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-data01220nas 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-labels01357nas 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-analysis01631nas 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/10.1145/2422085.242208701783nas 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-classifier02037nas a2200229 4500008004100000245004200041210004200083300001400125490000600139520143400145653002401579653001501603653002201618653000801640653002001648653001701668653002601685100002101711700001201732700002001744856004301764 2013 eng d00aParaconsistent OWL and Related Logics0 aParaconsistent OWL and Related Logics a395–4270 v43 aThe 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.10aAutomated Deduction10aComplexity10aDescription Logic10aOWL10aParaconsistency10aSemantic Web10aWeb Ontology Language1 aMaier, Frederick1 aMa, Yue1 aHitzler, Pascal uhttp://dx.doi.org/10.3233/SW-2012-006601673nas a2200229 4500008004100000245005800041210005500099260002500154490000800179520100400187653002001191653001701211653001701228653002201245100002101267700002001288700002001308700002101328700002301349700002701372856004401399 2011 eng d00aLocal Closed World Semantics: Keep it simple, stupid!0 aLocal Closed World Semantics Keep it simple stupid bCEUR-WS.orgc07/20110 v7453 aA combination of open and closed-world reasoning (usually called local closed world reasoning) is a desirable capability of knowledge representation formalisms for Semantic Web applications. However, none of the proposals made to date for extending description logics with local closed world capabilities has had any significant impact on applications. We believe that one of the key reasons for this is that current proposals fail to provide approaches which are intuitively accessible for application developers and at the same time are applicable, as extensions, to expressive description logics such as SROIQ, which underlies the Web Ontology Language OWL. In this paper we propose a new approach which overcomes key limitations of other major proposals made to date. It is based on an adaptation of circumscriptive description logics which, in contrast to previously reported circumscription proposals, is applicable to SROIQ without rendering reasoning over the resulting language undecidable.10acircumscription10aclosed world10adecidability10aDescription Logic1 aKrisnadhi, Adila1 aSengupta, Kunal1 aHitzler, Pascal1 aRosati, Riccardo1 aRudolph, Sebastian1 aZakharyaschev, Michael uhttp://ceur-ws.org/Vol-745/paper_12.pdf01617nas a2200229 4500008004100000245008100041210006900122300001100191490000600202520090100208653001401109653002901123653003001152653002901182653002301211100001201234700001501246700001701261700002001278700001701298856007201315 2010 eng d00aComputational Complexity and Anytime Algorithm for Inconsistency Measurement0 aComputational Complexity and Anytime Algorithm for Inconsistency a3–210 v43 a

Measuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first give a complete analysis of the computational complexity of computing inconsistency degrees. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximations of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm

10aalgorithm10acomputational complexity10ainconsistency measurement10aKnowledge representation10amulti-valued logic1 aMa, Yue1 aQi, Guilin1 aXiao, Guohui1 aHitzler, Pascal1 aLin, Zuoquan uhttp://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41&flag=102649nas a2200277 4500008004100000245009100041210006900132260003400201300001400235490000900249520174100258653003601999653001902035653003002054653003702084653002202121653002002143100002102163700002502184700002002209700001602229700003202245700001902277700002402296856005102320 2010 eng d00aProvenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data0 aProvenance Context Entity PaCE Scalable Provenance Tracking for  aHeidelberg, GermanybSpringer a461–4700 v61873 a

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. 

10aBiomedical knowledge repository10aContext theory10aProvenance context entity10aProvenance Management Framework.10aProvenir ontology10aRDF reification1 aSahoo, Satya, S.1 aBodenreider, Olivier1 aHitzler, Pascal1 aSheth, Amit1 aThirunarayan, Krishnaprasad1 aGertz, Michael1 aLudäscher, Bertram uhttp://dx.doi.org/10.1007/978-3-642-13818-8_32