LAKE (Language and Knowledge Engineering Group)

Our group researches engineering of language and knowledge ranging from data and knowledge acquisition, to their modelling and processing, and how these can help to develop artificially intelligent systems, with a special attention to computational semantics and understanding meaning.  

LAKE research group is affiliated to Poznan University of Technology (PUT) and members of the group also belong to the Center for Artificial Intelligence and Machine Learning (CAMIL).

Team

Agnieszka Ławrynowicz
Head of the Group
Jędrzej Potoniec
Asst. Professor
Dawid Wiśniewski
Research & Teaching Assistant, PhD Student
Ewa Śniegowska
Research & Teaching Assistant, PhD Student
Maksymilian Marcinowski
PhD Student

Research topics

  • Knowledge discovery, data mining, Web mining, machine learning and deep learning with textual and structured data
  • Knowledge extraction, natural language processing and understanding, semantic computing
  • Knowledge modeling: knowledge graphs, Semantic Web & Linked Data, ontology engineering
  • Data acquisition (web scrapping, crowdsourcing etc.), data modeling and inference using semantic annotations and metadata to support intelligent system behaviour, explanations and human-friendly intelligent interfaces to information systems

Selected projects

  • “ARISTOTELES: Methodology and algorithms for automatic revision of ontologies in task based scenarios” (2015-2018) funded by the Polish National Science Center under the SONATA program
  • Project LeoLOD “Learning and Evolving Ontologies from Linked Open Data” funded by the Foundation for Polish Science (FNP) under the POMOST program (2013-2015)
  • EU FP7 project e-LICO (2009-2012)
  • Polish Ministry of Science and Higher Education grant N N516 186437 on “Inductive reasoning on ontological knowledge bases” (2009-2012)
  • R&D project Diagraphe on Spoken Language Understanding for Orange Labs (Polish Telecommunication & France Telecom), 2011
  • SEMINTEC – mining frequent patterns in description logics with rules (2005-2010)
  • EU Marie Curie fellowship within PERSONET project on Web mining – University of Ulster (2004)
  • industry funded projects

Selected publications

Dawid Wisniewski, Jedrzej Potoniec, Agnieszka Lawrynowicz, C. Maria Keet. Analysis of Ontology Competency Questions and their formalizations in SPARQL-OWL, Journal of Web Semantics, DOI:10.1016/j.websem.2019.100534

Jedrzej Potoniec: Learning SPARQL Queries from Expected Results. Computing and Informatics 38(3): 679-700 (2019)

Dawid Wisniewski, Agnieszka Lawrynowicz: A Tagger for Glossary of Terms Extraction from Ontology Competency Questions. ESWC (Satellite Events) 2019: 181-185

Agnieszka Ławrynowicz, Jedrzej Potoniec, Michał Robaczyk, Tania Tudorache, 2018, Discovery of emerging design patterns in ontologies using tree mining. Semantic Web, 9(4):517–544, DOI:10.3233/SW-170280.

Dawid Wisniewski: Automatic Translation of Competency Questions into SPARQL-OWL Queries. WWW (Companion Volume) 2018: 855-859

Pawel Garbacz, Agnieszka Lawrynowicz, Bogumil Szady: Identity Criteria for Localities. FOIS 2018: 47-54

Jedrzej Potoniec: Finding Unexplainable Triples in an RDF Graph. ESWC (Satellite Events) 2018: 3-7

Agnieszka Ławrynowicz, 2017, Semantic Data Mining: An Ontology-based Approach, volume 29 of Studies on the Semantic Web. IOS Press/AKA Verlag, 2017.

Jedrzej Potoniec, Piotr Jakubowski, Agnieszka Ławrynowicz, 2017, Swift Linked Data Miner: Mining OWL 2 EL class expressions directly from online RDF datasets. J. Web Sem. 46-47:31–50, DOI:10.1016/j.websem.2017.08.001, http://www.sciencedirect.com/science/article/pii/S157082681730032X.

Tomasz Sosnowski, Jedrzej Potoniec: Towards Mining Patterns for Exploratory Search with Keval Algorithm. EKAW (Satellite Events) 2016: 180-183

Agnieszka Lawrynowicz, Diego Esteves, Pance Panov, Tommaso Soru, Saso Dzeroski, Joaquin Vanschoren: An Algorithm, Implementation and Execution Ontology Design Pattern. WOP@ISWC 2016: 55-68

Ewa Kowalczuk, Agnieszka Lawrynowicz: The Reporting Event Ontology Design Pattern and Its Extension to Report News Events. WOP@ISWC 2016: 105-117

Keet C.M., Ławrynowicz A., Test-Driven Development of ontologies. In The Semantic Web. Latest Advances and New Domains – 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29-June 2, 2016, Proceedings, Springer LNCS 9678, 642–657 (2016).

C. Maria Keet, Agnieszka Ławrynowicz, Claudia d’Amato, Alexandros Kalousis, Phong Nguyen, Raul Palma, Robert Stevens, Melanie Hilario, 2015, The Data Mining OPtimization Ontology. J. Web Sem. 32: 43-53.

Jedrzej Potoniec, Agnieszka Lawrynowicz: Combining Ontology Class Expression Generation with Mathematical Modeling for Ontology Learning. AAAI 2015: 4198-4199

Agnieszka Lawrynowicz, Ilona Lawniczak: Towards a Core Ontology of Occupational Safety and Health. OWLED 2015: 134-142

Mikolaj Morzy, Agnieszka Lawrynowicz, Mateusz Zozulinski: Using Substitutive Itemset Mining Framework for Finding Synonymous Properties in Linked Data. RuleML 2015: 422-430

Agnieszka Lawrynowicz, Ilona Lawniczak: The Hazardous Situation Ontology Design Pattern. WOP 2015

Agnieszka Ławrynowicz, Jedrzej Potoniec, 2014, Pattern Based Feature Construction in Semantic Data Mining. Int. J. Semantic Web Inf. Syst. 10(1): 27-65.

Claudia d’Amato, Nicola Fanizzi, Marko Grobelnik, Agnieszka Lawrynowicz, Vojtech Svatek, 2014, Inductive Reasoning and Machine Learning for the Semantic Web. Semantic Web 5(1):3-4.

Ławrynowicz A., Tresp V., Introducing machine learning. In J. Voelker and J. Lehmann,editors, Perspectives of Ontology Learning, Studies on the Semantic Web. AKA Heidelberg / IOS Press, 35–50 (2014).

Agnieszka Lawrynowicz, Jedrzej Potoniec: Fr-ONT: An Algorithm for Frequent Concept Mining with Formal Ontologies. ISMIS 2011: 428-437

Joanna Józefowska, Agnieszka Lawrynowicz, Tomasz Lukaszewski, 2010, The role of semantics in mining frequent patterns from knowledge bases in description logics with rules. Theory and Practice of Logic Programming 10(3): 251-289.

Claudia d’Amato, Nicola Fanizzi, Agnieszka Lawrynowicz: Categorize by: Deductive Aggregation of Semantic Web Query Results. ESWC (1) 2010: 91-105

Selected current and previous teaching

  • Artificial intelligence
  • Natural language processing
  • Semantic technologies / Semantic Web
  • Computational logics
  • Analysis of massive datasets
  • Probability theory and statistics