Заседание семинара "Математические модели информационных технологий"

Приглашаем Вас 26 августа в 15:10 на очередное заседание семинара "Математические модели информационных технологий" Департамента анализа данных и искусственного интеллекта и НУЛ "Интеллектуальные системы и структурный анализ" под руководством Сергея Кузнецова.

Программа: два доклада профессора Тома Ханика (Германия)

 

Докладчик: Tom Hanika, Berlin School of Library and Information Science, Humboldt-Universität zu Berlin/Knowledge & Data Engineering Group, University of Kassel, Germany

Аннотация: «Collaborative Conceptual Exploration» 15:10

In domains with high knowledge distribution, a natural objective is to create principle foundations for collaborative interactive learning environments. We present in this talk a first mathematical characterization of a collaborative learning group, a consortium, based on closure systems of attribute sets and the well-known attribute exploration algorithm from Formal Concept Analysis. To this end, we introduce (weak) local experts for subdomains of a given knowledge domain. These entities are able to refute and potentially accept a given (implicational) query for some closure system that is a restriction of the whole domain. On this, we build up a consortial expert and show first insights about the ability of such an expert to answer queries. Furthermore, we depict techniques on how to cope with falsely accepted implications and on combining counterexamples. Using notions from combinatorial design theory, we further expand those insights as far as providing first results on the decidability problem if a given consortium is able to explore some target domain. (Hanika, T., Zumbrägel, J.: Towards Collaborative Conceptual Exploration. In: Chapman, P., Endres, D., and Pernelle, N. (eds.) ICCS. pp. 120–134. Springer (2018).  

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«Discovering Implicational Knowledge in Wikidata»  16:00

Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Examples include the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata. A distinguishing feature of Wikidata is that the knowledge is collaboratively edited and curated. While this greatly enhances the scope of Wikidata, it also makes it impossible for a single individual to grasp complex connections between properties or understand the global impact of edits in the graph.

In this talk, we show an application of methods from Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data. Although the complex structure of data modeling in Wikidata is not amenable to a direct approach, we overcome this limitation by extracting contextual representations of parts of Wikidata in a systematic fashion. We demonstrate the practical feasibility of our approach through several experiments and show that the results may lead to the discovery of interesting implicational knowledge. Besides providing a method for obtaining large real-world data sets for FCA, we sketch potential applications in offering semantic assistance for editing and curating Wikidata. (Hanika, T., Marx, M., Stumme, G.: Discovering Implicational Knowledge in Wikidata. In: Cristea, D., Ber, F.L., and Sertkaya, B. (eds.) ICFCA. pp. 315–323. Springer (2019).  Просим заполнить форму регистрации.

Место проведения: Покровский бульвар, 11, корпус S, ауд. S902