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Projects / Programmes source: ARRS

Knowledge Technologies

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Periods
January 1, 2022 - December 31, 2027
Research activity

Code Science Field Subfield
2.07.00  Engineering sciences and technologies  Computer science and informatics   
6.05.00  Humanities  Linguistics   

Code Science Field
1.02  Natural Sciences  Computer and information sciences 
6.02  Humanities  Languages and Literature 
Keywords
machine learning, artificial intelligence, complex data, transfer learning, text mining, network analytics, decision support, language technologies, natural language processing, digital humanities, computational scientific discovery, ontologies, semantic technologies, open science
Evaluation (rules)
source: COBISS
Points
17,072.25
A''
3,378.66
A'
6,712.99
A1/2
9,243.58
CI10
15,660
CImax
597
h10
53
A1
56.19
A3
59.31
Data for the last 5 years (citations for the last 10 years) on May 28, 2023; A3 for period 2017-2021
Data for ARRS tenders ( 04.04.2019 – Programme tender , archive )
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Database Linked records Citations Pure citations Average pure citations
WoS  732  13,801  12,276  16.77 
Scopus  1,086  22,957  20,216  18.62 
Researchers (36)
no. Code Name and surname Research area Role Period No. of publications
1.  02749  PhD Marko Bohanec  Engineering sciences and technologies  Researcher  2022 - 2023  632 
2.  22278  PhD Janez Brank  Engineering sciences and technologies  Researcher  2022 - 2023  92 
3.  36220  PhD Martin Breskvar  Engineering sciences and technologies  Researcher  2022 - 2023  31 
4.  53484  PhD Michelangelo Ceci  Engineering sciences and technologies  Researcher  2022 - 2023  15 
5.  05806  PhD Bojan Cestnik  Engineering sciences and technologies  Researcher  2022 - 2023  189 
6.  15660  PhD Marko Debeljak  Natural sciences and mathematics  Researcher  2022 - 2023  303 
7.  11130  PhD Sašo Džeroski  Engineering sciences and technologies  Principal Researcher  2022 - 2023  1,171 
8.  05023  PhD Tomaž Erjavec  Humanities  Researcher  2022 - 2023  599 
9.  17137  Marko Grobelnik  Engineering sciences and technologies  Technician  2022 - 2023  419 
10.  32282  PhD Aneta Ivanovska  Engineering sciences and technologies  Researcher  2022 - 2023  122 
11.  53454  Jakob Jelenčič  Engineering sciences and technologies  Junior researcher  2022 - 2023  8 
12.  31050  PhD Dragi Kocev  Engineering sciences and technologies  Researcher  2022 - 2023  194 
13.  53530  Ana Kostovska  Engineering sciences and technologies  Junior researcher  2022 - 2023  32 
14.  31885  PhD Aljaž Košmerlj  Engineering sciences and technologies  Researcher  2022  34 
15.  28291  PhD Petra Kralj Novak  Engineering sciences and technologies  Researcher  2022 - 2023  127 
16.  39558  PhD Vladimir Kuzmanovski  Engineering sciences and technologies  Researcher  2022 - 2023  43 
17.  08949  PhD Nada Lavrač  Engineering sciences and technologies  Researcher  2022 - 2023  852 
18.  35470  PhD Jurica Levatić  Engineering sciences and technologies  Researcher  2022 - 2023  38 
19.  55188  Katja Meden  Social sciences  Junior researcher  2022 - 2023  14 
20.  57153  Sebastian Mežnar  Engineering sciences and technologies  Junior researcher  2022 - 2023  7 
21.  12570  PhD Dunja Mladenić  Engineering sciences and technologies  Researcher  2022 - 2023  635 
22.  03323  PhD Igor Mozetič  Engineering sciences and technologies  Researcher  2022 - 2023  181 
23.  55795  Nina Omejc  Engineering sciences and technologies  Junior researcher  2022 - 2023  11 
24.  36356  PhD Aljaž Osojnik  Engineering sciences and technologies  Researcher  2022 - 2023  44 
25.  27759  PhD Panče Panov  Engineering sciences and technologies  Researcher  2022 - 2023  136 
26.  38206  PhD Matej Petković  Engineering sciences and technologies  Researcher  2022 - 2023  59 
27.  29539  PhD Vid Podpečan  Engineering sciences and technologies  Researcher  2022 - 2023  89 
28.  31844  PhD Senja Pollak  Humanities  Researcher  2022 - 2023  230 
29.  53851  PhD Matthew RJ Purver  Humanities  Researcher  2022 - 2023  64 
30.  34452  PhD Nikola Simidjievski  Engineering sciences and technologies  Researcher  2022 - 2023  54 
31.  52066  PhD Blaž Škrlj  Engineering sciences and technologies  Researcher  2022 - 2023  113 
32.  39597  PhD Jovan Tanevski  Engineering sciences and technologies  Researcher  2022 - 2023  33 
33.  16302  PhD Ljupčo Todorovski  Engineering sciences and technologies  Researcher  2022 - 2023  431 
34.  04586  PhD Tanja Urbančič  Engineering sciences and technologies  Researcher  2022 - 2023  289 
35.  22279  PhD Bernard Ženko  Engineering sciences and technologies  Researcher  2022 - 2023  166 
36.  23582  PhD Martin Žnidaršič  Engineering sciences and technologies  Researcher  2022 - 2023  154 
Organisations (2)
no. Code Research organisation City Registration number No. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  85,680 
2.  1540  University of Nova Gorica  Nova Gorica  5920884000  13,237 
Abstract
Knowledge technologies (KT) are information technologies that support the acquisition, management, modelling and use of knowledge and data. KT cover many areas of artificial intelligence (AI), such as machine learning (ML) and language technologies (LT) (incl. natural language processing, NLP), as well as decision support (DS, closely associated with operations research and decision analysis). Our KT research programme will address challenging research topics in the field, designing and implementing new and improved methods, and applying KT approaches to practically relevant problems from science and society. It will comprise five pillars: ML, DS & AI, AI for Science, LT & Digital Humanities (DH), and KT for society. Building on our previous achievements in ML from complex data (which include efficient and effective methods for simultaneous prediction of multiple targets (MTP), also on data streams, and semantic data mining using ontologies), we will develop methods for ML from data of unprecedented complexity. We will address multi-target prediction from relational data, change detection/adaptation for MTP on time-evolving data streams, and neuro-symbolic learning on semantically enriched heterogeneous data, where we will exploit the advantages of both modern neural network-based embeddings and classical learning of explainable models. Explainable AI will be studied in the pillar DS&AI, in the context of both ML and DS, together with trustworthiness in AI (along the dimensions of fairness, robustness and sustainability). In DS, current decision modelling methods will be extended to more complex decision alternatives and also used to evaluate predictive models along multiple criteria. Methods will also be developed to propose new decision alternatives, which make minimal changes to existing ones, but are better suited to a given set of decision criteria. LT tools will be developed for less-resourced languages, based on open language resources (also for Slovenian), exploiting neural transfer learning approaches; the latter will also be used for the analysis of both text and network information in social networks. Our DH research will consider novel types of cultural heritage, such as olfactory and silk heritage, enhancing their understanding and protection. In the AI for Science pillar, we will develop semantic technologies for open science (e.g., ontologies for scientific knowledge) and automate scientific modelling, supporting collaborative and open science across a range of scientific disciplines. We will apply KT, especially ML, to (data from) different sciences and consider the synergies of ML and quantum computing. Finally, we will demonstrate the utility and societal impact of KT in various domains, ranging from sustainable agriculture, through personalized medicine/ healthcare, media, education and arts, and various industrial sectors (energy, transport, space).
Significance for science
Knowledge technologies (KT) are experiencing rapid scientific development in the areas of machine learning (ML), decision support (DS), artificial intelligence (AI), and language technologies (LT), as well as a large increase in practical adoption and a concomitant increase of industrial and public interest. Research performed within the proposed KT programme will advance science, technology and innovations in all of the above areas, corresponding roughly to research pillars of the programme. In ML, we will make novel contributions to the analysis of complex data, considering as yet unsolved problems, such as multi-target prediction from relational data and change detection/adaptation for multi-target prediction on time-evolving data streams. In a very novel research direction, we will consider neuro-symbolic learning on semantically enriched heterogeneous data, combining standard learning approaches and neural networks/embeddings to surpass both. Neural transfer learning approaches will be exploited to develop LT tools for less-resourced languages, as well as for the analysis of both text and network information in social networks. In DS, current decision modelling methods will be extended to more complex decision alternatives and also used to evaluate predictive models along multiple criteria. One of the pillars of our programme is concerned with AI for Science, considering the use of semantic technologies for open science (e.g., ontologies for formal representation of scientific knowledge). We argue that not only scientific data needs to be FAIR (Findable, Accessible, Interoperable, Reusable), but also other products of the scientific process (e.g., scientific models), and develop approaches to support this. We also propose methods for the automation of scientific modelling and thus contribute to the development of collaborative and open science across a range of scientific disciplines. Since we will apply knowledge technologies, especially ML, to (data from) different sciences, we will contribute not only to the development of computer science, but also to the development of other scientific areas. The most novel research area includes quantum technologies and in particular quantum computing, where we will consider the synergies of the two areas (quantum computing and ML). Other relevant topics will include material sciences, life sciences and ecology/environmental sciences. Our research in LT and digital humanities, as well as text and network analytics, will also contribute to the development of humanities and social sciences (e.g., by studying human interactions in the context of social networks). To maximize the impact on the development of science, we will continue to publish in prestigious journals with high Impact Factors, both in the general area of AI/KT (JAIR, AIJ, KBS), and in journals specific to the constituent areas (JMLR, MLJ, DAMI for ML; CK, LREV for LT). We will also present papers at top tier conference, e.g., IJCAI, ECAI, ICML, NeurIPS, ECML/PKDD, NAACL, LREC. We will - whenever possible - follow FAIR principles in publication. Our publications, developed software and datasets will be made openly available and published on public software and data repositories, such as Zenodo, CLARIN.SI, and GitHub. Moreover, developed methodologies, implemented as data processing workflows, incorporating representation learning and machine learning algorithms, will also be made publicly available. We will also exploit our collaborative networks and continue with leading and collaborating in international scientific research endeavours, thus further strengthening our key role in the research areas of KT (as exemplified by our successful participation in international projects, including 71 EU projects in the 2015-2021 program funding period, five of which we coordinated).
Significance for the country
Through the many applications of knowledge technologies (KT) planned, the research programme will have strong societal impact in many different areas. Our work on language technologies, especially on open language resources, contributes to the accessibility of Slovenian language, a cultural pillar of Slovenia. Slovenian language data, provided via the CLARIN.SI infrastructure, already supports teaching and research on Slovenian language at universities and research institutes in Slovenia, the work of Slovenian lexicologists and translators, as well as the development of language-aware software. It will continue to do so to an even greater extent in the proposed programme. Our natural language processing (NLP) research focusses on state-of-the art methods for efficient transfer learning, including cross-lingual methods. This allows digitally less-resourced languages, including Slovenian, to benefit from the tools developed for more resourced languages. Several stakeholders will benefit from the planned NLP applications, including the media sector (via news analysis tools for keyword extraction, comment moderation), terminologists and translation companies (via terminology management applications). Our research in text analysis and language technologies will help monitor and understand the dynamics of society in specific domains, such as employment, where texts (job postings) are regularly published. Our research in the domain of digital humanities and cultural heritage is expected to have an impact on growing awareness of culture, enhancing understanding of our cultural heritage, and providing education and research resources. It will also provide resources for the fashion and creative industries in the area of silk heritage and olfactory heritage, as well as for the textile and fragrance industries. In the area of olfactory heritage, we will contribute policy recommendations to preserve/safeguard past and future olfactory heritage. Through applications of machine learning (ML), and especially decision support, to environmental problems, we will influence the protection of natural heritage. Our work concerning the environmental impact of agriculture, environmental protection and sustainable development will support the European Common Agricultural Policy (CAP), where the digitization of agriculture and the transition to a sustainable agri-food sector is one of the main objectives. Our impact on the development of CAP will also influence the implementation of the 2030 Agenda for Sustainable Development, monitored through a variety of sustainable development goals (SDGs). We are applying KTs, especially ML and decision modelling, to a variety of problems in the health/wellbeing arena. This includes the use of ML for drug development, especially for diseases that are emerging or becoming more frequent. It also includes the use of ML in the area of personalized medicine, e.g., for recommending patient-specific combination therapies for cancer. Using ML to improve our understanding of the causes and mechanisms underlying health is combined with the development of decision models. This leads to decision support systems that help to prevent, detect, treat and manage diseases, and support older persons to remain active and healthy. Overall, this leads to improved health and wellbeing of the population. To achieve broader impact, we organize scientific and educational events, both national and international. These include premier events in the areas of machine learning (ECML PKDD), decision support (IFIP WG DSS) and semantic technologies (The Web Conference). They also include summer schools and specialized education events for specific areas. This also increases the international visibility of Slovenia, which is well known for the high quality of its AI research and researchers, and extensive service the latter provide to the international scientific community. We also transfer the knowledge generated in our research to the younger generations through the graduate education process. Members of our group teach graduate courses at many Slovenian institutions of higher education (incl. the University of Ljubljana, University of Nova Gorica, Jožef Stefan International Postgraduate School in Ljubljana, Faculty of Information Studies in Novo Mesto. They cover topics from KT (incl. ML, NLP and decision support) and the application areas (e.g., ecological modelling and its use for agricultural ecosystems). Through the graduate education process, and especially through advising MSc and PhD student, we develop excellent young researchers and professionals in the areas of knowledge technologies and artificial intelligence. These are highly sought after and have high value on both the domestic and international labour market (all of our PhD graduates from the 2015-2021 funding period are employed, 8 in research/higher education, 12 in the business sector, and 12 abroad). In this way, we develop extremely valuable human resources, in an area where there is clearly high and growing demand for skilled researchers/professionals.
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