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-data01537nas a2200157 4500008004100000245009300041210006900134520093500203653002301138653002101161653000901182100002101191700002001212700002401232856012301256 2021 eng d00aSemantic Compression with Region Calculi in Nested Hierarchical Grids (Technical Report)0 aSemantic Compression with Region Calculi in Nested Hierarchical 3 a

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.

10aHierarchical Grids10aKnowledge Graphs10aRCC51 aZalewski, Joseph1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/semantic-compression-region-calculi-nested-hierarchical-grids-technical-report01713nas a2200313 4500008004100000245006300041210005800104260001200162490000700174520076200181653002100943653002300964653003100987653002101018653002901039100001901068700002001087700001601107700002001123700003001143700002101173700002301194700002201217700001701239700001901256700001601275700001701291856009101308 2020 eng d00aThe Enslaved Ontology: Peoples of the Historic Slave Trade0 aEnslaved Ontology Peoples of the Historic Slave Trade c08/20200 v633 a

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.

10adata integration10adigital humanities10ahistory of the slave trade10amodular ontology10aOntology Design Patterns1 aShimizu, Cogan1 aHitzler, Pascal1 aHirt, Quinn1 aRehberger, Dean1 aEstrecha, Seila, Gonzalez1 aFoley, Catherine1 aSheill, Alicia, M.1 aHawthorne, Walter1 aMixter, Jeff1 aWatrall, Ethan1 aCarty, Ryan1 aTarr, Duncan uhttps://daselab.cs.ksu.edu/publications/enslaved-ontology-peoples-historic-slave-trade02855nas 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=printable01097nas a2200229 4500008004100000245008000041210006900121260002500190300001100215490000900226520039900235653002300634653001600657653000800673100001800681700002100699700002300720700002000743700001600763700002800779856006000807 2014 eng d00aAll But Not Nothing: Left-Hand Side Universals for Tractable {OWL} Profiles0 aAll But Not Nothing LeftHand Side Universals for Tractable OWL P bCEUR-WS.orgc10/2014 a97-1080 v12653 aWe 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.10adescription logics10aHorn Logics10aOWL1 aCarral, David1 aKrisnadhi, Adila1 aRudolph, Sebastian1 aHitzler, Pascal1 aKeet, Maria1 aTamma, Valentina, A. M. uhttp://ceur-ws.org/Vol-1265/owled2014_submission_13.pdf01631nas a2200181 4500008004100000245004400041210004400085300000600129490000700135520112800142653002901270653002301299653001501322100002201337700002301359700002001382856004701402 2013 eng d00aComplexities of Horn Description Logics0 aComplexities of Horn Description Logics a20 v143 aDescription 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.10acomputational complexity10adescription logics10aHorn logic1 aKrötzsch, Markus1 aRudolph, Sebastian1 aHitzler, Pascal uhttp://doi.acm.org/10.1145/2422085.2422087