01414nas 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 a<p>We 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.</p>
10aDeep Learning10aDescription Logic10aEL+10aLSTM10aNeSy10aReasoning1 aEberhart, Aaron1 aEbrahimi, Monireh1 aZhou, Lu1 aShimizu, Cogan1 aHitzler, Pascal  uhttp://ceur-ws.org/Vol-2600/paper5.pdf01534nas 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 a<p>Interest 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.</p>
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