01724nas 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-induction01537nas 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-report01470nas a2200205 4500008004100000245006600041210006600107300001400173490000700187520083300194653002001027653003301047653002901080653002801109653002901137100001801166700001601184700002001200856004401220 2013 eng d00aReasoning with Inconsistencies in Hybrid MKNF Knowledge Bases0 aReasoning with Inconsistencies in Hybrid MKNF Knowledge Bases a263–2900 v213 aThis paper is concerned with the handling of inconsistencies occurring in the combination of description logics and rules, especially in hybrid MKNF knowledge bases. More precisely, we present a paraconsistent semantics for hybrid MKNF knowledge bases (called para-MKNF knowledge bases) based on four-valued logic as proposed by Belnap. We also reduce this paraconsistent semantics to the stable model semantics via a linear transformation operator, which shows the relationship between the two semantics and indicates that the data complexity in our paradigm is not higher than that of classical reasoning. Moreover, we provide fixpoint operators to compute paraconsistent MKNF models, each suitable to different kinds of rules. At last we present the data complexity of instance checking in different paraMKNF knowledge bases.10aData complexity10aDescription logics and rules10aKnowledge representation10aNon-monotonic reasoning10aParaconsistent reasoning1 aHuang, Shasha1 aLi, Qingguo1 aHitzler, Pascal uhttp://dx.doi.org/10.1093/jigpal/jzs04301632nas a2200217 4500008004100000245009000041210006900131300001600200490000800216520093900224653002201163653002901185653002201214653002801236653001501264653001701279100002001296700002701316700002001343856005101363 2011 eng d00aLocal Closed World Reasoning with Description Logics under the Well-Founded Semantics0 aLocal Closed World Reasoning with Description Logics under the W a1528–15540 v1753 a

An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases [52], and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.

10aDescription Logic10aKnowledge representation10aLogic Programming10aNon-monotonic reasoning10aOntologies10aSemantic Web1 aKnorr, Matthias1 aAlferes, José, Júlio1 aHitzler, Pascal uhttp://dx.doi.org/10.1016/j.artint.2011.01.00701617nas 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=101540nas a2200193 4500008004100000245003000041210002800071300001200099490000600111520103000117653002401147653002101171653002901192653002101221653001701242100002001259700002401279856004301303 2010 eng d00aA Reasonable Semantic Web0 aReasonable Semantic Web a39–440 v13 a

The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as "shared inference," which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on entirely different methods like machine learning or natureinspired computing need to be investigated, while quality assurance needs to be done in terms of precision and recall values (as in information retrieval) and not necessarily in terms of soundness and completeness of the underlying algorithms.

10aAutomated Reasoning10aFormal Semantics10aKnowledge representation10aLinked Open Data10aSemantic Web1 aHitzler, Pascal1 avan Harmelen, Frank uhttp://dx.doi.org/10.3233/SW-2010-0010