semantic similarity
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2022 ◽  
Vol 24 (3) ◽  
pp. 0-0

The cost-effective and easy availability of handheld mobile devices and ubiquity of location acquisition services such as GPS and GSM networks has helped expedient logging and sharing of location histories of mobile users. This work aims to find semantic user similarity using their past travel histories. Application of the semantic similarity measure can be found in tourism-related recommender systems and information retrieval. The paper presents Earth Mover’s Distance (EMD) based semantic user similarity measure using users' GPS logs. The similarity measure is applied and evaluated on the GPS dataset of 182 users collected from April 2007 to August 2012 by Microsoft's GeoLife project. The proposed similarity measure is compared with conventional similarity measures used in literature such as Jaccard, Dice, and Pearsons’ Correlation. The percentage improvement of EMD based approach over existing approaches in terms of average RMSE is 10.70%, and average MAE is 5.73%.


2022 ◽  
Vol 24 (3) ◽  
pp. 1-17
Author(s):  
Sunita Tiwari ◽  
Saroj Kaushik

The cost-effective and easy availability of handheld mobile devices and ubiquity of location acquisition services such as GPS and GSM networks has helped expedient logging and sharing of location histories of mobile users. This work aims to find semantic user similarity using their past travel histories. Application of the semantic similarity measure can be found in tourism-related recommender systems and information retrieval. The paper presents Earth Mover’s Distance (EMD) based semantic user similarity measure using users' GPS logs. The similarity measure is applied and evaluated on the GPS dataset of 182 users collected from April 2007 to August 2012 by Microsoft's GeoLife project. The proposed similarity measure is compared with conventional similarity measures used in literature such as Jaccard, Dice, and Pearsons’ Correlation. The percentage improvement of EMD based approach over existing approaches in terms of average RMSE is 10.70%, and average MAE is 5.73%.


Author(s):  
Ghazeefa Fatima ◽  
Rao Muhammad Adeel Nawab ◽  
Muhammad Salman Khan ◽  
Ali Saeed

Semantic word similarity is a quantitative measure of how much two words are contextually similar. Evaluation of semantic word similarity models requires a benchmark corpus. However, despite the millions of speakers and the large digital text of the Urdu language on the Internet, there is a lack of benchmark corpus for the Cross-lingual Semantic Word Similarity task for the Urdu language. This article reports our efforts in developing such a corpus. The newly developed corpus is based on the SemEval-2017 task 2 English dataset, and it contains 1,945 cross-lingual English–Urdu word pairs. For each of these pairs of words, semantic similarity scores were assigned by 11 native Urdu speakers. In addition to corpus generation, this article also reports the evaluation results of a baseline approach, namely “Translation Plus Monolingual Analysis” for automated identification of semantic similarity between English–Urdu word pairs. The results showed that the path length similarity measure performs better for the Google and Bing translated words. The newly created corpus and evaluation results are freely available online for further research and development.


2022 ◽  
Vol 139 ◽  
pp. 102629
Author(s):  
Haoran Wang ◽  
Haiping Zhang ◽  
Shangjing Jiang ◽  
Guoan Tang ◽  
Xueying Zhang ◽  
...  

Author(s):  
Anna Giabelli ◽  
Lorenzo Malandri ◽  
Fabio Mercorio ◽  
Mario Mezzanzanica ◽  
Navid Nobani
Keyword(s):  

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Juan J. Lastra-Díaz ◽  
Alicia Lara-Clares ◽  
Ana Garcia-Serrano

Abstract Background Ontology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. However, current state-of-the-art semantic measures libraries have some performance and scalability drawbacks derived from their ontology representations based on relational databases, or naive in-memory graph representations. Likewise, a recent reproducible survey on word similarity shows that one hybrid IC-based measure which integrates a shortest-path computation sets the state of the art in the family of ontology-based semantic measures. However, the lack of an efficient shortest-path algorithm for their real-time computation prevents both their practical use in any application and the use of any other path-based semantic similarity measure. Results To bridge the two aforementioned gaps, this work introduces for the first time an updated version of the HESML Java software library especially designed for the biomedical domain, which implements the most efficient and scalable ontology representation reported in the literature, together with a new method for the approximation of the Dijkstra’s algorithm for taxonomies, called Ancestors-based Shortest-Path Length (AncSPL), which allows the real-time computation of any path-based semantic similarity measure. Conclusions We introduce a set of reproducible benchmarks showing that HESML outperforms by several orders of magnitude the current state-of-the-art libraries in the three aforementioned biomedical ontologies, as well as the real-time performance and approximation quality of the new AncSPL shortest-path algorithm. Likewise, we show that AncSPL linearly scales regarding the dimension of the common ancestor subgraph regardless of the ontology size. Path-based measures based on the new AncSPL algorithm are up to six orders of magnitude faster than their exact implementation in large ontologies like SNOMED-CT and GO. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.


2022 ◽  
pp. 116466
Author(s):  
Abdullah Almuhaimeed ◽  
Mohammed A. Alhomidi ◽  
Mohammed N. Alenezi ◽  
Emad Alamoud ◽  
Saad Alqahtani

2022 ◽  
Vol 70 (3) ◽  
pp. 4763-4780
Author(s):  
Abdulaziz Al-Besher ◽  
Kailash Kumar ◽  
M. Sangeetha ◽  
Tinashe Butsa

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