Ing. Milan Dojčinovski, Ph.D.

2012

This project will develop cutting-edge concepts and technologies in areas of software and web engineering that will include six topics: (1) model-driven development of software, (2) interoperability of object-oriented languages, (3) Intelligent filtering of news articles on the Web, (4) software ecosystems, (5) semantic representation of services and applications, (6) semantics and dynamics of users` interests. This project will enable integration of research results within these topics, improve organization of work via better management of work, integration of research results` evaluation with better distribution of research funds that will reflect actual research outcomes.

2011

Web Services enhanced with semantics, known as Semantic Web Services, have been in an active development for one decade now. They leverage expressive semantic languages for description of services and they use logical reasoning and problem-solving methods to enable automation of service provisioning (such as discovery, selection, composition, mediation and invocation of services). Many works in this area have built problem-solving infrastructures that act on behalf of users with one goal to reduce their manual activities and replace them by intelligent behavior of software agents. Although they provide very sophisticated automation methods, they usually assume that software agents can ultimately replace users' activities. They rely only on an explicit desired state and a desired quality that should result from the service provisioning and not how users could participate in the provisioning process. They do not take into account a wider user's environmental context such as his/her prese

2019 - 2023

The main aim of this Action will be to promote synergies across Europe between linguists, computer scientists, terminologists, and other stakeholders in industry and society, in order to investigate and extend the area of linguistic data science. We understand linguistic data science as a subfield of the emerging “data science”, which focuses on the systematic analysis and study of the structure and properties of data at a large scale, along with methods and techniques to extract new knowledge and insights from it. Linguistic data science is a specific case, which is concerned with providing a formal basis to the analysis, representation, integration and exploitation of language data (syntax, morphology, lexicon, etc.). In fact, the specificities of linguistic data are an aspect largely unexplored so far in a big data context. In order to support the study of linguistic data science in the most efficient and productive way, the construction of a mature holistic ecosystem of multilingual and semantically interoperable linguistic data will be required at Web scale. Such an ecosystem, unavailable today, is needed to foster the systematic cross-lingual discovery, exploration, exploitation, extension, curation and quality control of linguistic data . We argue that linked data (LD) technologies, in combination with natural language processing (NLP) techniques and multilingual language resources (LRs) (bilingual dictionaries, multilingual corpora, terminologies, etc.), have the potential to enable such an ecosystem that will allow for transparent information flow across linguistic data sources in multiple languages, by addressing the semantic interoperability problem.

2014

The goal of this project will be to research and develop novel methods for Web services recommendation that will take into account their dynamics. This project will build on the results of previous SGS projects where we developed base datasets about web services and base methods for recommendation. In this project we further plan to utilize these datasets, extend them with new information and develop new methods that will take into account web services age and their lifecycle. The research of this project will continue to address research questions that were identified during the AAAI symposium in Stanford in March 2012 entitled Intelligent Web Services Meet Social Computing. In upcoming years we plan to organize the similar event.

2013

The goal of the project will be to research and develop novel crowdsourcing methods for web service annotation and methods for analysis of web service descriptions and their recommendations. We will evaluate results of this research on a set of web services from public web service directories such as ProgrammableWeb.com. We will evaluate crowdsourcing methods on a group of students who will attend the Web 2.0 course in the upcoming summer semester. This research project directly addresses selected questions that have been discussed during the AAAI Spring Symposia in Stanford in March 2012 entitled Intelligent Web Services Meet Social Computing. Doc. Ing. Tomáš Vitvar, Ph.D. was a co-chair of this symposium.