A RetroSearch Logo

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Search Query:

Showing content from https://link.springer.com/chapter/10.1007/978-3-031-06981-9_14 below:

Learning Concept Lengths Accelerates Concept Learning in ALC

References

  1. Ashburner, M., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)

    Article  Google Scholar 

  2. Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  3. Badea, L., Nienhuys-Cheng, S.-H.: A refinement operator for description logics. In: Cussens, J., Frisch, A. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 40–59. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44960-4_3

    Chapter  Google Scholar 

  4. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(2) (2012)

    Google Scholar 

  5. Bin, S., Bühmann, L., Lehmann, J., Ngonga Ngomo, A.C.: Towards SPARQL-based induction for large-scale RDF data sets. In: ECAI 2016, pp. 1551–1552, IOS Press (2016)

    Google Scholar 

  6. Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94(2), 233–259 (2013). https://doi.org/10.1007/s10994-013-5363-6

    Article  MathSciNet  MATH  Google Scholar 

  7. Bühmann, L., Lehmann, J., Westphal, P.: DL-Learner—a framework for inductive learning on the Semantic Web. J. Web Semant. 39, 15–24 (2016)

    Article  Google Scholar 

  8. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  9. Gene Ontology Consortium: The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 32(suppl1), D258–D261 (2004)

    Article  Google Scholar 

  10. Dai, Y., Wang, S., Xiong, N.N., Guo, W.: A survey on knowledge graph embedding: approaches, applications and benchmarks. Electronics 9(5), 750 (2020)

    Article  Google Scholar 

  11. Demir, C., Ngomo, A.C.N.: Convolutional complex knowledge graph embeddings. arXiv preprint arXiv:2008.03130 (2020)

  12. Deshpande, O., et al.: Building, maintaining, and using knowledge bases: a report from the trenches. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 1209–1220 (2013)

    Google Scholar 

  13. Fanizzi, N., d’Amato, C., Esposito, F.: DL-FOIL concept learning in description logics. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 107–121. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85928-4_12

    Chapter  Google Scholar 

  14. Heindorf, S., et al.: EvoLearner: Learning description logics with evolutionary algorithms. In: Proceedings of the ACM Web Conference (2022)

    Google Scholar 

  15. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  16. Hogan, A., et al.: Knowledge graphs. Synth. Lect. Data Semant. Knowl. 12(2), 1–257 (2021)

    Article  Google Scholar 

  17. Ioannidis, V.N., et al.: DRKG-drug repurposing knowledge graph for COVID-19 (2020)

    Google Scholar 

  18. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  19. Kouagou, N.J., Heindorf, S., Demir, C., Ngomo, A.N.: Neural class expression synthesis. CoRR abs/2111.08486 (2021)

    Google Scholar 

  20. Krötzsch, M., Simancik, F., Horrocks, I.: A description logic primer. CoRR abs/1201.4089 (2012)

    Google Scholar 

  21. Lehmann, J.: DL-Learner: learning concepts in description logics. J. Mach. Learn. Res. 10, 2639–2642 (2009)

    MathSciNet  MATH  Google Scholar 

  22. Lehmann, J.: Learning OWL Class Expressions, vol. 22. IOS Press (2010)

    Google Scholar 

  23. Lehmann, J., Auer, S., Bühmann, L., Tramp, S.: Class expression learning for ontology engineering. J. Web Semant. 9(1), 71–81 (2011)

    Article  Google Scholar 

  24. Lehmann, J., Hitzler, P.: Concept learning in description logics using refinement operators. Mach. Learn. 78(1–2), 203 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  25. MacLean, F.: Knowledge graphs and their applications in drug discovery. Expert Opin. Drug Discov. 16(9), 1057–1069 (2021)

    Article  Google Scholar 

  26. Nickel, M., Tresp, V., Kriegel, H.P.: Factorizing YAGO: scalable machine learning for linked data. In: Proceedings of the 21st international conference on World Wide Web, pp. 271–280 (2012)

    Google Scholar 

  27. Percha, B., Altman, R.B.: A global network of biomedical relationships derived from text. Bioinformatics 34(15), 2614–2624 (2018)

    Article  Google Scholar 

  28. Rizzo, G., Fanizzi, N., d’Amato, C.: Class expression induction as concept space exploration: from DL-Foil to DL-Focl. Future Gener. Comput. Syst. 108, 256–272 (2020)

    Article  Google Scholar 

  29. Rizzo, G., Fanizzi, N., d’Amato, C., Esposito, F.: A framework for tackling myopia in concept learning on the web of data. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds.) EKAW 2018. LNCS (LNAI), vol. 11313, pp. 338–354. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03667-6_22

    Chapter  MATH  Google Scholar 

  30. Rudolph, S.: Foundations of description logics. In: Polleres, A., d’Amato, C., Arenas, M., Handschuh, S., Kroner, P., Ossowski, S., Patel-Schneider, P. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 76–136. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23032-5_2

    Chapter  Google Scholar 

  31. Sarker, M.K., Hitzler, P.: Efficient concept induction for description logics. In: AAAI, pp. 3036–3043 (2019)

    Google Scholar 

  32. Schmidt-Schauß, M., Smolka, G.: Attributive concept descriptions with complements. Artif. Intell. 48(1), 1–26 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  33. Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)

    Article  Google Scholar 

  34. Wang, Z., Li, J., Liu, Z., Tang, J.: Text-enhanced representation learning for knowledge graph. In: Proceedings of International Joint Conference on Artificial Intelligent (IJCAI), pp. 4–17 (2016)

    Google Scholar 

  35. Weston, J., Bordes, A., Yakhnenko, O., Usunier, N.: Connecting language and knowledge bases with embedding models for relation extraction. arXiv preprint arXiv:1307.7973 (2013)

  36. Wishart, D.S., et al.: DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46(D1), D1074–D1082 (2018)

    Google Scholar 

  37. Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)

    Google Scholar 

  38. Zaheer, M., Kottur, S., Ravanbakhsh, S., Póczos, B., Salakhutdinov, R., Smola, A.J.: Deep sets. In: NIPS, pp. 3391–3401 (2017)

    Google Scholar 

Download references

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4