Important references used for framework implementation

Bioinformatics datasets

1. Chen, B., Dong, X., Jiao, D., Wang, H., Zhu, Q., Ding, Y., & Wild, D. J. (2010). Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data. BMC bioinformatics, 11(1), 255.
2. Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., ... & Overington, J. P. (2012). ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic acids research, 40(D1), D1100-D1107.
3. Belleau, F., Nolin, M. A., Tourigny, N., Rigault, P., & Morissette, J. (2008). Bio2RDF: towards a mashup to build bioinformatics knowledge systems. Journal of biomedical informatics, 41(5), 706-716.

Federated SPARQL query bilder

1. Schweiger, D., Trajanoski, Z., & Pabinger, S. (2014). SPARQLGraph: a web-based platform for graphically querying biological Semantic Web databases.BMC bioinformatics, 15(1), 1.
2. Seneviratne, O., & Sealfon, R. (2010). QueryMed: An Intuitive Federated SPARQL Query Builder for Biomedical RDF Data, Research Gate.
3. Dodds, L. (2007). Twinkle: A sparql query tool.
4. Dietze, H., & Schroeder, M. (2009). GoWeb: a semantic search engine for the life science web. BMC bioinformatics, 10(10), 1.
5. De Leon Battista, A., Villanueva-Rosales, N., Palenychka, M., & Dumontier, M. (2007, November). Smart: A web-based, ontology-driven, semantic web query answering application. In Proceedings of the 2007 International Conference on Semantic Web Challenge-Volume 295 (pp. 129-136). CEUR-WS. org.
6. Quilitz, B., & Leser, U. (2008, June). Querying distributed RDF data sources with SPARQL. In European Semantic Web Conference (pp. 524-538). Springer Berlin Heidelberg.

Vector Space Model and Cosine Similarity

1. Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613-620
2. Sidorov, G., Gelbukh, A., Gómez-Adorno, H., & Pinto, D. (2014). Soft similarity and soft cosine measure: Similarity of features in vector space model. Computación y Sistemas, 18(3), 491-504
3. Turney, P. D., & Pantel, P. (2010). From frequency to meaning: Vector space models of semantics. Journal of artificial intelligence research, 37(1), 141-188.
4. Andreas, J., & Ghahramani, Z. (2013). A generative model of vector space semantics. ACL 2013, 91.
5. Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130-137.
6. Takale, S. A., & Nandgaonkar, S. S. (2010). Measuring semantic similarity between words using web documents. International Journal of Advanced Computer Science and Applications (IJACSA), 1(4).

PIBAS

1. V. Cvjetkovic, M. Djokic, B. Arsic, M. Curcic, The ontology supported intelligent system for experiment search in the scientific Research Centre, Kragujevac Journal of Science, ISSN: 1450-9636, vol 36, pp. 95-110.
2. Cvjetkovic, Vladimir, Djokic, Marija, Semantic web based organization of scientific bibliography references, Proceedings SCIECONF (ScieConf), ISBN: 978-80-554-0891-0, ISSN: 1339-9071, vol. 3, issue 1, pp. 230 - 235, 2015
3. B. Arsic, M. Djokic, V. Cvjetkovic, P. Spalevic, M. Zivanovic and M. Mladenovic, Integration of bioactive substances data for preclinical testing with Cheminformatics and Bioinformatics resources, Proceedings of the 23nd International Electrotechnical and Computer Science Conference, ISSN:1581-45, pp. 146-149, ERK, September 22 – 24, 2014, Portorož, Slovenia