@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} } @article {983, title = {Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model}, journal = {IEEE Access}, volume = {11}, year = {2023}, pages = {42343-42360}, keywords = {Biological system modeling, bottom-up parsing, Convolutional Neural Network, Convolutional neural networks, Feature extraction, Location awareness, occlusion, Pose estimation, skeletal keypoint, Solid modeling}, doi = {10.1109/ACCESS.2023.3271593}, author = {Aonty, Shuhena Salam and Deb, Kaushik and Sarma, Moumita Sen and Dhar, Pranab Kumar and Shimamura, Tetsuya} } @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 {758, title = {Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption}, booktitle = {2nd International Conference on Sensors, Signal and Image Processing (SSIP 19)}, year = {2019}, publisher = {ACM}, organization = {ACM}, address = {Prague, Czech Republic}, abstract = {

People are living longer than ever due to advances in healthcare, and this has prompted many healthcare providers to look towards remote patient care as a means to meet the needs of the future. It is now a priority to enable people to reside in their own homes rather than in overburdened facilities whenever possible. The increasing maturity of IoT technologies and the falling costs of connected sensors has made the deployment of remote healthcare at scale an increasingly attractive prospect. In this work we demonstrate that we can measure the consistency and regularity of the behaviour of a household using sensor readings generated from interaction with the home environment. We show that we can track changes in this behaviour regularity longitudinally and detect changes that may be related to significant life events or trends that may be medically significant. We achieve this using periodicity analysis on water usage readings sampled from the main household water meter every 15 minutes for over 8 months. We utilise an IoT Application Enablement Platform in conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area Network in order to validate a data collection methodology that could be deployed at large scale in future. We envision the statistical methods described here being applied to data streams from the homes of elderly and at-risk groups, both as a means of\  early illness\  detection\  and\  for\  monitoring\  the well-being of those with known illnesses.

}, keywords = {Ambient Assisted Living, Home Monitoring, Internet of Things, Sensor Applications, Sensor Networks}, author = {Quinn, Se{\'a}n and Murphy, Noel and Smeaton, Alan F.} } @article {890, title = {Machine learning for internet of things data analysis: a survey}, journal = {Digital Communications and Networks}, volume = {4}, year = {2018}, pages = {161-175}, abstract = {

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.

}, keywords = {Internet of Things, Machine learning, Smart City, Smart data}, issn = {2352-8648}, doi = {https://doi.org/10.1016/j.dcan.2017.10.002}, url = {https://www.sciencedirect.com/science/article/pii/S235286481730247X}, author = {Mohammad Saeid Mahdavinejad and Mohammadreza Rezvan}, editor = {Mohammadamin Barekatain and Peyman Adibi and Payam Barnaghi and Amit P. Sheth} } @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} } @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 {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} } @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} } @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} } @article {78, title = {A Reasonable Semantic Web}, journal = {Semantic Web}, volume = {1}, year = {2010}, pages = {39{\textendash}44}, abstract = {

The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as "shared inference," which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on entirely different methods like machine learning or natureinspired computing need to be investigated, while quality assurance needs to be done in terms of precision and recall values (as in information retrieval) and not necessarily in terms of soundness and completeness of the underlying algorithms.

}, keywords = {Automated Reasoning, Formal Semantics, Knowledge representation, Linked Open Data, Semantic Web}, doi = {10.3233/SW-2010-0010}, url = {http://dx.doi.org/10.3233/SW-2010-0010}, author = {Pascal Hitzler and Frank van Harmelen} }