@proceedings {770, title = {Completion Reasoning Emulation for the Description Logic EL+}, journal = {Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice}, volume = {2600}, year = {2020}, month = {03/2020}, publisher = {CEUR-WS.org}, address = {Stanford University, Palo Alto, California, USA}, abstract = {

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{\textquoteright}s and LSTM{\textquoteright}s predictions on corrupt data with correct answers.

}, keywords = {Deep Learning, Description Logic, EL+, LSTM, NeSy, Reasoning}, url = {http://ceur-ws.org/Vol-2600/paper5.pdf}, author = {Aaron Eberhart and Monireh Ebrahimi and Lu Zhou and Cogan Shimizu and Pascal Hitzler} } @article {775, title = {Multimodal mental health analysis in social media}, journal = {PLoS ONE}, year = {2020}, abstract = {

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}

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.

}, keywords = {Explainable Machine Learning, Hypothesis Testing, National Language Processing, Prediction, Regression}, url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248\&type=printable}, author = {Amir Hossein Yazdavar and Mohammad Saeid Mahdavinejad and Goonmeet Baja and William Romine and Amit Sheth and Amir Hassan Monadjemi and Krishnaprasad Thirunarayan and John M. Meddar and Annie Myers and Jyotishman Pathak and Pascal Hitzler} } @conference {747, title = {A Method for Automatically Generating Schema Diagrams for OWL Ontologies}, booktitle = {1st Iberoamerican Knowledge Graph and Semantic Web Conference (KGSWC)}, year = {2019}, month = {06/2019}, publisher = {Springer}, organization = {Springer}, chapter = {149-161}, address = {Villa Clara, Cuba}, abstract = {

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.

}, keywords = {design patterns, evaluation, implementation, ontology, schema diagrams, visualization}, author = {Cogan Shimizu and Aaron Eberhart and Nazifa Karima and Quinn Hirt and Adila Krisnadhi and Pascal Hitzler} } @conference {10, title = {DistEL: A Distributed EL+ Ontology Classifier}, booktitle = {Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, co-located with the International Semantic Web Conference (ISWC 2013)}, volume = {1046}, year = {2013}, month = {10/2013}, pages = {17-32}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Sydney, Australia}, abstract = {OWL 2 EL ontologies are used to model and reason over data from diverse domains such as biomedicine, geography and road traffic. Data in these domains is increasing at a rate quicker than the increase in main memory and computation power of a single machine. Recent efforts in OWL reasoning algorithms lead to the decrease in classification time from several hours to a few seconds even for large ontologies like SNOMED CT. This is especially true for ontologies in the description logic EL+ (a fragment of the OWL 2 EL profile). Reasoners such as Pellet, Hermit, ELK etc. make an assumption that the ontology would fit in the main memory, which is unreasonable given projected increase in data volumes. Increase in the data volume also necessitates an increase in the computation power. This lead us to the use of a distributed system, so that memory and computation requirements can be spread across machines. We present a distributed system for the classification of EL+ ontologies along with some results on its scalability and performance.}, keywords = {Classification, DistEL, Distributed Reasoning, EL+, OWL, Scalability}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti}, editor = {Thorsten Liebig and Achille Fokoue} } @conference {128, title = {Towards an Efficient Algorithm to Reason over Description Logics Extended with Nominal Schemas}, booktitle = {Web Reasoning and Rule Systems - 7th International Conference, {RR} 2013, Mannheim, Germany, July 27-29, 2013. Proceedings}, year = {2013}, pages = {65{\textendash}79}, abstract = {

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.

}, keywords = {description logics, EL++, Nominal Schemas}, doi = {10.1007/978-3-642-39666-3_6}, url = {http://dx.doi.org/10.1007/978-3-642-39666-3_6}, author = {David Carral and Cong Wang and Pascal Hitzler} }