00639nas a2200157 4500008004100000245010100041210006900142100002000211700002400231700001900255700002100274700002000295700001800315700002500333856012300358 2023 eng d00aOpenness and Transparency in Academic Publishing: A Decade of Data from the Semantic Web Journal0 aOpenness and Transparency in Academic Publishing A Decade of Dat1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aShimizu, Cogan1 aDalal, Abhilekha1 aEberhart, Aaron1 aEells, Andrew1 aNorouzi, Sanaz, Saki uhttps://daselab.cs.ksu.edu/publications/openness-and-transparency-academic-publishing-decade-data-semantic-web-journal00493nas a2200157 4500008004100000245005700041210005600098260001200154490000600166100002000172700002000192700002200212700002700234700001300261856006100274 2022 eng d00aNeuro-symbolic approaches in artificial intelligence0 aNeurosymbolic approaches in artificial intelligence c06/20220 v91 aHitzler, Pascal1 aEberhart, Aaron1 aEbrahimi, Monireh1 aSarker, Md Kamruzzaman1 aZhou, Lu uhttps://academic.oup.com/nsr/article/9/6/nwac035/654246000497nas a2200145 4500008004100000245005900041210005700100260000900157490000700166100002700173700001300200700002000213700002000233856009800253 2022 eng d00aNeuro-Symbolic Artificial Intelligence: Current Trends0 aNeuroSymbolic Artificial Intelligence Current Trends c20210 v341 aSarker, Md Kamruzzaman1 aZhou, Lu1 aEberhart, Aaron1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/neuro-symbolic-artificial-intelligence-current-trends00451nas a2200109 4500008004100000245007300041210006600114100002200180700002000202700002000222856009900242 2021 eng d00aOn the Capabilities of Pointer Networks for Deep Deductive Reasoning0 aCapabilities of Pointer Networks for Deep Deductive Reasoning1 aEbrahimi, Monireh1 aEberhart, Aaron1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/capabilities-pointer-networks-deep-deductive-reasoning01804nas a2200145 4500008004100000245004700041210004700088520133500135100002001470700001901490700002301509700002701532700002001559856007901579 2021 eng d00aExpressibility of OWL Axioms with Patterns0 aExpressibility of OWL Axioms with Patterns3 a
The high expressivity of the Web Ontology Language (OWL) makes it possible to describe complex relationships between classes, roles, and individuals in an ontology. However, this high expressivity can be an obstacle to correct usage and wide adoption. Past attempts to ameliorate this have included the development of specific, presumably human-friendly syntaxes, such as the Manchester syntax or graphical interfaces for OWL axioms, albeit with limited success. If modelers want to develop suitable OWL axioms it is important to make this as easy as possible. In this paper, we adopt an idea from the Protégé plug-in, OWLAx, which provides a simple, clickable interface to automatically input axioms of a limited number of types by following simple axiom patterns. In particular, each of these axiom patterns contains at most three classes or roles. We hypothesize that most of the axioms in existing ontologies could be expressed semantically in terms of simple patterns like these, which would mean that more complex patterns can be used very sparingly. Our findings, based on an analysis of 518 ontologies from six public ontology repositories, confirm this hypothesis: Over 90% of class axioms in the average ontology are indeed expressible with our simple patterns. We provide a detailed analysis of our findings.
1 aEberhart, Aaron1 aShimizu, Cogan1 aChowdhury, Sulogna1 aSarker, Md Kamruzzaman1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/expressibility-owl-axioms-patterns00650nas a2200181 4500008004100000245007600041210006900117260000900186100002200195700002700217700002200244700001400266700002000280700001700300700001900317700002000336856011200356 2021 eng d00aNeuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment0 aNeuroSymbolic Deductive Reasoning for CrossKnowledge Graph Entai bAAAI1 aEbrahimi, Monireh1 aSarker, Md Kamruzzaman1 aBianchi, Federico1 aXie, Ning1 aEberhart, Aaron1 aDoran, Derek1 aKim, HyeongSik1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/neuro-symbolic-deductive-reasoning-cross-knowledge-graph-entailment00850nas a2200169 4500008004100000245003700041210003700078260001400115300001000129520037000139100002000509700001800529700002000547700001700567700002300584856007300607 2021 eng d00aSeed Patterns for Modeling Trees0 aSeed Patterns for Modeling Trees bIOS Press a48-673 aTrees – i.e., the type of data structure known under this name – are central to many aspects of knowledge organization. We investigate some central design choices concerning the ontological modeling of such trees. In particular, we consider the limits of what is expressible in the Web Ontology Language and provide a reusable ontology design pattern for trees.1 aEberhart, Aaron1 aCarral, David1 aHitzler, Pascal1 aLapp, Hilmar1 aRudolph, Sebastian uhttps://daselab.cs.ksu.edu/publications/seed-patterns-modeling-trees01539nas a2200205 4500008004100000245004500041210004500086250000700131520093100138100001801069700002001087700002001107700002501127700001901152700002001171700002001191700002001211700001701231856008501248 2021 eng d00aToward Undifferentiated Cognitive Models0 aToward Undifferentiated Cognitive Models a193 aAutonomous systems are a new frontier for pushing sociotechnical advancement. Such systems will eventually become pervasive, involved in everything from manufacturing, healthcare, defense, and even research itself. However, proliferation is stifled by the high development costs and the resulting inflexibility of the produced systems. The current time needed to create and integrate state of the art autonomous systems that operate as team members in complex situations is a 3-15 year development period, often requiring humans to adapt to limitations in the resulting systems. A new research thrust in interactive task learning (ITL) has begun, calling for natural human-autonomy interaction to facilitate system flexibility and minimize users’ complexity in providing autonomous systems with new tasks. We discuss the development of an undifferentiated agent with a modular framework as a method of approaching that goal.1 aKupitz, Colin1 aEberhart, Aaron1 aSchmidt, Daniel1 aStevens, Christopher1 aShimizu, Cogan1 aHitzler, Pascal1 aSalvucci, Dario1 aMaruyama, Benji1 aMyers, Chris uhttps://daselab.cs.ksu.edu/publications/toward-undifferentiated-cognitive-models00484nas a2200133 4500008004100000245005800041210005800099260001400157100001900171700002200190700002000212700002400232856009400256 2021 eng d00aTowards a Modular Ontology for Space Weather Research0 aTowards a Modular Ontology for Space Weather Research bIOS Press1 aShimizu, Cogan1 aMcGranaghan, Ryan1 aEberhart, Aaron1 aKellerman, Adam, C. uhttps://daselab.cs.ksu.edu/publications/towards-modular-ontology-space-weather-research-000490nas a2200121 4500008004100000245007000041210006800111100002200179700002000201700002200221700002000243856010500263 2021 eng d00aTowards Bridging the Neuro-Symbolic Gap: Deep Deductive Reasoners0 aTowards Bridging the NeuroSymbolic Gap Deep Deductive Reasoners1 aEbrahimi, Monireh1 aEberhart, Aaron1 aBianchi, Federico1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/towards-bridging-neuro-symbolic-gap-deep-deductive-reasoners01414nas 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 aWe 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.pdf01281nas a2200157 4500008004100000245004400041210004200085520078800127100002000915700001900935700002500954700002000979700002700999700001901026856007801045 2020 eng d00aA Domain Ontology for Task Instructions0 aDomain Ontology for Task Instructions3 a Knowledge graphs and ontologies represent information in a variety of different applications. One use case, the Intelligence, Surveillance, & Reconnaissance: Mutli-Attribute Task Battery (ISR-MATB), comes from Cognitive Science, where researchers use interdisciplinary methods to understand the mind and cognition. The ISR-MATB is a set of tasks that a cognitive or human agent perform which test visual, auditory, and memory capabilities. An ontology can represent a cognitive agent’s background knowledge of the task it was instructed to perform and act as an interchange format between different Cognitive Agent tasks similar to ISR-MATB. We present several modular patterns for representing ISR-MATB task instructions, as well as a unified diagram that links them together.1 aEberhart, Aaron1 aShimizu, Cogan1 aStevens, Christopher1 aHitzler, Pascal1 aMyers, Christopher, W.1 aMaruyam, Benji uhttps://daselab.cs.ksu.edu/publications/domain-ontology-task-instructions01070nas a2200121 4500008003900000245002900039210002700068490000900095520074100104100002000845700002000865856006300885 2020 d00aA Functional API for OWL0 aFunctional API for OWL0 v27213 aWe present (f OWL), a minimalistic, functional programming style ontology editor that is based directly on the OWL 2 Structural Specification. (f OWL) is written from scratch, entirely in Clojure, having no other dependencies. Ontologies in (f OWL) are implemented as standalone and homogeneous data structures, which means that the same exact functions written for single axioms or expressions often work identically on any part of an ontology, even the entire ontology itself. The lazy functional style of Clojure also allows for intuitive and simple ontology creation and modification with a minimal memory footprint. All of this is possible without ever needing to use a single class, except of course in the Ontologies one creates!1 aEberhart, Aaron1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/functional-api-owl00542nas a2200181 4500008004100000245003500041210003100076100001900107700002000126700002000146700001600166700002500182700002700207700002000234700001800254700002000272856006800292 2020 eng d00aAn Ontology of Instruction 1.00 aOntology of Instruction 101 aShimizu, Cogan1 aHitzler, Pascal1 aEberhart, Aaron1 aHirt, Quinn1 aStevens, Christopher1 aMyers, Christopher, W.1 aMaruyama, Benji1 aKupitz, Colin1 aSalvucci, Dario uhttps://daselab.cs.ksu.edu/publications/ontology-instruction-1000432nas a2200133 4500008004100000245005800041210005800099490001900157100001900176700002200195700002000217700002400237856003700261 2020 eng d00aTowards a Modular Ontology for Space Weather Research0 aTowards a Modular Ontology for Space Weather Research0 vabs/2009.122851 aShimizu, Cogan1 aMcGranaghan, Ryan1 aEberhart, Aaron1 aKellerman, Adam, C. uhttps://arxiv.org/abs/2009.1228501534nas 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 aInterest 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-ontologies00883nas a2200229 4500008004100000020002200041245004000063210003900103260003900142300001200181490001000193520026000203653000800463653002200471653000700493653002200500653001900522100002000541700002300561700002000584856004900604 2018 eng d a978-3-319-98191-800aPseudo-Random ALC Syntax Generation0 aPseudoRandom ALC Syntax Generation aHeraklion, Crete, GreecebSpringer a19–220 v111553 aWe discuss a tool capable of rapidly generating pseudo-random syntactically valid ALC expression trees. The program is meant to allow a researcher to create large sets of independently valid expressions with a minimum of personal bias for experimentation.10aALC10aDescription Logic10aDL10arandom generation10asynthetic data1 aEberhart, Aaron1 aCheatham, Michelle1 aHitzler, Pascal uhttps://doi.org/10.1007/978-3-319-98192-5\_4