@article {934, title = {Conversational Ontology Alignment with ChatGPT}, year = {2023}, author = {Sanaz Saki Norouzi and Mohammad Saeid Mahdavinejad and Pascal Hitzler} } @article {906, title = {The KnowWhereGraph Ontology}, year = {2023}, author = {Cogan Shimizu and Shirly Stephen and Kitty Currier and Pascal Hitzler and Rui Zhu and Krzysztof Janowicz and Mark Schildhauer and Mohammad Saeid Mahdavinejad and Abhilekha Dalal and Adrita Barua and Ling Cai and Gengchen Mai and Zhangyu Wang and Yuanyuan Tian and Sanaz Saki Norouzi and Zilong Liu and Meilin Shi and Colby K. Fisher} } @proceedings {958, title = {The KnowWhereGraph Ontology: A Showcase}, journal = {Proceedings of the Joint Ontology Workshops 2023 Episode IX: The Quebec Summer of Ontology co-located with the 13th International Conference on Formal Ontology in Information Systems (FOIS 2023),}, year = {2023}, month = {2023}, publisher = {CEUR Workshop Proceedings 3637, CEUR-WS.org 2023.}, address = {Sherbrooke, Quebec, Canada}, author = {Cogan Shimizu and Shirly Stephen and Rui Zhu and Kitty Currier and Mark Schildhauer and Dean Rehberger and Krzysztof Janowicz and Pascal Hitzler and Colby Fisher and Mohammad Saeid Mahdavinejad and Antrea Christou and Adrita Barua and Abhilehka Dalal and Sanaz Saki Norouzi and Yuanyuan Tian and Ling Cai and Gengchen Mai and Zhangyu Wang and Zilong Liu and Meilin Shi} } @conference {916, title = {The KnowWhereGraph Ontology: A Showcase}, year = {2023}, author = {Cogan Shimizu and Shirly Stephen and Rui Zhu and Kitty Currier and Mark Schildhauer and Dean Rehberger and Pascal Hitzler and Krzysztof Janowicz and Colby K. Fisher and Mohammad Saeid Mahdavinejad and Antrea Christou and Adrita Barua and Abhilekha Dalal and Sanaz Saki Norouzi and Zilong Liu and Meilin Shi and Ling Cai and Gengchen Mai and Zhangyu Wang and Yuanyuan Tian} } @conference {914, title = {KnowWhereGraph-Lite: A Perspective of the KnowWhereGraph}, booktitle = {KGSWC 2023}, year = {2023}, author = {Cogan Shimizu and Shirly Stephen and Antrea Christou and Kitty Currier and Mohammad Saeid Mahdavinejad and Sanaz Saki Norouzi and Abhilekha Dalal and Adrita Barua and Colby K. Fisher and Anthony D{\textquoteright}Onofrio and Thomas Thelen and Krzysztof Janowicz and Dean Rehberger and Mark Schildhauer 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} } @article {890, title = {Machine learning for internet of things data analysis: a survey}, journal = {Digital Communications and Networks}, volume = {4}, year = {2018}, pages = {161-175}, abstract = {

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.

}, keywords = {Internet of Things, Machine learning, Smart City, Smart data}, issn = {2352-8648}, doi = {https://doi.org/10.1016/j.dcan.2017.10.002}, url = {https://www.sciencedirect.com/science/article/pii/S235286481730247X}, author = {Mohammad Saeid Mahdavinejad and Mohammadreza Rezvan}, editor = {Mohammadamin Barekatain and Peyman Adibi and Payam Barnaghi and Amit P. Sheth} }