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-induction02037nas 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-2012-006601632nas 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.2011.01.00701837nas a2200205 4500008004100000245007000041210006900111300001400180490000700194520119100201653002301392653003201415653000801447653002501455653001701480653003201497100001801529700002001547856006401567 2010 eng d00aConcept learning in description logics using refinement operators0 aConcept learning in description logics using refinement operator a203–2500 v783 a

With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.

10adescription logics10aInductive logic programming10aOWL10arefinement operators10aSemantic Web10aStructured Machine Learning1 aLehmann, Jens1 aHitzler, Pascal uhttp://springerlink.metapress.com/content/c040n45u15qrnu44/01540nas 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