ISSN 1997-7670 (Print)
ISSN 2541-8785 (Online)

List of issues > Series «Mathematics». 2017. Vol. 21

Document Models

A.A. Malykh, A.V. Mantsivoda

In this paper, the concept of locally simple models is considered. Locally simple models are arbitrarily complex models built from relatively simple components. A lot of practically important domains of discourse can be described as locally simple models, for example, business models of enterprises and companies. Up to now, research in human reasoning automation has been mainly concentrated around the most intellectually intensive activities, such as automated theorem proving. On the other hand, the retailer business model is formed from ”jobs”, and each ”job” can be modelled and automated more or less easily. At the same time, the whole retailer model as an integrated system is extremely complex. In this paper, we offer a variant of the mathematical definition of a locally simple model. This definition is intended for modelling a wide range of domains. Therefore, we also must take into account the perceptual and psychological issues. Logic is elitist, and if we want to attract to our models as many people as possible, we need to hide this elitism behind some metaphor, to which ’ordinary’ people are accustomed. As such a metaphor, we use the concept of a document, so our locally simple models are called document models. Document models are built in the paradigm of semantic programming. This allows us to achieve another important goal - to make the documentary models executable. Executable models are models that can act as practical information systems in the described domain of discourse. Thus, if our model is executable, then programming becomes redundant. The direct use of a model, instead of its programming coding, brings important advantages, for example, a drastic cost reduction for development and maintenance. Moreover, since the model is well and sound, and not dissolved within programming modules, we can directly apply AI tools, in particular, machine learning. This significantly expands the possibilities for automation and robotization of management and control activities.

locally simple model, document model, semantic programming

1. Vityev Е. Semantic Probablistic Inference of Predictions. Izv. Irkutsk. Gos. Univ. Ser. Mat., 2017, vol. 21. (in Russian)

2. Malykh A., Mantsivoda A., V.S.Ulyanov. Logical Architectures and Object Oriented Approach. Vestnik NGU. Series: Mathematics, Mechanics, Informatics., 2009, vol.9, issue 3, pp. 64-85. (In Russian)

3. Malykh A., Mantsivoda A. Object Theories over List Superstructures. Izv. Irkutsk. Gos. Univ. Ser. Mat., 2012, no 4, pp. 27-44. (in Russian)

4. Malykh A., Mantsivoda A. Sistema Libretto: razrabotka web-resursov v edinoi modeli dannykh i znanii [Libretto System: Web Resources Development Based On a Holistic Data and Knowledge Model]. In: Proceedings of The 6th All-RussianConference on Control Problems, Gelendzhik, September, pp.73-75.

5. Berners-Lee T., Hendler J., Lassila O. The Semantic Web. Scientific American, May 2001. https://doi.org/10.1038/scientificamerican0501-34

6. Goncharov S.S., Ershov Yu.L., Sviridenko, D.I. Semantic foundations of programming. Lecture Notes in Computer Science, 1987, vol. 278, pp. 116-122. https://doi.org/10.1007/3-540-18740-5_28

7. Goncharov S.S., Ershov Yu.L., Sviridenko D.I. Semantic programming. In: Information processing, Proc. IFIP 10th World Comput. Congress, Dublin, 1986, vol.10, pp.1093-1100.

8. Horrocks I., Patel-Schneider P., Van Harmelen F. From SHIQ and RDF to OWL: The making of a Web Ontology Language. Journal of Web Semantics, v.1, no 1, pp.7–26. https://doi.org/10.1016/j.websem.2003.07.001

9. Horrocks I., Sattler U., Tobies U. Practical reasoning for expressive description logics. In: H. Ganzinger, D. McAllester, and A. Voronkov, editors, Proceedings of the 6th International Conference on Logic for Programming and Automated Reasoning (LPAR’99), no 1705 in Lecture Notes in Artificial Intelligence, Springer-Verlag, 1999, pp. 161-180. https://doi.org/10.1007/3-540-48242-3_11

10. Kovalerchuk B., Vityaev E. Data Mining in finance: Advances in Relational and Hybrid Methods. Kluwer Academic Publishers, 2001. 456 p.

11. Malykh A., Mantsivoda A.A. Query Language for Logic Architectures. Lecture Notes in Computer Science, no 5947, Springer-Verlag, Berlin Heidelberg, 2010, pp.294–305. https://doi.org/10.1007/978-3-642-11486-1_25

12. Robinson J.A. A Machine Oriented Logic Based on the Resolution Principle. JACM, 1965, no 12, pp.23-41. https://doi.org/10.1145/321250.321253

13. Riazanov A., Voronkov A. The Design and Implementation of VAMPIRE. Journal AI Communications, 2002, vol. 15, issue 2,3, pp.91-110.

14. Semantic Web activity. http://www.w3.org/2001/sw/.

Full text (russian)