Filters
cris logo
-
New search Filters
Select from list
Displayed from Show more
You have reached the maximum number of displayed search results.
All results displayed
No results are available for the search performed
Displayed from
You have reached the maximum number of displayed search results.
All results displayed
Loading results
Obtaining statistical data

Projects / Programmes source: ARIS

Knowledge Technologies

Edit
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
Evaluation of bibliographic research performance indicators according to ARIS methodology
Points
18,516.11
A''
3,809.14
A'
7,870.91
A1/2
10,578.24
CI10
17,795
CImax
628
h10
56
A1
62.52
A3
28.8
Data for the last 5 years (citations for the last 10 years) on April 19, 2024; A3 for period 2018-2022
Data for ARIS 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  835  15,855  14,078  16.86 
Scopus  1,217  26,144  23,046  18.94 
Researchers (38)
no. Code Name and surname Research area Role Period No. of publications
1.  02749  PhD Marko Bohanec  Computer science and informatics  Researcher  2022 - 2024  639 
2.  53934  PhD Hashem Esmaeim Bordbar  Mathematics  Researcher  2023 - 2024  57 
3.  22278  PhD Janez Brank  Computer science and informatics  Researcher  2022 - 2024  95 
4.  36220  PhD Martin Breskvar  Computer science and informatics  Researcher  2022 - 2023  36 
5.  53484  PhD Michelangelo Ceci  Computer science and informatics  Researcher  2022 - 2024  23 
6.  05806  PhD Bojan Cestnik  Computer science and informatics  Researcher  2022 - 2024  196 
7.  15660  PhD Marko Debeljak  Biology  Researcher  2022 - 2024  313 
8.  11130  PhD Sašo Džeroski  Computer science and informatics  Head  2022 - 2024  1,204 
9.  05023  PhD Tomaž Erjavec  Linguistics  Researcher  2022 - 2024  636 
10.  17137  Marko Grobelnik  Computer science and informatics  Technical associate  2022 - 2024  439 
11.  57501  PhD Seyed Ahmad Hosseini  Computer intensive methods and applications  Researcher  2023 - 2024  27 
12.  32282  PhD Aneta Ivanovska  Computer science and informatics  Researcher  2022  125 
13.  53454  Jakob Jelenčič  Computer science and informatics  Junior researcher  2022 - 2024  10 
14.  31050  PhD Dragi Kocev  Computer science and informatics  Researcher  2022 - 2024  204 
15.  53530  Ana Kostovska  Computer science and informatics  Junior researcher  2022 - 2024  41 
16.  31885  PhD Aljaž Košmerlj  Computer science and informatics  Researcher  2022  34 
17.  28291  PhD Petra Kralj Novak  Computer science and informatics  Researcher  2022 - 2024  130 
18.  39558  PhD Vladimir Kuzmanovski  Computer science and informatics  Researcher  2022  43 
19.  08949  PhD Nada Lavrač  Computer science and informatics  Researcher  2022 - 2024  867 
20.  35470  PhD Jurica Levatić  Computer science and informatics  Researcher  2022 - 2023  43 
21.  55188  Katja Meden  Information science and librarianship  Junior researcher  2022 - 2024  26 
22.  57153  Sebastian Mežnar  Computer science and informatics  Junior researcher  2022 - 2024  11 
23.  12570  PhD Dunja Mladenić  Computer science and informatics  Researcher  2022 - 2024  662 
24.  03323  PhD Igor Mozetič  Computer science and informatics  Researcher  2022 - 2024  184 
25.  55795  Nina Omejc  Computer science and informatics  Junior researcher  2022 - 2024  13 
26.  36356  PhD Aljaž Osojnik  Computer science and informatics  Researcher  2022 - 2024  47 
27.  27759  PhD Panče Panov  Computer science and informatics  Researcher  2022 - 2024  155 
28.  38206  PhD Matej Petković  Computer science and informatics  Researcher  2022 - 2023  65 
29.  29539  PhD Vid Podpečan  Computer science and informatics  Researcher  2022 - 2024  103 
30.  31844  PhD Senja Pollak  Linguistics  Researcher  2022 - 2024  288 
31.  53851  PhD Matthew RJ Purver  Linguistics  Researcher  2022 - 2024  106 
32.  34452  PhD Nikola Simidjievski  Computer science and informatics  Researcher  2022 - 2024  58 
33.  52066  PhD Blaž Škrlj  Computer science and informatics  Researcher  2022 - 2024  126 
34.  39597  PhD Jovan Tanevski  Computer science and informatics  Researcher  2022 - 2024  33 
35.  16302  PhD Ljupčo Todorovski  Computer science and informatics  Researcher  2022 - 2024  443 
36.  04586  PhD Tanja Urbančič  Computer science and informatics  Researcher  2022 - 2023  290 
37.  22279  PhD Bernard Ženko  Computer science and informatics  Researcher  2022 - 2024  172 
38.  23582  PhD Martin Žnidaršič  Computer science and informatics  Researcher  2022 - 2024  165 
Organisations (2)
no. Code Research organisation City Registration number No. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  90,682 
2.  1540  University of Nova Gorica  Nova Gorica  5920884000  14,063 
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.
Confirmation required
Views history
Favourite