01891nas a2200193 4500008004100000245005900041210005900100260002200159300001400181520124700195653003001442653002501472653001901497653002701516100001701543700002101560700002001581856009601601 2025 eng d00aHidden Neuron Activation Analysis on Labeled Text Data0 aHidden Neuron Activation Analysis on Labeled Text Data aUSAbACMc12/2025 a206 - 2103 a

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’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.

10aConcept-based Explanation10aDense Layer Analysis10aExplainable AI10aHidden Neuron Analysis1 aDas, Avishek1 aDalal, Abhilekha1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/hidden-neuron-activation-analysis-labeled-text-data01281nas a2200193 4500008004100000245006600041210006300107520069400170653001400864653002800878653001300906100002100919700002400940700002100964700002600985700002001011700001901031856003701050 2025 eng d00aAn Ontology for Representing Curriculum and Learning Material0 aOntology for Representing Curriculum and Learning Material3 aEducational, learning, and training materials have become extremely commonplace across the Internet. Yet, they frequently remain disconnected from each other, fall into platform silos, and so on. One way to overcome this is to provide a mechanism to integrate the material and provide cross-links across topics. In this paper, we present the Curriculum KG Ontology, which we use as a framework for the dense interlinking of educational materials, by first starting with organizational and broad pedagogical principles. We provide a materialized graph for the Prototype Open Knowledge Network use-case, and validate it using competency questions sourced from domain experts and educators. 10aeducation10amodular ontology design10aontology1 aChristou, Antrea1 aJaldi, Chris, Davis1 aZalewski, Joseph1 aMcGinty, Hande, Kucuk1 aHitzler, Pascal1 aShimizu, Cogan uhttps://arxiv.org/abs/2506.0575101724nas a2200181 4500008004100000245006500041210006500106520107500171653002101246653001801267653004001285653002001325653001701345100003401362700002101396700002001417856010501437 2024 eng d00aAutomating CNN Neuron Interpretation using Concept Induction0 aAutomating CNN Neuron Interpretation using Concept Induction3 a

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.

10aAutomation in AI10aDeep Learning10aExplainable Artificial Intelligence10aKnowledge Graph10aSemantic Web1 aAkkamahadevi, Samatha, Ereshi1 aDalal, Abhilekha1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/automating-cnn-neuron-interpretation-using-concept-induction01220nas a2200169 4500008004100000245006200041210006100103260001400164520066400178653000800842653002200850653001900872100002100891700002000912700002000932856009800952 2024 eng d00aError-margin Analysis for Hidden Neuron Activation Labels0 aErrormargin Analysis for Hidden Neuron Activation Labels bSpringer 3 a

Understanding how high-level concepts are represented with- in artificial neural networks is a fundamental challenge in the field of arti- ficial intelligence. While existing literature in explainable AI emphasizes the importance of labeling neurons with concepts to understand their functioning, they mostly focus on identifying what stimulus activates a neuron in most cases; this corresponds to the notion of recall in informa- tion retrieval. We argue that this is only the first-part of a two-part job; it is imperative to also investigate neuron responses to other stimuli, i.e., their precision. We call this the neuron label’s error margin.

10aCNN10aConcept Induction10aExplainable AI1 aDalal, Abhilekha1 aRayan, Rushrukh1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/error-margin-analysis-hidden-neuron-activation-labels01734nas a2200145 4500008004100000245009800041210006900139520113700208653002201345653003301367653001901400653002001419100002101439856012801460 2024 eng d00aUnderstanding CNN Hidden Neuron Activations using Concept Induction over Background Knowledge0 aUnderstanding CNN Hidden Neuron Activations using Concept Induct3 a

A major challenge in Explainable AI is interpreting hidden neuron activations accurately. These in- terpretations can reveal what a deep learning system perceives as relevant in the input data, thereby addressing the black-box nature of such systems. The state of the art indicates that hidden node acti- vations can be interpretable by humans, but there’s a lack of systematic automated methods to verify these interpretations, especially those that utilize substantial background knowledge and inherently explainable methods. In this proposal, we introduce a novel model-agnostic post-hoc Explainable AI method based on a Wikipedia-derived concept hierarchy with approximately 2 million classes. Our approach utilizes OWL-reasoning-based Concept Induction for explanation generation and compares with off-the-shelf pre-trained multimodal-based explainable methods. Our results demonstrate that our method automatically provides meaningful class expressions as explanations to individual neurons in the dense layer of a Convolutional Neural Network, outperforming prior work in both quantitative and qualitative aspects.

10aConcept Induction10aConvolutional Neural Network10aExplainable AI10aKnowledge Graph1 aDalal, Abhilehka uhttps://daselab.cs.ksu.edu/publications/understanding-cnn-hidden-neuron-activations-using-concept-induction-over-background01357nas a2200217 4500008004100000245009200041210006900133260001400202520061900216653000800835653002200843653001900865653000800884100002100892700002000913700001800933700002600951700002400977700002001001856011801021 2024 eng d00aOn the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis0 aValue of Labeled Data and Symbolic Methods for Hidden Neuron Act bSpringer 3 a

We introduce a novel model-agnostic post-hoc Explainable AI method that provides meaningful interpretations for hidden neuron activations in a Convolutional Neural Network. Our approach uses a Wikipedia-derived concept hierarchy with approx. 2 million classes as background knowledge, and deductive reasoning based Concept Induc- tion for explanation generation. Additionally, we explore and compare the capabilities of off-the-shelf pre-trained multimodal-based explainable methods. Our evaluation shows that our neurosymbolic method holds a competitive edge in both quantitative and qualitative aspects.

10aCNN10aConcept Induction10aExplainable AI10aLLM1 aDalal, Abhilekha1 aRayan, Rushrukh1 aBarua, Adrita1 aVasserman, Eugene, Y.1 aSarker, Kamruzzaman1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/value-labeled-data-and-symbolic-methods-hidden-neuron-activation-analysis01414nas a2200241 4500008004100000245006500041210006400106260007400170490000900244520070200253653001800955653002200973653000800995653000901003653000901012653001401021100002001035700002201055700001301077700001901090700002001109856004301129 2020 eng d00aCompletion Reasoning Emulation for the Description Logic EL+0 aCompletion Reasoning Emulation for the Description Logic EL aStanford University, Palo Alto, California, USAbCEUR-WS.orgc03/20200 v26003 a

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's and LSTM's predictions on corrupt data with correct answers.

10aDeep Learning10aDescription Logic10aEL+10aLSTM10aNeSy10aReasoning1 aEberhart, Aaron1 aEbrahimi, Monireh1 aZhou, Lu1 aShimizu, Cogan1 aHitzler, Pascal uhttp://ceur-ws.org/Vol-2600/paper5.pdf02855nas a2200277 4500008004100000245005400041210005400095520195300149653003302102653002302135653003302158653001502191653001502206100002802221700003402249700001902283700002002302700001602322700002802338700003202366700002102398700001702419700002302436700002002459856009802479 2020 eng d00aMultimodal mental health analysis in social media0 aMultimodal mental health analysis in social media3 a

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.

10aExplainable Machine Learning10aHypothesis Testing10aNational Language Processing10aPrediction10aRegression1 aYazdavar, Amir, Hossein1 aMahdavinejad, Mohammad, Saeid1 aBaja, Goonmeet1 aRomine, William1 aSheth, Amit1 aMonadjemi, Amir, Hassan1 aThirunarayan, Krishnaprasad1 aMeddar, John, M.1 aMyers, Annie1 aPathak, Jyotishman1 aHitzler, Pascal uhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&type=printable01534nas a2200241 4500008004100000245007700041210006900118260004100187520073700228653002000965653001500985653001901000653001301019653002001032653001801052100001901070700002001089700001901109700001601128700002101144700002001165856010701185 2019 eng d00aA Method for Automatically Generating Schema Diagrams for OWL Ontologies0 aMethod for Automatically Generating Schema Diagrams for OWL Onto aVilla Clara, CubabSpringerc06/20193 a

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.

10adesign patterns10aevaluation10aimplementation10aontology10aschema diagrams10avisualization1 aShimizu, Cogan1 aEberhart, Aaron1 aKarima, Nazifa1 aHirt, Quinn1 aKrisnadhi, Adila1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/method-automatically-generating-schema-diagrams-owl-ontologies01783nas a2200253 4500008003900000245005000039210004800089260004400137300001000181490000900191520104900200653001901249653001101268653002601279653000801305653000801313653001601321100002301337700002001360700002201380700002101402700002001423856008601443 2013 d00aDistEL: A Distributed EL+ Ontology Classifier0 aDistEL A Distributed EL Ontology Classifier aSydney, AustraliabCEUR-WS.orgc10/2013 a17-320 v10463 aOWL 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.10aClassification10aDistEL10aDistributed Reasoning10aEL+10aOWL10aScalability1 aMutharaju, Raghava1 aHitzler, Pascal1 aMateti, Prabhaker1 aLiebig, Thorsten1 aFokoue, Achille uhttps://daselab.cs.ksu.edu/publications/distel-distributed-el-ontology-classifier01105nas a2200169 4500008004100000245009900041210006900140300001200209520055900221653002300780653000900803653002000812100001800832700001500850700002000865856005000885 2013 eng d00aTowards an Efficient Algorithm to Reason over Description Logics Extended with Nominal Schemas0 aTowards an Efficient Algorithm to Reason over Description Logics a65–793 a

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.

10adescription logics10aEL++10aNominal Schemas1 aCarral, David1 aWang, Cong1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/978-3-642-39666-3_6