is used as a distance measure between two terms in a graph.
- QUOTE: Concerning the comparison between individual ontology terms, the two types of approaches reviewed by Pesquita et al. (2009) are similar to those proposed by Blanchard et al. (2008), namely the edge-based measures which rely on counting edges in the graph, and node-based measures which exploit information contained in the considered term, its descendants and its parents.
2009a
- (Dong et al., 2009) ⇒ Hai Dong, Farookh Khadeer Hussain, and Elizabeth Chang (2009). "A Hybrid Concept Similarity Measure Model for Ontology Environment". In: Proceedings of On the Move to Meaningful Internet Systems (OTM 2009) Workshops. Lecture Notes in Computer Science, vol 5872. Springer.
2009b
- (Pesquita et al., 2009 ) ⇒ Catia Pesquita, Daniel Faria, Andre O. Falcao, Phillip Lord, and Francisco M. Couto (2009). "Semantic Similarity in Biomedical Ontologies". In: PLoS Computational Biology 5(7): e1000443.
- QUOTE: Since the first application of semantic similarity in biology, by Lord et al. (2003) , several semantic similarity measures have been developed for use with GO, as shown in Table 1.
| Measure | Approach | Techniques |
|---|---|---|
| Resnik | Node-based | MICA |
| Lin (Lin, 1998) | Node-based | MICA |
| Jiang and Conrath (Jiang & Conrath, 1997) | Node-based | MICA |
| GraSM (Couto et al., 2005) | Node-based | DCA |
| Schlicker et al. (2006) | Node-based | MICA |
| Wu et al. (2005) | Edge-based | Shared path |
| Wu et al. (2006) | Edge-based | Shared path; distance |
| Bodenreider et al. (2008) | Node-based | Shared annotations |
| Othman et al. (2008) | Hybrid | IC/depth/number of children; distance |
| Wang et al. (2007) | Hybrid | Shared ancestors |
| Riensche et al. (2007) | Node-based | IC/MICA; shared annotations |
| Yu et al. (2005) | Edge-based | Shared path |
| Cheng et al. (2004) | Edge-based | Shared path |
| Pozo et al. (2008) | Edge-based | Shared path |
2007
- (Wang et al., 2007a) ⇒ James Z. Wang, Zhidian Du, Rapeeporn Payattakool, Philip S. Yu, and Chin-Fu Chen (2007). "A new method to measure the semantic similarity of GO terms"In: Bioinformatics 23 (10).
2008
- (Othman et al., 2008) ⇒ Razib M.Othmana, Safaai Deris, and Rosli M.Illias (2008) "A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences" In: Journal of Biomedical Informatics 41(1), Elsevier.
2003
- (Lord et al., 2003) ⇒ P. W. Lord, R. D. Stevens, A. Brass, and C. A. Goble (2003). "Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation". In: Bioinformatics. 2003;19:1275–1283.
2002
- (Pekar & Steeb, 2002) ⇒ Viktor Pekar, and Steffen Staab (2002). "Taxonomy Learning - Factoring the Structure of a Taxonomy into a Semantic Classification Decision". In: Proceeding of the 19th International Conference on Computational Linguistics (COLING 2002).
- QUOTE: ... where $\delta(a,b)$ describes the number of edges on the shortest path between $a$ and $b$. The taxonomic similarity between $a$ and $b$ is then given by
|
$T(a, b)=\dfrac{\delta(\operatorname{root}, c)}{\delta(a, c)+\delta(b, c)+\delta(root, c)}$ |
(2) |
- where $c = lcs(a,b)$. $T$ is such that $0\leq T \leq 1$, with 1 standing for the maximum taxonomic similarity.
$T$ is directly proportional to the number of edges from the least common super-concept to the root, which agrees with the intuition that a given number of edges between two concrete concepts signifies greater similarity than the same number of edges between two abstract concepts.
- where $c = lcs(a,b)$. $T$ is such that $0\leq T \leq 1$, with 1 standing for the maximum taxonomic similarity.
1998
- (Lin, 1998) ⇒ Dekang Lin. (1998). “An Information-Theoretic Definition of Similarity.” In: Proceedings of the 15th International Conference on Machine Learning (ICML 1998).
1997
- (Jiang & Conrath, 1997) ⇒ Jay J. Jiang, and David W. Conrath. (1997). “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy.” In: Proceedings on International Conference on Research in Computational Linguistics (ROCLING X).
1995
- (Resnik, 1995) ⇒ Philip Resnik. (1995). “Using Information Content to Evaluate Semantic Similarity in a Taxonomy.” In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI 1995).