%0 Conference Proceedings %B Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice %D 2020 %T Completion Reasoning Emulation for the Description Logic EL+ %A Aaron Eberhart %A Monireh Ebrahimi %A Lu Zhou %A Cogan Shimizu %A Pascal Hitzler %K Deep Learning %K Description Logic %K EL+ %K LSTM %K NeSy %K Reasoning %X

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.

%B Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice %I CEUR-WS.org %C Stanford University, Palo Alto, California, USA %V 2600 %8 03/2020 %G eng %U http://ceur-ws.org/Vol-2600/paper5.pdf %0 Journal Article %J PLoS ONE %D 2020 %T Multimodal mental health analysis in social media %A Amir Hossein Yazdavar %A Mohammad Saeid Mahdavinejad %A Goonmeet Baja %A William Romine %A Amit Sheth %A Amir Hassan Monadjemi %A Krishnaprasad Thirunarayan %A John M. Meddar %A Annie Myers %A Jyotishman Pathak %A Pascal Hitzler %K Explainable Machine Learning %K Hypothesis Testing %K National Language Processing %K Prediction %K Regression %X

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Depression is a major public health concern in the U.S. and globally. While successful early

identification and treatment can lead to many positive health and behavioral outcomes,

depression, remains undiagnosed, untreated or undertreated due to several reasons,

including denial of the illness as well as cultural and social stigma. With the ubiquity of social

media platforms, millions of people are now sharing their online persona by expressing their

thoughts, moods, emotions, and even their daily struggles with mental health on social

media. Unlike traditional observational cohort studies conducted through questionnaires

and self-reported surveys, we explore the reliable detection of depressive symptoms from

tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)

data to discern depressive behaviors using a wide variety of features including individuallevel

demographics. By developing a multimodal framework and employing statistical techniques

to fuse heterogeneous sets of features obtained through the processing of visual,

textual, and user interaction data, we significantly enhance the current state-of-the-art

approaches for identifying depressed individuals on Twitter (improving the average F1-

Score by 5 percent) as well as facilitate demographic inferences from social media. Besides

providing insights into the relationship between demographics and mental health, our

research assists in the design of a new breed of demographic-aware health interventions.

%B PLoS ONE %G eng %U https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&type=printable %0 Conference Paper %B Twelfth International Workshop on Neural-Symbolic Learning and Reasoning %D 2017 %T Propositional rule extraction from neural networks under background knowledge %A Maryam Labaf %E Pascal Hitzler %Y Anthony B. Evans %K Background knowledge %K Neural Network %K Propositional Logic %K Rule Extraction %B Twelfth International Workshop on Neural-Symbolic Learning and Reasoning %8 07/2017 %G eng %0 Journal Article %J Logic Journal of the IGPL %D 2013 %T Reasoning with Inconsistencies in Hybrid MKNF Knowledge Bases %A Shasha Huang %A Qingguo Li %A Pascal Hitzler %K Data complexity %K Description logics and rules %K Knowledge representation %K Non-monotonic reasoning %K Paraconsistent reasoning %X This 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. %B Logic Journal of the IGPL %V 21 %P 263–290 %G eng %U http://dx.doi.org/10.1093/jigpal/jzs043 %R 10.1093/jigpal/jzs043 %0 Conference Paper %B Web Reasoning and Rule Systems - 7th International Conference, {RR} 2013, Mannheim, Germany, July 27-29, 2013. Proceedings %D 2013 %T Towards an Efficient Algorithm to Reason over Description Logics Extended with Nominal Schemas %A David Carral %A Cong Wang %A Pascal Hitzler %K description logics %K EL++ %K Nominal Schemas %X

Extending description logics with so-called nominal schemas has been shown to be a major step towards integrating description logics with rules paradigms. However, establishing efficient algorithms for reasoning with nominal schemas has so far been a challenge. In this paper, we present an algorithm to reason with the description logic fragment ELROVn, a fragment that extends EL++ with nominal schemas. We also report on an implementation and experimental evaluation of the algorithm, which shows that our approach is indeed rather efficient.

%B Web Reasoning and Rule Systems - 7th International Conference, {RR} 2013, Mannheim, Germany, July 27-29, 2013. Proceedings %P 65–79 %G eng %U http://dx.doi.org/10.1007/978-3-642-39666-3_6 %R 10.1007/978-3-642-39666-3_6 %0 Journal Article %J Artificial Intelligence %D 2011 %T Local Closed World Reasoning with Description Logics under the Well-Founded Semantics %A Matthias Knorr %A José Júlio Alferes %A Pascal Hitzler %K Description Logic %K Knowledge representation %K Logic Programming %K Non-monotonic reasoning %K Ontologies %K Semantic Web %X

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.

%B Artificial Intelligence %V 175 %P 1528–1554 %G eng %U http://dx.doi.org/10.1016/j.artint.2011.01.007 %N 9-10 %R 10.1016/j.artint.2011.01.007 %0 Conference Proceedings %B Learning paradigms in dynamic environments %D 2010 %T 10302 Abstracts Collection - Learning paradigms in dynamic environments %E Barbara Hammer %E Pascal Hitzler %E Wolfgang Maass %E Marc Toussaint %K Autonomous learning %K Dynamic systems %K Neural-symbolic integration %K Neurobiology %K Recurrent neural networks %K Speech processing %B Learning paradigms in dynamic environments %I Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany %C Dagstuhl, Germany %G eng %U http://drops.dagstuhl.de/opus/volltexte/2010/2804