@article {987, title = {Neuro-Symbolic AI and the Semantic Web}, journal = {Semantic Web}, year = {2024}, author = {Pascal Hitzler and Monireh Ebrahimi and Md Kamruzzaman Sarker and Daria Stepanova} } @article {905, title = {Explaining Deep Learning Hidden Neuron Activations using Concept Induction}, year = {2023}, abstract = {

One of the current key challenges in Explainable AI is in correctly interpreting activations of hidden neurons. It seems evident that accurate interpretations thereof would provide insights into the question what a deep learning system has internally detected as relevant on the input, thus lifting some of the black box character of deep learning systems.

The state of the art on this front indicates that hidden node activations appear to be interpretable in a way that makes sense to humans, at least in some cases. Yet, systematic automated methods that would be able to first hypothesize an interpretation of hidden neuron activations, and then verify it, are mostly missing.\ 

In this paper, we provide such a method and demonstrate that it provides meaningful interpretations. It is based on using large-scale background knowledge -- a class hierarchy of approx. 2 million classes curated from the Wikipedia Concept Hierarchy -- together with a symbolic reasoning approach called concept induction\ based on description logics that was originally developed for applications in the Semantic Web field.\ 

Our results show that we can automatically attach meaningful labels from the background knowledge to individual neurons in the dense layer of a Convolutional Neural Network through a hypothesis and verification process.

}, author = {Abhilekha Dalal and Md Kamruzzaman Sarker and Adrita Barua and Pascal Hitzler} } @article {965, title = {Secure Architecture for Biometric Authentication}, year = {2023}, author = {George T. Amariucai and Pascal Hitzler and Abhishek Jana and Monireh Ebrahimi and Md Kamruzzaman Sarker} } @article {944, title = {Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge}, journal = {Journal of Web Semantics}, volume = {79:100807}, year = {2023}, author = {Cara Widmer and Md Kamruzzaman Sarker and Srikanth Nadella and Joshua Fiechter and Ion Juvina and Brandon Minnery and Pascal Hitzler and Joshua Schwartz and Michael Raymer} } @article {946, title = {Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication}, journal = {Proceedings of Privacy Enhancing Technologies }, volume = {2022 (4)}, year = {2022}, pages = {86-104}, chapter = {86}, author = {Abhishek Jana and Bipin Paudel and Md Kamruzzaman Sarker and Monireh Ebrahimi and Pascal Hitzler and George T Amariucai} } @article {897, title = {Neuro-symbolic approaches in artificial intelligence}, journal = {National Science Review}, volume = {9}, year = {2022}, month = {06/2022}, doi = {https://doi.org/10.1093/nsr/nwac035}, url = {https://academic.oup.com/nsr/article/9/6/nwac035/6542460}, author = {Pascal Hitzler and Aaron Eberhart and Monireh Ebrahimi and Md Kamruzzaman Sarker and Lu Zhou} } @article {862, title = {Neuro-Symbolic Artificial Intelligence: Current Trends}, journal = {AI Communications}, volume = {34}, year = {2022}, month = {2021}, chapter = {197}, author = {Md Kamruzzaman Sarker and Lu Zhou and Aaron Eberhart and Pascal Hitzler} } @book {901, title = {Neuro-Symbolic Artificial Intelligence - The State of the Art}, series = {Frontiers in Artificial Intelligence and Applications}, number = {342}, year = {2022}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, url = {https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art}, author = {Pascal Hitzler and Md Kamruzzaman Sarker} } @conference {860, title = {Expressibility of OWL Axioms with Patterns}, booktitle = {ESWC 2021}, year = {2021}, abstract = {

The high expressivity of the Web Ontology Language (OWL) makes it possible to describe complex relationships between classes, roles, and individuals in an ontology. However, this high expressivity can be an obstacle to correct usage and wide adoption. Past attempts to ameliorate this have included the development of specific, presumably human-friendly syntaxes, such as the Manchester syntax or graphical interfaces for OWL axioms, albeit with limited success. If modelers want to develop suitable OWL axioms it is important to make this as easy as possible. In this paper, we adopt an idea from the Prot{\'e}g{\'e} plug-in, OWLAx, which provides a simple, clickable interface to automatically input axioms of a limited number of types by following simple axiom patterns. In particular, each of these axiom patterns contains at most three classes or roles. We hypothesize that most of the axioms in existing ontologies could be expressed semantically in terms of simple patterns like these, which would mean that more complex patterns can be used very sparingly. Our findings, based on an analysis of 518 ontologies from six public ontology repositories, confirm this hypothesis: Over 90\% of class axioms in the average ontology are indeed expressible with our simple patterns. We provide a detailed analysis of our findings.

}, author = {Aaron Eberhart and Cogan Shimizu and Sulogna Chowdhury and Md Kamruzzaman Sarker and Pascal Hitzler} } @conference {948, title = {Expressibility of OWL Axioms with Patterns}, booktitle = {The Semantic Web {\textendash} 18th International Conference, ESWC 2021}, year = {2021}, pages = {230-245}, author = {Aaron Eberhart and Cogan M Shimizu and Sulogna Chowdhury and Md Kamruzzaman Sarker and Pascal Hitzler} } @conference {856, title = {Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment}, booktitle = {AAAI-MAKE 2021}, year = {2021}, publisher = {AAAI}, organization = {AAAI}, author = {Monireh Ebrahimi and Md Kamruzzaman Sarker and Federico Bianchi and Ning Xie and Aaron Eberhart and Derek Doran and HyeongSik Kim and Pascal Hitzler} } @article {853, title = {Counterfactual reasoning over large-scale human performance optimization experiments}, journal = {Virtual poster presented at the annual meeting of the Psychonomic Society, November 2020}, year = {2020}, author = {Ion Juvina and William R. Aue and Brandon Minnery and Pascal Hitzler and Srikanth Nadella and Md Kamruzzaman Sarker} } @article {790, title = {Neural-Symbolic Integration and the Semantic Web}, journal = {Semantic Web}, volume = {11}, year = {2020}, url = {http://www.semantic-web-journal.net/content/neural-symbolic-integration-and-semantic-web-0}, author = {Pascal Hitzler and Federico Bianchi and Monireh Ebrahimi and Md Kamruzzaman Sarker} } @conference {813, title = {Wikipedia Knowledge Graph for Explainable AI}, booktitle = {Second Iberoamerican Knowledge Graphs and Semantic Web Conference (KGSWC)}, year = {2020}, month = {11/2020}, abstract = {

Explainable artificial intelligence (XAI) requires domain information to explain a system{\textquoteright}s decisions, for which structured forms of domain information like Knowledge Graphs (KGs) or ontologies are best suited. As such, readily available KGs are important to accelerate progress in XAI. To facilitate the advancement of XAI, we present the Wikipedia Knowledge Graph (WKG), based on information from English Wikipedia. Each Wikipedia article title, its corresponding category, and the category hierarchy are transformed into different entities in the knowledge graph. As the Wikipedia category hierarchy is not a tree, instead forming a graph, to make the finding process of the parent category easier, we break cycles in the category hierarchy. We evaluate whether the WKG is helpful to improve XAI compared with existing KGs, finding that WKG is better suited than the current state of the art. We also compare the cycle-free WKG with the Suggested Upper Merged Ontology (SUMO) and DBpedia schema KGs, finding minimal to no information loss.

}, author = {Md Kamruzzaman Sarker and Joshua Schwartz and Pascal Hitzler and Lu Zhou and Srikanth Nadella and Brandon Minnery and Ion Juvina and Michael L. Raymer and William R. Aue} } @conference {713, title = {Efficient Concept Induction for Description Logics}, booktitle = {AAAI Conference on Artificial Intelligence}, volume = {33}, year = {2019}, month = {01/2019}, publisher = {AAAI}, organization = {AAAI}, address = {Honolulu, US}, abstract = {

Concept Induction refers to the problem of creating complex Description Logic class descriptions (i.e., TBox axioms) from instance examples (i.e.,\  ABox data). In this paper we look particularly at the case where both a set of positive and a set of negative instances are given, and complex class expressions are sought under which the positive but not the negative examples fall. Concept induction has found applications in ontology engineering, but existing algorithms have fundamental performance issues in some scenarios, mainly because a high number of invokations of an external Description Logic reasoner is usually required. In this paper we present a new algorithm for this problem which drastically reduces the number of reasoner invokations needed. While this comes at the expense of a more limited traversal of the search space, we show that our approach improves execution times by up to several orders of magnitude, while output correctness, measured in the amount of correct coverage of the input instances, remains reasonably high in many cases. Our approach thus should provide a strong alternative to existing systems, in particular in settings where other systems are prohibitively slow.

}, author = {Md Kamruzzaman Sarker and Pascal Hitzler} } @article {717, title = {Reasoning over RDF Knowledge Bases using Deep Learning}, journal = {arXiv preprint arXiv:1811.04132}, year = {2018}, abstract = {

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever-increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.

}, url = {https://arxiv.org/abs/1811.04132}, author = {Ebrahimi, Monireh and Md Kamruzzaman Sarker and Bianchi, Federico and Xie, Ning and Doran, Derek and Pascal Hitzler} } @proceedings {606, title = {Explaining Trained Neural Networks with Semantic Web Technologies: First Steps}, journal = {Twelveth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy}, year = {2017}, month = {07/2017}, edition = {12}, address = {London, UK}, abstract = {

The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept.

}, keywords = {Artificial Intelligence}, url = {http://daselab.cs.wright.edu/nesy/NeSy17/}, author = {Md Kamruzzaman Sarker and Ning Xie and Derek Doran and Michael Raymer and Pascal Hitzler} } @proceedings {647, title = {Relating Input Concepts to Convolutional Neural Network Decisions}, journal = {NIPS 2017 Workshop: Interpreting, Explaining and Visualizing Deep Learning, NIPS IEVDL 2017}, year = {2017}, month = {12/2017}, publisher = {NIPS}, address = {CA, USA}, abstract = {

Many current methods to interpret convolutional neural networks (CNNs) use visualization techniques and words to highlight concepts of the input seemingly relevant to a CNN{\textquoteright}s decision. The methods hypothesize that the recognition of these concepts are instrumental in the decision a CNN reaches, but the nature of this relationship has not been well explored. To address this gap, this paper examines the quality of a concept{\textquoteright}s recognition by a CNN and the degree to which the recognitions are associated with CNN decisions. The study considers a CNN trained for scene recognition over the ADE20k dataset. It uses a novel approach to find and score the strength of minimally distributed representations of input concepts (defined by objects in scene images) across late stage feature maps. Subsequent analysis finds evidence that concept recognition impacts decision making. Strong recognition of concepts frequently-occurring in few scenes are indicative of correct decisions, but recognizing concepts common to many scenes may mislead the network.

}, author = {Ning Xie and Md Kamruzzaman Sarker and Derek Doran and Pascal Hitzler and Michael Raymer} } @booklet {586, title = {Rule-based OWL Modeling with ROWLTab Protege Plugin}, year = {2017}, abstract = {

It has been argued that it is much easier to convey logi- cal statements using rules rather than OWL (or description logic (DL)) axioms. Based on recent theoretical developments on transformations between rules and DLs, we have developed ROWLTab, a Prot ́eg ́e plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL 2 DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule. In this paper, we present ROWLTab, together with a user evaluation of its effectiveness compared to entering axioms using the standard Prot ́eg ́e interface. Our evaluation shows that modeling with ROWLTab is much quicker than the standard interface, while at the same time, also less prone to errors for hard modeling tasks.

}, author = {Md Kamruzzaman Sarker and Adila Krisnadhi and David Carral and Pascal Hitzler} } @conference {572, title = {Modeling OWL with Rules: The ROWL Protege Plugin}, year = {2016}, publisher = {15th International Semantic Web Conference (ISWC) 2016}, organization = {15th International Semantic Web Conference (ISWC) 2016}, address = {Kobe, Japan}, abstract = {

Abstract. In our experience, some ontology users find it much easier to convey logical statements using rules rather than OWL (or description logic) axioms. Based on recent theoretical developments on transformations between rules and description logics, we develop ROWL, a Proteg{\textasciiacute} e plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule.

}, url = {http://ceur-ws.org/Vol-1690/paper92.pdf}, author = {Md Kamruzzaman Sarker and David Carral and Adila Krisnadhi and Pascal Hitzler} } @conference {573, title = {OWLAx: A Protege Plugin to Support Ontology Axiomatization through Diagramming}, year = {2016}, publisher = {15th International Semantic Web Conference, ISWC2016, Kobe, Japan, October 2016}, organization = {15th International Semantic Web Conference, ISWC2016, Kobe, Japan, October 2016}, address = {Kobe, Japan}, abstract = {

Abstract. Once the conceptual overview, in terms of a somewhat informal class diagram, has been designed in the course of engineering an ontology, the process of adding many of the appropriate logical axioms is mostly a routine task. We provide a Prot{\textasciiacute}eg{\textasciiacute}e3 plugin which supports this task, together with a visual user interface, based on established methods for ontology design pattern modeling.

}, url = {http://ceur-ws.org/Vol-1690/paper83.pdf}, author = {Md Kamruzzaman Sarker and Adila Krisnadhi and Pascal Hitzler} } @conference {514, title = {Emotion recognition from speech based on relevant feature and majority voting}, booktitle = {ICIEV}, year = {2014}, abstract = {

This paper proposes an approach to detect emotion from human speech employing majority voting technique over several machine learning techniques. The contribution of this work is in two folds: firstly it selects those features of speech which is most promising for classification and secondly it uses the majority voting technique that selects the exact class of emotion. Here, majority voting technique has been applied over Neural Network (NN), Decision Tree (DT), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Input vector of NN, DT, SVM and KNN consists of various acoustic and prosodic features like Pitch, Mel-Frequency Cepstral coefficients etc. From speech signal many feature have been extracted and only promising features have been selected. To consider a feature as promising, Fast Correlation based feature selection (FCBF) and Fisher score algorithms have been used and only those features are selected which are highly ranked by both of them. The proposed approach has been tested on Berlin dataset of emotional speech [3] and Electromagnetic Articulography (EMA) dataset [4]. The experimental result shows that majority voting technique attains better accuracy over individual machine learning techniques. The employment of the proposed approach can effectively recognize the emotion of human beings in case of social robot, intelligent chat client, call-center of a company etc.

}, author = {Md Kamruzzaman Sarker and Kazi Md Rokibul, Alam and Md Arifuzzaman} }