@conference {996, title = {Hidden Neuron Activation Analysis on Labeled Text Data}, booktitle = {K-CAP {\textquoteright}25: Knowledge Capture Conference 2025}, year = {2025}, month = {12/2025}, pages = {206 - 210}, publisher = {ACM}, organization = {ACM}, address = {USA}, abstract = {

Understanding the internal mechanisms of deep neural networks remains a central challenge in the field of Explainable Artificial Intelligence (XAI). With the rapid advancement of neural architectures in natural language processing (NLP), analyzing the role of hidden neurons in capturing and processing linguistic features has become increasingly important. This study investigates Hidden Neuron Activation Analysis on labeled text data to reveal how individual neurons contribute to a model{\textquoteright}s decision-making process. We propose a model-agnostic explainability framework for text classifiers that identifies concepts activating specific neurons involved in classification. An LSTM-based network is trained on the AG News topic classification dataset, comprising four distinct classes, and the final Dense layer with 64 neurons was analyzed. In addition, statistical analyses like the Mann-Whitney U Test is conducted to assess the robustness and reliability of the system. The statistical analysis shows that, concepts plays important role in the decision making process of neural network. Our findings enhance interpretability in NLP models and offer a foundation for optimizing neural architectures in text classification tasks.

}, keywords = {Concept-based Explanation, Dense Layer Analysis, Explainable AI, Hidden Neuron Analysis}, author = {Avishek Das and Abhilekha Dalal and Pascal Hitzler} } @conference {979, title = {Automating CNN Neuron Interpretation using Concept Induction}, booktitle = {THE 23RD INTERNATIONAL SEMANTIC WEB CONFERENCE, ISWC 2024}, year = {2024}, abstract = {

This paper presents an automation pipeline for interpreting hidden neuron activations in Convolutional Neural Networks (CNNs), a crucial objective of Explainable AI (XAI). Previously, our research group addressed this objective by employing concept induction and semantic reasoning using a concept hierarchy derived from the Wikipedia knowledge graph. However, the process was executed manually, taking several days to complete. In this study, we have fully automated the workflow, achieving consistent results while significantly reducing the execution time. The automation pipeline streamlines model training, data preparation, concept induction, image retrieval, classification, and statistical validation, thereby completely eliminating the manual intervention. This automation enables us to efficiently interpret and validate CNN neuron activations by modifying parameters, such as incorporating a broader range of training images and classes and examining additional concept induction results across various neuron layers using different analytical tools.

}, keywords = {Automation in AI, Deep Learning, Explainable Artificial Intelligence, Knowledge Graph, Semantic Web}, author = {Samatha Ereshi Akkamahadevi and Abhilekha Dalal and Pascal Hitzler} } @conference {982, title = {BMGC: A Deep Learning Approach to Classify Bengali Music Genres}, booktitle = {Proceedings of the 4th International Conference on Networking, Information Systems \& Security}, year = {2021}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {

Music genre classification (MGC) is the process of tagging music with their appropriate genres by analyzing music signals or the lyrics. With the accelerated surge in music data repositories, MGC can be extensively used in music recommendation systems, advertisement, and streaming services for systematic and efficient management. However, there have been many works on English music classification using different statistical and machine learning approaches, but there is no notable progress found in the arena of Bengali music. Besides, a few significant works have been found in utilizing Deep Learning (DL) methods to classify different music genres. Bengali music is significantly enriched with its contents and uniqueness. Moreover, the extent and scope of exploring the DL approach in Bengali music ground are still latent. Therefore, Bengali music genre classification is quite a new research area in the Deep learning field. In this work, we have constructed a Bengali Music Genre Classifier (BMGC) to categorize 6 Bengali music genres: {\textquoteleft}Adhunik{\textquoteright}, {\textquoteleft}Band{\textquoteright}, {\textquoteleft}Hiphop{\textquoteright}, {\textquoteleft}Nazrulgeeti{\textquoteright}, {\textquoteleft}Lalon{\textquoteright}, and {\textquoteleft}Rabindra Sangeet{\textquoteright}. We have created a Bengali music genre classification dataset (hereafter named BMGCD) containing 2944 Bengali music clips, and a Gated Recurrent Unit based deep learning model has been developed to predict the music genre from audio signals. Our developed model achieved an accuracy of 80.4\% and 80.6\% F1-score which surpasses the related existing works.

}, keywords = {Bengali music classification, Bengali Music Dataset., Deep Learning, Gated Recurrent Unit (GRU), Mel Frequency Cepstral Coefficient (MFCC), Recurrent Neural Network}, isbn = {9781450388719}, doi = {10.1145/3454127.3456593}, url = {https://doi.org/10.1145/3454127.3456593}, author = {Sen Sarma, Moumita and Das, Avishek} } @conference {981, title = {Traditional Bengali Food Classification Using Convolutional Neural Network}, booktitle = {2021 IEEE Region 10 Symposium (TENSYMP)}, year = {2021}, pages = {1-8}, keywords = {Computational modeling, Convolutional Neural Network, Convolutional neural networks, Data models, fine tuning, image classification, Image recognition, Neural networks, traditional Bengali foods, Transfer learning, Turning, VGG16}, doi = {10.1109/TENSYMP52854.2021.9550874}, author = {Uddin, Asif Mahbub and Al Miraj, Abdullah and Sen Sarma, Moumita and Das, Avishek and Gani, Md. Manjurul} } @proceedings {770, title = {Completion Reasoning Emulation for the Description Logic EL+}, journal = {Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice}, volume = {2600}, year = {2020}, month = {03/2020}, publisher = {CEUR-WS.org}, address = {Stanford University, Palo Alto, California, USA}, abstract = {

We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be inspected part-way through, rather than simply learning reasoner answers, which is typical in many of the black-box systems currently in use. We demonstrate that this idea is feasible by training a long short-term memory (LSTM) artificial neural network to learn EL+ reasoning patterns with two different data sets. We also show that this trained system is resistant to noise by corrupting a percentage of the test data and comparing the reasoner{\textquoteright}s and LSTM{\textquoteright}s predictions on corrupt data with correct answers.

}, keywords = {Deep Learning, Description Logic, EL+, LSTM, NeSy, Reasoning}, url = {http://ceur-ws.org/Vol-2600/paper5.pdf}, author = {Aaron Eberhart and Monireh Ebrahimi and Lu Zhou and Cogan Shimizu and Pascal Hitzler} } @article {784, title = {The Enslaved Ontology: Peoples of the Historic Slave Trade}, journal = {Journal of Web Semantics}, volume = {63}, year = {2020}, month = {08/2020}, abstract = {

We present the Enslaved Ontology (V1.0) which was developed for integrating data about the historic slave trade from diverse sources in a use case driven by historians. Ontology development followed modular ontology design principles as derived from ontology design pattern application best practices and the eXtreme Design Methodology. Ontology content focuses on data about historic persons and the event records from which this data can be taken. It also incorporates provenance modeling and some temporal and spatial aspects. The ontology is available as serialized in the Web Ontology Language OWL, and carries modularization annotations using the Ontology Pattern Language (OPLa). It is available under the Creative Commons CC BY 4.0 license.

}, keywords = {data integration, digital humanities, history of the slave trade, modular ontology, Ontology Design Patterns}, doi = {https://doi.org/10.1016/j.websem.2020.100567}, author = {Cogan Shimizu and Pascal Hitzler and Quinn Hirt and Dean Rehberger and Seila Gonzalez Estrecha and Catherine Foley and Alicia M. Sheill and Walter Hawthorne and Jeff Mixter and Ethan Watrall and Ryan Carty and Duncan Tarr} } @conference {747, title = {A Method for Automatically Generating Schema Diagrams for OWL Ontologies}, booktitle = {1st Iberoamerican Knowledge Graph and Semantic Web Conference (KGSWC)}, year = {2019}, month = {06/2019}, publisher = {Springer}, organization = {Springer}, chapter = {149-161}, address = {Villa Clara, Cuba}, abstract = {

Interest in Semantic Web technologies, including knowledge graphs and ontologies, is increasing rapidly in industry and academics. In order to support ontology engineers and domain experts, it is necessary to provide them with robust tools that facilitate the ontology engineering process. Often, the schema diagram of an ontology is the most important tool for quickly conveying the overall purpose of an ontology. In this paper, we present a method for programmatically generating a schema diagram from an OWL file. We evaluate its ability to generate schema diagrams similar to manually drawn schema diagrams and show that it outperforms VOWL and OWLGrEd. In addition, we provide a prototype implementation of this tool.

}, keywords = {design patterns, evaluation, implementation, ontology, schema diagrams, visualization}, author = {Cogan Shimizu and Aaron Eberhart and Nazifa Karima and Quinn Hirt and Adila Krisnadhi and Pascal Hitzler} } @conference {748, title = {Pseudo-Random ALC Syntax Generation}, booktitle = {The Semantic Web: ESWC 2018 Satellite Events - ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers}, volume = {11155}, year = {2018}, pages = {19{\textendash}22}, publisher = {Springer}, organization = {Springer}, address = {Heraklion, Crete, Greece}, abstract = {We discuss a tool capable of rapidly generating pseudo-random syntactically valid ALC expression trees. The program is meant to allow a researcher to create large sets of independently valid expressions with a minimum of personal bias for experimentation.}, keywords = {ALC, Description Logic, DL, random generation, synthetic data}, isbn = {978-3-319-98191-8}, doi = {10.1007/978-3-319-98192-5\_4}, url = {https://doi.org/10.1007/978-3-319-98192-5\_4}, author = {Aaron Eberhart and Michelle Cheatham and Pascal Hitzler} } @mastersthesis {633, title = {Efficient Reasoning Algorithms for Fragments of Horn Description Logics}, volume = {Doctor of Philosophy (PhD)}, year = {2017}, pages = {70}, school = {Wright State University}, address = {Dayton}, abstract = {We characterize two fragments of Horn Description Logics and we define two specialized reasoning algorithms that effectively solve the standard reasoning tasks over each of such fragments. We believe our work to be of general interest since (1) a rather large proportion of real-world Horn ontologies belong to some of these two fragments and (2) the implementations based on our reasoning approach significantly outperform state-of-the-art reasoners. Claims (1) and (2) are extensively proven via empirically evaluation. }, keywords = {Description Logic, Knowledge representation, Reasoning}, url = {http://rave.ohiolink.edu/etdc/view?acc_num=wright1491317096530938}, author = {David Carral} } @conference {156, title = {Distributed and Scalable OWL EL Reasoning}, booktitle = {Proceedings of the 12th Extended Semantic Web Conference (ESWC 2015) }, year = {2015}, publisher = {Springer}, organization = {Springer}, address = {Portoroz, Slovenia}, abstract = {

OWL 2 EL is one of the tractable proles of the Web Ontology\ Language (OWL) which is a W3C-recommended standard. OWL 2\ EL provides sucient expressivity to model large biomedical ontologies\ as well as streaming data such as trac, while at the same time allows\ for ecient reasoning services. Existing reasoners for OWL 2 EL, however,\ use only a single machine and are thus constrained by memory and\ computational power. At the same time, the automated generation of\ ontological information from streaming data and text can lead to very\ large ontologies which can exceed the capacities of these reasoners. We\ thus describe a distributed reasoning system that scales well using a cluster\ of commodity machines. We also apply our system to a use case on\ city trac data and show that it can handle volumes which cannot be\ handled by current single machine reasoners.

}, keywords = {DistEL, Distributed Reasoning, Ontology Classification, OWL EL}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti and Freddy L{\'e}cu{\'e}} } @conference {133, title = {All But Not Nothing: Left-Hand Side Universals for Tractable {OWL} Profiles}, booktitle = {Proceedings of the 11th International Workshop on OWL: Experiences and Directions (OWLED 2014) co-located with 13th International Semantic Web Conference on (ISWC 2014), Riva del Garda, Italy, October 17-18, 2014.}, volume = {1265}, year = {2014}, month = {10/2014}, pages = {97-108}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {We show that occurrences of the universal quantifier in the left-hand side of general concept inclusions can be rewritten into EL++ axioms under certain circumstances. I.e., this intuitive modeling feature is available for OWL EL while retaining tractability. Furthermore, this rewriting makes it possible to reason over corresponding extensions of EL++ and Horn-SROIQ using standard reasoners.}, keywords = {description logics, Horn Logics, OWL}, url = {http://ceur-ws.org/Vol-1265/owled2014_submission_13.pdf}, author = {David Carral and Adila Krisnadhi and Sebastian Rudolph and Pascal Hitzler}, editor = {C. Maria Keet and Valentina A. M. Tamma} } @conference {11, title = {Distributed OWL EL Reasoning: The Story So Far}, booktitle = {Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy}, volume = {1261}, year = {2014}, month = {10/2014}, pages = {61-76}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Riva del Garda, Italy}, abstract = {

Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires distributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now exist. For OWL 2 EL, several distributed reasoning approaches have been tried, but are all perceived to be inefficient. We analyze this perception. We analyze completion rule based distributed approaches, using different characteristics, such as dependency among the rules, implementation optimizations, how axioms and rules are distributed. We also present a distributed queue approach for the classification of ontologies in description logic EL+ (fragment of OWL 2 EL).

}, keywords = {Distributed Reasoning, OWL EL, Scalability}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti}, editor = {Thorsten Liebig and Achille Fokoue} } @conference {135, title = {EL-ifying Ontologies}, booktitle = {Automated Reasoning - 7th International Joint Conference, IJCAR 2014, Held as Part of the Vienna Summer of Logic, {VSL} 2014, Vienna, Austria, July 19-22, 2014. Proceedings}, year = {2014}, pages = {464{\textendash}479}, abstract = {

The OWL 2 profiles are fragments of the ontology language OWL 2 for which standard reasoning tasks are feasible in polynomial time. Many OWL ontologies, however, contain a typically small number of out-of-profile axioms, which may have little or no influence on reasoning outcomes. We investigate techniques for rewriting axioms into the EL and RL profiles of OWL 2. We have tested our techniques on both classification and data reasoning tasks with encouraging results.

}, keywords = {description logics, OWL, Rewriting, Tractable Reasoning}, doi = {10.1007/978-3-319-08587-6_36}, url = {http://dx.doi.org/10.1007/978-3-319-08587-6_36}, author = {David Carral and Cristina Feier and Cuenca Grau, Bernardo and Pascal Hitzler and Ian Horrocks} } @conference {131, title = {Pushing the Boundaries of Tractable Ontology Reasoning}, booktitle = {The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II}, year = {2014}, pages = {148{\textendash}163}, abstract = {

We identify a class of Horn ontologies for which standard reasoning tasks such as instance checking and classification are tractable. The class is general enough to include the OWL 2 EL, QL, and RL profiles. Verifying whether a Horn ontology belongs to the class can be done in polynomial time. We show empirically that the class includes many real-world ontologies that are not included in any OWL 2 profile, and thus that polynomial time reasoning is possible for these ontologies.

}, keywords = {description logics, OWL, Tractable Reasoning}, doi = {10.1007/978-3-319-11915-1_10}, url = {http://dx.doi.org/10.1007/978-3-319-11915-1_10}, author = {David Carral and Cristina Feier and Cuenca Grau, Bernardo and Pascal Hitzler and Ian Horrocks} } @conference {7, title = {Revisiting default description logics {\textendash} and their role in aligning ontologies}, booktitle = {Semantic Technology, 4th Joint International Conference, JIST 2014}, volume = {8943}, year = {2014}, month = {11/2014}, pages = {3-18}, publisher = {Lecture Notes in Computer Science, Springer}, organization = {Lecture Notes in Computer Science, Springer}, address = {Chiang Mai, Thailand}, abstract = {We present a new approach to extend the Web Ontology Language (OWL) with the capabilities to reason with defaults. This work improves upon the previously established results on integrating defaults with description logics (DLs), which were shown to be decidable only when the application of defaults is restricted to named individuals in the knowledge base. We demonstrate that the application of defaults (integrated with DLs) does not have to be restricted to named individuals to retain decidability and elaborate on the application of defaults in the context of ontology alignment and ontology-based systems.}, keywords = {default logic, defaults, description logics, Ontology Alignment}, doi = {10.1007/978-3-319-15615-6_1}, author = {Kunal Sengupta and Pascal Hitzler and Krzysztof Janowicz}, editor = {T. Supnithi and T. Yamaguchi and Jeff Z. Pan and V. Wuwongse and M. Buranarach} } @conference {134, title = {Is Your Ontology as Hard as You Think? Rewriting Ontologies into Simpler DLs}, booktitle = {Informal Proceedings of the 27th International Workshop on Description Logics, Vienna, Austria, July 17-20, 2014.}, year = {2014}, pages = {128{\textendash}140}, abstract = {

We investigate cases where an ontology expressed in a seemingly hard DL can be polynomially reduced to one in a simpler logic, while preserving reasoning outcomes for classification and fact entailment. Our transformations target the elimination of inverse roles, universal and existential restrictions, and in the best case allow us to rewrite the given ontology into one of the OWL 2 profiles. Even if an ontology cannot be fully rewritten into a profile, in many cases our transformations allow us to exploit further optimisation techniques. Moreover, the elimination of some out-of-profile axioms can improve the performance of modular reasoners, such as MORe. We have tested our techniques on both classification and data reasoning tasks with encouraging results.

}, keywords = {description logics, OWL, Tractable Reasoning}, url = {http://ceur-ws.org/Vol-1193/paper_75.pdf}, author = {David Carral and Cristina Feier and Ana Armas Romero and Cuenca Grau, Bernardo and Pascal Hitzler and Ian Horrocks} } @conference {94, title = {Automatic Domain Identification for Linked Open Data}, booktitle = {2013 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2013}, year = {2013}, pages = {205{\textendash}212}, address = {Atlanta, GA, USA}, abstract = {

Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.

}, keywords = {dataset search, Domain Identification, Linked Open Data Cloud}, doi = {10.1109/WI-IAT.2013.206}, url = {http://dx.doi.org/10.1109/WI-IAT.2013.206}, author = {Sarasi Lalithsena and Pascal Hitzler and Amit Sheth and Prateek Jain} } @article {60, title = {Complexities of Horn Description Logics}, journal = {ACM Trans. Comput. Log.}, volume = {14}, year = {2013}, pages = {2}, abstract = {Description Logics (DLs) have become a prominent paradigm for representing knowledge bases in a variety of application areas. Central to leveraging them for corresponding systems is the provision of a favourable balance between expressivity of the knowledge representation formalism on the one hand, and runtime performance of reasoning algorithms on the other. Due to this, Horn description logics (Horn DLs) have attracted attention since their (worst-case) data complexities are in general lower than their overall (i.e. combined) complexities, which makes them attractive for reasoning with large sets of instance data (ABoxes). However, the natural question whether Horn DLs also provide advantages for schema (TBox) reasoning has hardly been addressed so far. In this paper, we therefore provide a thorough and comprehensive analysis of the combined complexities of Horn DLs. While the combined complexity for many Horn DLs studied herein turns out to be the same as for their non-Horn counterparts, we identify subboolean DLs where Hornness simplifies reasoning. We also provide convenient normal forms for Horn DLs.}, keywords = {computational complexity, description logics, Horn logic}, doi = {10.1145/2422085.2422087}, url = {http://doi.acm.org/10.1145/2422085.2422087}, author = {Markus Kr{\"o}tzsch and Sebastian Rudolph and Pascal Hitzler} } @conference {10, title = {DistEL: A Distributed EL+ Ontology Classifier}, booktitle = {Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, co-located with the International Semantic Web Conference (ISWC 2013)}, volume = {1046}, year = {2013}, month = {10/2013}, pages = {17-32}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Sydney, Australia}, abstract = {OWL 2 EL ontologies are used to model and reason over data from diverse domains such as biomedicine, geography and road traffic. Data in these domains is increasing at a rate quicker than the increase in main memory and computation power of a single machine. Recent efforts in OWL reasoning algorithms lead to the decrease in classification time from several hours to a few seconds even for large ontologies like SNOMED CT. This is especially true for ontologies in the description logic EL+ (a fragment of the OWL 2 EL profile). Reasoners such as Pellet, Hermit, ELK etc. make an assumption that the ontology would fit in the main memory, which is unreasonable given projected increase in data volumes. Increase in the data volume also necessitates an increase in the computation power. This lead us to the use of a distributed system, so that memory and computation requirements can be spread across machines. We present a distributed system for the classification of EL+ ontologies along with some results on its scalability and performance.}, keywords = {Classification, DistEL, Distributed Reasoning, EL+, OWL, Scalability}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti}, editor = {Thorsten Liebig and Achille Fokoue} } @conference {159, title = {D-SPARQ: Distributed, Scalable and Efficient RDF Query Engine}, booktitle = {Proceedings of the ISWC 2013 Posters \& Demonstrations Track}, volume = {1035}, year = {2013}, pages = {261{\textendash}264}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Sydney, Australia}, abstract = {

We present D-SPARQ, a distributed RDF query engine that combines the MapReduce processing framework with a NoSQL distributed data store, MongoDB. The performance of processing SPARQL queries mainly depends on the efficiency of handling the join operations between the RDF triple patterns. Our system features two unique characteristics that enable efficiently tackling this challenge: 1) Identifying specific patterns of the input queries that enable improving the performance by running different parts of the query in a parallel mode. 2) Using the triple selectivity information for reordering the individual triples of the input query within the identified query patterns. The preliminary results demonstrate the scalability and efficiency of our distributed RDF query engine.

}, keywords = {D-SPARQ, Distributed Querying, Scalable RDF querying, SPARQL}, url = {http://ceur-ws.org/Vol-1035/iswc2013_poster_21.pdf}, author = {Raghava Mutharaju and Sherif Sakr and Alessandra Sala and Pascal Hitzler}, editor = {Eva Blomqvist and Tudor Groza} } @article {61, title = {Paraconsistent OWL and Related Logics}, journal = {Semantic Web}, volume = {4}, year = {2013}, pages = {395{\textendash}427}, abstract = {The Web Ontology Language OWL is currently the most prominent formalism for representing ontologies in Semantic Web applications. OWL is based on description logics, and automated reasoners are used to infer knowledge implicitly present in OWL ontologies. However, because typical description logics obey the classical principle of explosion, reasoning over inconsistent ontologies is impossible in OWL. This is so despite the fact that inconsistencies are bound to occur in many realistic cases, e.g., when multiple ontologies are merged or when ontologies are created by machine learning or data mining tools. In this paper, we present four-valued paraconsistent description logics which can reason over inconsistencies. We focus on logics corresponding to OWL DL and its profiles. We present the logic SROIQ4, showing that it is both sound relative to classical SROIQ and that its embedding into SROIQ is consequence preserving. We also examine paraconsistent varieties of EL++, DL-Lite, and Horn-DLs. The general framework described here has the distinct advantage of allowing classical reasoners to draw sound but nontrivial conclusions from even inconsistent knowledge bases. Truth-value gaps and gluts can also be selectively eliminated from models (by inserting additional axioms into knowledge bases). If gaps but not gluts are eliminated, additional classical conclusions can be drawn without affecting paraconsistency.}, keywords = {Automated Deduction, Complexity, Description Logic, OWL, Paraconsistency, Semantic Web, Web Ontology Language}, doi = {10.3233/SW-2012-0066}, url = {http://dx.doi.org/10.3233/SW-2012-0066}, author = {Frederick Maier and Yue Ma and Pascal Hitzler} } @article {62, title = {Reasoning with Inconsistencies in Hybrid MKNF Knowledge Bases}, journal = {Logic Journal of the IGPL}, volume = {21}, year = {2013}, pages = {263{\textendash}290}, abstract = {This paper is concerned with the handling of inconsistencies occurring in the combination of description logics and rules, especially in hybrid MKNF knowledge bases. More precisely, we present a paraconsistent semantics for hybrid MKNF knowledge bases (called para-MKNF knowledge bases) based on four-valued logic as proposed by Belnap. We also reduce this paraconsistent semantics to the stable model semantics via a linear transformation operator, which shows the relationship between the two semantics and indicates that the data complexity in our paradigm is not higher than that of classical reasoning. Moreover, we provide fixpoint operators to compute paraconsistent MKNF models, each suitable to different kinds of rules. At last we present the data complexity of instance checking in different paraMKNF knowledge bases.}, keywords = {Data complexity, Description logics and rules, Knowledge representation, Non-monotonic reasoning, Paraconsistent reasoning}, doi = {10.1093/jigpal/jzs043}, url = {http://dx.doi.org/10.1093/jigpal/jzs043}, author = {Shasha Huang and Qingguo Li and Pascal Hitzler} } @conference {128, title = {Towards an Efficient Algorithm to Reason over Description Logics Extended with Nominal Schemas}, booktitle = {Web Reasoning and Rule Systems - 7th International Conference, {RR} 2013, Mannheim, Germany, July 27-29, 2013. Proceedings}, year = {2013}, pages = {65{\textendash}79}, abstract = {

Extending description logics with so-called nominal schemas has been shown to be a major step towards integrating description logics with rules paradigms. However, establishing efficient algorithms for reasoning with nominal schemas has so far been a challenge. In this paper, we present an algorithm to reason with the description logic fragment ELROVn, a fragment that extends EL++ with nominal schemas. We also report on an implementation and experimental evaluation of the algorithm, which shows that our approach is indeed rather efficient.

}, keywords = {description logics, EL++, Nominal Schemas}, doi = {10.1007/978-3-642-39666-3_6}, url = {http://dx.doi.org/10.1007/978-3-642-39666-3_6}, author = {David Carral and Cong Wang and Pascal Hitzler} } @conference {127, title = {Extending Description Logic Rules}, booktitle = {The Semantic Web: Research and Applications - 9th Extended Semantic Web Conference, ESWC 2012, Heraklion, Crete, Greece, May 27-31, 2012. Proceedings}, year = {2012}, pages = {345{\textendash}359}, abstract = {

Description Logics {\textendash} the logics underpinning the Web Ontology Language OWL {\textendash} and rules are currently the most prominent paradigms used for modeling knowledge for the Semantic Web. While both of these approaches are based on classical logic, the paradigms also differ significantly, so that naive combinations result in undesirable properties such as undecidability. Recent work has shown that many rules can in fact be expressed in OWL. In this paper we extend this work to include some types of rules previously excluded. We formally define a set of first order logic rules, C-Rules, which can be expressed within OWL extended with role conjunction. We also show that the use of nominal schemas results in even broader coverage.

}, keywords = {description logics, OWL, Rules}, doi = {10.1007/978-3-642-30284-8_30}, url = {http://dx.doi.org/10.1007/978-3-642-30284-8_30}, author = {David Carral and Pascal Hitzler} } @conference {126, title = {A logical geo-ontology design pattern for quantifying over types}, booktitle = {SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems (formerly known as GIS), SIGSPATIAL{\textquoteright}12, Redondo Beach, CA, USA, November 7-9, 2012}, year = {2012}, pages = {239{\textendash}248}, keywords = {Biodiversity, description logics, Ontology Design Patterns, OWL}, doi = {10.1145/2424321.2424352}, url = {http://doi.acm.org/10.1145/2424321.2424352}, author = {David Carral and Krzysztof Janowicz and Pascal Hitzler} } @conference {124, title = {Recent Advances in Integrating {OWL} and Rules}, booktitle = {Web Reasoning and Rule Systems - 6th International Conference, RR 2012, Vienna, Austria, September 10-12, 2012. Proceedings}, volume = {7497}, year = {2012}, month = {09/2012}, pages = {225-228}, publisher = {Springer}, organization = {Springer}, address = {Austria, Vienna}, abstract = {As part of the quest for a unifying logic for the Semantic Web Technology Stack, a central issue is finding suitable ways of integrating description logics based on the Web Ontology Language (OWL) with rule-based approaches based on logic programming. Such integration is difficult since naive approaches typically result in the violation of one or more desirable design principles. For example, while both OWL 2 DL and RIF Core (a dialect of the Rule Interchange Format RIF) are decidable, their naive union is not, unless carefully chosen syntactic restrictions are applied. We report on recent advances and ongoing work by the authors in integrating OWL and rulesWe take an OWL-centric perspective, which means that we take OWL 2 DL as a starting point and pursue the question of how features of rulebased formalisms can be added without jeopardizing decidability. We also report on incorporating the closed world assumption and on reasoning algorithms. This paper essentially serves as an entry point to the original papers, to which we will refer throughout, where detailed expositions of the results can be found.}, keywords = {description logics, OWL, Rules}, doi = {10.1007/978-3-642-33203-6_20}, url = {http://dx.doi.org/10.1007/978-3-642-33203-6_20}, author = {Matthias Knorr and David Carral and Pascal Hitzler and Adila Krisnadhi and Frederick Maier and Cong Wang}, editor = {Markus Kr{\"o}tzsch and Umberto Straccia} } @article {65, title = {Type-Elimination-Based Reasoning for the Description Logic SHIQbs using Decision Diagrams and Disjunctive Datalog}, journal = {Logical Methods in Computer Science}, volume = {8}, year = {2012}, abstract = {We propose a novel, type-elimination-based method for standard reasoning in the description logic SHIQbs extended by DL-safe rules. To this end, we first establish a knowledge compilation method converting the terminological part of an ALCIb knowledge base into an ordered binary decision diagram (OBDD) that represents a canonical model. This OBDD can in turn be transformed into disjunctive Datalog and merged with the assertional part of the knowledge base in order to perform combined reasoning. In order to leverage our technique for full SHIQbs, we provide a stepwise reduction from SHIQbs to ALCIb that preserves satisfiability and entailment of positive and negative ground facts. The proposed technique is shown to be worst-case optimal w.r.t. combined and data complexity.}, keywords = {datalog, decision diagrams, description logics, type elimination}, doi = {10.2168/LMCS-8(1:12)2012}, url = {http://dx.doi.org/10.2168/LMCS-8(1:12)2012}, author = {Sebastian Rudolph and Markus Kr{\"o}tzsch and Pascal Hitzler} } @conference {160, title = {Very Large Scale OWL Reasoning through Distributed Computation}, booktitle = {11th International Semantic Web Conference (ISWC 2012), Proceedings, Part II}, volume = {7650}, year = {2012}, pages = {407{\textendash}414}, publisher = {Springer}, organization = {Springer}, address = {Boston, MA, USA}, abstract = {

Due to recent developments in reasoning algorithms of the\ various OWL profiles, the classification time for an ontology has come\ down drastically. For all of the popular reasoners, in order to process\ an ontology, an implicit assumption is that the ontology should fit in\ primary memory. The memory requirements for a reasoner are already\ quite high, and considering the ever increasing size of the data to be\ processed and the goal of making reasoning Web scale, this assumption\ becomes overly restrictive. In our work, we study several distributed\ classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.

}, keywords = {Distributed Reasoning, Ontology Classification, OWL EL}, doi = {10.1007/978-3-642-35173-0_30}, url = {http://dx.doi.org/10.1007/978-3-642-35173-0_30}, author = {Raghava Mutharaju}, editor = {Philippe Cudr{\'e}-Mauroux and Jeff Heflin and Evren Sirin and Tania Tudorache and J{\'e}r{\^o}me Euzenat and Manfred Hauswirth and Josiane Xavier Parreira and James A. Hendler and Guus Schreiber and Abraham Bernstein and Eva Blomqvist} } @conference {139, title = {A Better Uncle for {OWL}: Nominal Schemas for Integrating Rules and Ontologies}, booktitle = {Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, March 28 - April 1, 2011}, year = {2011}, month = {03/2011}, pages = {645-654}, publisher = {ACM}, organization = {ACM}, abstract = {We propose a description-logic style extension of OWL 2 with nominal schemas which can be used like "variable nominal classes" within axioms. This feature allows ontology languages to express arbitrary DL-safe rules (as expressible in SWRL or RIF) in their native syntax. We show that adding nominal schemas to OWL 2 does not increase the worst-case reasoning complexity, and we identify a novel tractable language SROELV3(\cap, x) that is versatile enough to capture the lightweight languages OWL EL and OWL RL.}, keywords = {datalog, Description Logic, Semantic Web Rule Language, SROIQ, tractability, Web Ontology Language}, isbn = {978-1-4503-0632-4}, doi = {10.1145/1963405.1963496}, url = {http://doi.acm.org/10.1145/1963405.1963496}, author = {Markus Kr{\"o}tzsch and Frederick Maier and Adila Krisnadhi and Pascal Hitzler}, editor = {Sadagopan Srinivasan and Krithi Ramamritham and Arun Kumar and M. P. Ravindra and Elisa Bertino and Ravi Kumar} } @article {74, title = {Local Closed World Reasoning with Description Logics under the Well-Founded Semantics}, journal = {Artificial Intelligence}, volume = {175}, year = {2011}, pages = {1528{\textendash}1554}, abstract = {

An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases [52], and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.

}, keywords = {Description Logic, Knowledge representation, Logic Programming, Non-monotonic reasoning, Ontologies, Semantic Web}, doi = {10.1016/j.artint.2011.01.007}, url = {http://dx.doi.org/10.1016/j.artint.2011.01.007}, author = {Matthias Knorr and Jos{\'e} J{\'u}lio Alferes and Pascal Hitzler} } @conference {143, title = {Local Closed World Semantics: Keep it simple, stupid!}, booktitle = {Proceedings of the 24th International Workshop on Description Logics (DL 2011), Barcelona, Spain, July 13-16, 2011}, volume = {745}, year = {2011}, month = {07/2011}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {A combination of open and closed-world reasoning (usually called local closed world reasoning) is a desirable capability of knowledge representation formalisms for Semantic Web applications. However, none of the proposals made to date for extending description logics with local closed world capabilities has had any significant impact on applications. We believe that one of the key reasons for this is that current proposals fail to provide approaches which are intuitively accessible for application developers and at the same time are applicable, as extensions, to expressive description logics such as SROIQ, which underlies the Web Ontology Language OWL. In this paper we propose a new approach which overcomes key limitations of other major proposals made to date. It is based on an adaptation of circumscriptive description logics which, in contrast to previously reported circumscription proposals, is applicable to SROIQ without rendering reasoning over the resulting language undecidable.}, keywords = {circumscription, closed world, decidability, Description Logic}, url = {http://ceur-ws.org/Vol-745/paper_12.pdf}, author = {Adila Krisnadhi and Kunal Sengupta and Pascal Hitzler}, editor = {Riccardo Rosati and Sebastian Rudolph and Michael Zakharyaschev} } @proceedings {314, title = {10302 Abstracts Collection - Learning paradigms in dynamic environments}, journal = {Learning paradigms in dynamic environments}, year = {2010}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany}, address = {Dagstuhl, Germany}, keywords = {Autonomous learning, Dynamic systems, Neural-symbolic integration, Neurobiology, Recurrent neural networks, Speech processing}, issn = {1862-4405}, url = {http://drops.dagstuhl.de/opus/volltexte/2010/2804}, editor = {Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint} } @article {80, title = {Concept learning in description logics using refinement operators}, journal = {Machine Learning}, volume = {78}, year = {2010}, pages = {203{\textendash}250}, abstract = {

With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.

}, keywords = {description logics, Inductive logic programming, OWL, refinement operators, Semantic Web, Structured Machine Learning}, url = {http://springerlink.metapress.com/content/c040n45u15qrnu44/}, author = {Jens Lehmann and Pascal Hitzler} } @article {82, title = {Generalized Distance Functions in the Theory of Computation}, journal = {Computer Journal}, volume = {53}, year = {2010}, pages = {443{\textendash}464}, abstract = {

We discuss a number of distance functions encountered in the theory of computation, including metrics, ultra-metrics, quasi-metrics, generalized ultra-metrics, partial metrics, d-ultra-metrics and generalized metrics. We consider their properties, associated fixed-point theorems and some general applications they have within the theory of computation. We consider in detail the applications of generalized distance functions in giving a uniform treatment of several important semantics for logic programs, including acceptable programs and natural generalizations of them, and also the supported model and the stable model in the context of locally stratified extended disjunctive logic programs and databases.

}, keywords = {denotational semantics, fixed-point theorems, generalized distance functions, Logic Programming, stable model, supported model, topology, ultra-metrics}, doi = {10.1093/comjnl/bxm108}, url = {http://dx.doi.org/10.1093/comjnl/bxm108}, author = {Anthony K. Seda and Pascal Hitzler} }