@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 {888, title = {Semantic Compression with Region Calculi in Nested Hierarchical Grids}, booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems}, year = {2021}, pages = {305{\textendash}308}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {

We propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.

}, keywords = {Hierarchical Grids, Knowledge Graphs, RCC5}, isbn = {9781450386647}, doi = {10.1145/3474717.3483965}, url = {https://doi.org/10.1145/3474717.3483965}, author = {Zalewski, Joseph and Hitzler, Pascal and Janowicz, Krzysztof} } @article {864, title = {Semantic Compression with Region Calculi in Nested Hierarchical Grids (Technical Report)}, year = {2021}, abstract = {

We propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.

}, keywords = {Hierarchical Grids, Knowledge Graphs, RCC5}, author = {Joseph Zalewski and Pascal Hitzler and Krzysztof Janowicz} } @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 {775, title = {Multimodal mental health analysis in social media}, journal = {PLoS ONE}, year = {2020}, abstract = {

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}

Depression is a major public health concern in the U.S. and globally. While successful early

identification and treatment can lead to many positive health and behavioral outcomes,

depression, remains undiagnosed, untreated or undertreated due to several reasons,

including denial of the illness as well as cultural and social stigma. With the ubiquity of social

media platforms, millions of people are now sharing their online persona by expressing their

thoughts, moods, emotions, and even their daily struggles with mental health on social

media. Unlike traditional observational cohort studies conducted through questionnaires

and self-reported surveys, we explore the reliable detection of depressive symptoms from

tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)

data to discern depressive behaviors using a wide variety of features including individuallevel

demographics. By developing a multimodal framework and employing statistical techniques

to fuse heterogeneous sets of features obtained through the processing of visual,

textual, and user interaction data, we significantly enhance the current state-of-the-art

approaches for identifying depressed individuals on Twitter (improving the average F1-

Score by 5 percent) as well as facilitate demographic inferences from social media. Besides

providing insights into the relationship between demographics and mental health, our

research assists in the design of a new breed of demographic-aware health interventions.

}, keywords = {Explainable Machine Learning, Hypothesis Testing, National Language Processing, Prediction, Regression}, url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248\&type=printable}, author = {Amir Hossein Yazdavar and Mohammad Saeid Mahdavinejad and Goonmeet Baja and William Romine and Amit Sheth and Amir Hassan Monadjemi and Krishnaprasad Thirunarayan and John M. Meddar and Annie Myers and Jyotishman Pathak 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 {608, title = { Propositional rule extraction from neural networks under background knowledge}, booktitle = {Twelfth International Workshop on Neural-Symbolic Learning and Reasoning}, year = {2017}, month = {07/2017}, keywords = {Background knowledge, Neural Network, Propositional Logic, Rule Extraction}, author = {Maryam Labaf}, editor = {Pascal Hitzler} } @conference {610, title = {A Modification to the Hazardous Situation ODP to Support Risk Assessment and Mitigation}, booktitle = {Workshop on Ontology Design Patterns (WOP)}, year = {2016}, month = {10/2016}, abstract = {

The Hazardous Situation ontology design pattern models the consequences of exposure of an object to a hazard. In its current form, the ODP is well suited for representing the consequences of exposure after the fact, which is very useful for applications such as damage assessment and recovery planning. In this work, we present a modification to this pattern that enables it to additionally support proactive questions central to risk assessment and mitigation planning.

}, keywords = {hazard, Ontology Design Pattern, risk assessment, risk mitigation}, author = {Michelle Cheatham and Ferguson, Holly and Charles, Vardeman and Cogan Shimizu} } @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 {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 {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} } @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} } @conference {112, title = {Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data}, booktitle = {Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010}, volume = {6187}, year = {2010}, pages = {461{\textendash}470}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg, Germany}, abstract = {

The Semantic Web Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current Semantic Web provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples that hinders data sharing. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses the notion of provenance context to create provenance-aware RDF triples without the use of RDF reification or blank nodes. We also define the formal semantics of PaCE through a simple extension of the existing RDF(S) semantics that ensures compatibility of PaCE with existing Semantic Web tools and implementations. We have implemented the PaCE approach in the Biomedical Knowledge Repository (BKR) project at the US National Library of Medicine to support provenance tracking on RDF data extracted from multiple sources, including biomedical literature and the UMLS Metathesaurus. The evaluations demonstrate a minimum of 49\% reduction in total number of provenancespecific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, using the PACE approach improves the performance of complex provenance queries by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries.\ 

}, keywords = {Biomedical knowledge repository, Context theory, Provenance context entity, Provenance Management Framework., Provenir ontology, RDF reification}, doi = {10.1007/978-3-642-13818-8_32}, url = {http://dx.doi.org/10.1007/978-3-642-13818-8_32}, author = {Satya S. Sahoo and Olivier Bodenreider and Pascal Hitzler and Amit Sheth and Krishnaprasad Thirunarayan}, editor = {Michael Gertz and Bertram Lud{\"a}scher} }