Publications

2023

1. Is the Proof Length a Good Indicator of Hardness for Reason-able Embeddings?

   Potoniec, Jędrzej

   In NeSy 2023 Neural-Symbolic Learning and Reasoning 2023 : Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning, La Certosa di Pontignano, Siena, Italy, July 3-5, 2023, 2023

   Abs Paper

   Reason-able embeddings are recently proposed embeddings for knowledge bases (KBs) in the description logic 𝒜ℒ𝒞 capable of casting multiple KBs into a single latent space using a transferable neural reasoner. While they exhibit remarkable performance on real-world KBs, it is so far unknown what are their exact limits. In this paper, we systematically investigate their performance using a set of synthetic KBs in the description logic ℰℒ, a subset of 𝒜ℒ𝒞. We use the proof length as a measure of reasoning complexity and present a random KB generator taking the proof length into account. We train the reason-able embeddings with and without transfer learning and investigate whether the complexity of the training set and the test set is related to the reasoning performance of the embeddings and their neural reasoner.

2. Reason-able Embeddings: Learning Concept Embeddings with a Transferable Neural Reasoner

   Adamski, Dariusz Max and Potoniec, Jędrzej

   Semantic Web, 2023

   Abs Paper

   We present a novel approach for learning embeddings of ALC knowledge base concepts. The embeddings reflect the semantics of the concepts in such a way that it is possible to compute an embedding of a complex concept from the embeddings of its parts by using appropriate neural constructors. Embeddings for different knowledge bases are vectors in a shared vector space, shaped in such a way that approximate subsumption checking for arbitrarily complex concepts can be done by the same neural network, called a reasoner head, for all the knowledge bases. To underline this unique property of enabling reasoning directly on embeddings, we call them reason-able embeddings. We report the results of experimental evaluation showing that the difference in reasoning performance between training a separate reasoner head for each ontology and using a shared reasoner head, is negligible.

2022

1. Continuous discovery of Causal nets for non-stationary business processes using the Online Miner

   Potoniec, Jędrzej and Sroka, Daniel and Pawlak, Tomasz P.

   European Journal of Operational Research, 2022

   Abs Paper

   Capturing business process specifics using a model is essential to effectively manage, control, and instruct the process participants with their roles and tasks. A normative process model is an invaluable source of information, not only for human inspection but also for software supporting and controlling the process. The actual process execution likely deviates from the normative model and the magnitude of deviation usually increases over time due to process evolution. A descriptive process model is far more informative when comes to the analysis of the actual execution of the process and spotting the deviations from the norm. However, handcrafting the descriptive model using process-related documents is prohibitively laborious. To automate the discovery of the descriptive process models, we propose Online Miner (OM), an algorithm that continuously builds process models using a stream of events raised by the process. OM first builds a sound process model using a historical event log and then incrementally adapts this model to new data chunks. OM represents the model using a Causal-net – a dependency graph of activities in which arcs are bound with each other to reflect parallel dependencies and exclusive alternatives. OM finds the maximal bindings being consistent with the event log. OM produces sound and perfect fit and generalizing well models. OM quickly reacts to the concept drift spot in the event log by seamlessly adapting the model. OM helps business analytics experts with their everyday tasks of monitoring, auditing, and enhancing business processes.

2. BigCQ: Generating a Synthetic Set of Competency Questions Formalized into SPARQL-OWL

   Wiśniewski, Dawid and Potoniec, Jędrzej and Ławrynowicz, Agnieszka

   In Proceedings of the 36th AAAI Conference on Artificial Intelligence, 2022

   Paper
3. Inductive learning of OWL 2 property chains

   Potoniec, Jędrzej

   IEEE Access, 2022

   Abs Paper

   We present an algorithm to inductively learn Web Ontology Language (OWL) 2 property chains to be used in object subproperty axioms. For efficiency, it uses specialized encodings and data structures based on hash-maps and sparse matrices. The algorithm is based on the frequent pattern search principles and uses a novel measure called s-support. We prove soundness and termination of the algorithm, and report on evaluation where we mine axioms from DBpedia 2016-10. We extensively discuss the 36 mined axioms and conclude that 30 (83%) of them are correct and could be added to the ontology.

4. Quality Versus Speed in Energy Demand Prediction

   Andrzejewski, Witold and Potoniec, Jędrzej and Drozdowski, Maciej and Stefanowski, Jerzy and Wrembel, Robert and Stapf, Paweł

   In Database and Expert Systems Applications : 33rd International Conference, DEXA 2022, Vienna, Austria, August 22–24, 2022, Proceedings, Part I, 2022

   Abs Paper

   Effective heat energy demand prediction is essential in combined heat power systems. The algorithms considered so far do not sufficiently take into account the computational costs and ease of implementation in industrial systems. However, computational cost is of key importance in edge and IoT systems, where prediction algorithms are constantly updated with new arriving data. In this paper, we propose two types of algorithms for heat demands prediction: (1) novel extensions to the algorithm originally proposed by E. Dotzauer and (2) based on a kind of autoregressive predictor. They were developed within an R &D project for a company operating a cogeneration system and for their real dataset. We evaluate the algorithms experimentally focusing on prediction quality and computational cost. The algorithms are compared against two state-of-the art artificial neural networks.

5. ReqTagger: A rule-based tagger for automatic Glossary of Terms extraction from ontology requirements

   Wiśniewski, Dawid and Potoniec, Jędrzej and Ławrynowicz, Agnieszka

   Foundations of Computing and Decision Sciences, 2022

   Abs Paper

   Glossary of Terms extraction from textual requirements is an impor- tant step in ontology engineering methodologies. Although initially it was intended to be performed manually, last years have shown that some degree of automatization is possible. Based on these promising approaches, we introduce a novel, human inter- pretable, rule-based method named ReqTagger, which can extract candidates for ontology entities (classes or instances) and relations (data or object properties) from textual requirements automatically. We compare ReqTagger to existing automatic methods on an evaluation benchmark consisting of over 550 requirements and tagged with over 1700 entities and relations expected to be extracted. We discuss the quality of ReqTagger and provide details showing why it outperforms other methods. We also publish both the evaluation dataset and the implementation of ReqTagger.

6. Sarcastic RoBERTa: A RoBERTa-Based Deep Neural Network Detecting Sarcasm on Twitter

   Hercog, Maciej and Jaroński, Piotr and Kolanowski, Jan and Mieczyński, Paweł and Wiśniewski, Dawid and Potoniec, Jędrzej

   In Big Data Analytics and Knowledge Discovery : 24th International Conference, DaWaK 2022, Vienna, Austria, August 22–24, 2022, Proceedings, 2022

   Abs Paper

   Sarcastic RoBERTa is an approach to recognizing sarcastic tweets written in English. It is based on a pre-trained RoBERTa model supported by a 3-layer feed-forward fully-connected neural network. It establishes a new state-of-the-art result on the iSarcasm dataset, attaining the F1 score of 0.526, and being not far from the human performance of 0.616.

2021

1. Incorporating Presuppositions of Competency Questions into Test-Driven Development of Ontologies

   Potoniec, Jędrzej and Wiśniewski, Dawid and Ławrynowicz, Agnieszka

   In SEKE 2021 : Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering, 2021

   Paper
2. SeeQuery: An Automatic Method for Recommending Translations of Ontology Competency Questions into SPARQL-OWL

   Wiśniewski, Dawid and Potoniec, Jędrzej and Ławrynowicz, Agnieszka

   In CIKM ’21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021

   Paper

2020

1. Dataset of Ontology Competency Questions to SPARQL-OWL Queries Translations

   Potoniec, Jędrzej and Wiśniewski, Dawid and Ławrynowicz, Agnieszka and Keet, Maria C.

   Data in Brief, 2020

   Paper
2. Learning OWL 2 Property Characteristics as an Explanation for a Recurrent Neural Network

   Potoniec, Jędrzej

   Bulletin of the Polish Academy of Sciences. Technical Sciences, 2020

   Paper
3. Mining Cardinality Restrictions in OWL

   Potoniec, Jędrzej

   Foundations of Computing and Decision Sciences, 2020

   Abs Paper

   We present an approach to mine cardinality restriction axioms from an existing knowledge graph, in order to extend an ontology describing the graph. We compare frequency estimation with kernel density estimation as approaches to obtain the cardinalities in restrictions. We also propose numerous strategies for filtering obtained axioms in order to make them more available for the ontology engineer. We report the results of experimental evaluation on DBpedia 2016-10 and show that using kernel density estimation to compute the cardinalities in cardinality restrictions yields more robust results that using frequency estimation. We also show that while filtering is of limited usability for minimum cardinality restrictions, it is much more important for maximum cardinality restrictions. The presented findings can be used to extend existing ontology engineering tools in order to support ontology construction and enable more efficient creation of knowledge-intensive artificial intelligence systems.

4. Urszula Walińska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis

   Walińska, Urszula and Potoniec, Jędrzej

   In COLING 2020 : The International Workshop on Semantic Evaluation : Proceedings of the Fourteenth Workshop, December 12-13, 2020, Barcelona, Spain (online), 2020

   Paper

2019

1. Analysis of Ontology Competency Questions and their formalizations in SPARQL-OWL

   Wiśniewski, Dawid and Potoniec, Jędrzej and Ławrynowicz, Agnieszka and Keet, Maria C.

   Journal of Web Semantics, 2019

   Paper
2. Learning SPARQL queries from expected results

   Potoniec, Jędrzej

   Computing and Informatics, 2019

   Paper
3. Mining Cardinality Restrictions in OWL

   Potoniec, Jędrzej

   In PP-RAI’2019 Polskie Porozumienie na Rzecz Rozwoju Sztucznej Inteligencji, 16-18.10.2019, Wrocław, Poland : Conference Proceedings, 2019

   Paper

2018

1. Discovery of emerging design patterns in ontologies using tree mining

   Ławrynowicz, Agnieszka and Potoniec, Jędrzej and Robaczyk, Michał and Tudorache, Tania

   Semantic Web, 2018

   Paper
2. Finding Unexplainable Triples in an RDF Graph

   Potoniec, Jędrzej

   In The Semantic Web: ESWC 2018 Satellite Events : ESWC 2018 Satellite Events Heraklion, Crete, Greece, June 3–7, 2018 : Revised Selected Papers, 2018

   Paper

2017

1. Swift Linked Data Miner Extension for WebProtégé

   Sosnowski, Tomasz and Potoniec, Jędrzej and Ławrynowicz, Agnieszka

   In Knowledge Engineering and Knowledge Management : EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Bologna, Italy, November 19–23, 201 : Revised Selected Papers, 2017

   Paper
2. Swift Linked Data Miner: Mining OWL 2 EL class expressions directly from online RDF datasets

   Potoniec, Jędrzej and Jakubowski, Piotr and Ławrynowicz, Agnieszka

   Journal of Web Semantics, 2017

   Paper
3. Towards Mining Patterns for Exploratory Search with Keval Algorithm

   Sosnowski, Tomasz and Potoniec, Jędrzej

   In Knowledge Engineering and Knowledge Management : EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Bologna, Italy, November 19–23, 201 : Revised Selected Papers, 2017

   Paper
4. Translating Competency Questions to simple SPARQL-OWL

   Wiśniewski, Dawid and Potoniec, Jędrzej and Ławrynowicz, Agnieszka

   In Human Language Technologies as a Challenge for Computer Science and Linguistics : Conference Proceedings, 2017

2016

1. An On-Line Learning to Query System

   Potoniec, Jędrzej

   In Proceedings of the ISWC 2016 Posters & Demonstrations Track co-located with 15th International Semantic Web Conference (ISWC 2016), 2016

   Paper
2. A Protégé Plugin with Swift Linked Data Miner

   Potoniec, Jędrzej and Ławrynowicz, Agnieszka

   In Proceedings of the ISWC 2016 Posters & Demonstrations Track co-located with 15th International Semantic Web Conference (ISWC 2016), 2016

   Paper
3. Not-So-Linked Solution to the Linked Data Mining Challenge 2016

   Potoniec, Jędrzej

   In Proceedings of the 5th Workshop on Data Mining and Knowledge Discovery meets Linked Open Data and the 1st International Workshop on Completing and Debugging the Semantic Web (Know@LOD&CoDeS-2016), 2016

2015

1. Combining ontology class expression generation with mathematical modeling for ontology learning

   Potoniec, Jędrzej and Ławrynowicz, Agnieszka

   In AAAI’15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
2. Towards Memory-Effcient Answering of Tree-Shaped SPARQL Queries Using GPUs

   Potoniec, Jędrzej and Madziar, Michał

   In Proceedings of the ISWC 2015 Posters & Demonstrations Track, 2015
3. Towards Ontology Refinement by Combination of Machine Learning and Attribute Exploration

   Potoniec, Jędrzej

   In Knowledge Engineering and Knowledge Management : EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers, 2015

   Paper

2014

1. Extracting Usage Patterns of Ontologies on the Web: a Case Study on GoodRelations Vocabulary in RDFa

   Kowalczuk, Ewa and Potoniec, Jędrzej and Ławrynowicz, Agnieszka

   In Proceedings of the 11th International Workshop on OWL : Experiences and Directions (OWLED 2014), Riva del Garda, Italy, October 17-18, 2014, 2014
2. Hypothesis-Driven Interactive Classification Based on AVO

   Łukaszewski, Tomasz and Potoniec, Jędrzej and Wilk, Szymon

   In Man-Machine Interactions 3, 2014

   Paper
3. Pattern Based Feature Construction in Semantic Data Mining

   Ławrynowicz, Agnieszka and Potoniec, Jędrzej

   International Journal on Semantic Web and Information Systems, 2014

   Paper
4. Towards Combining Machine Learning with Attribute Exploration for Ontology Refinement

   Potoniec, Jędrzej and Rudolph, Sebastian and Ławrynowicz, Agnieszka

   In Proceedings of the ISWC 2014 Posters & Demonstrations Track a track within the 13th International Semantic Web Conference (ISWC 2014), 2014