Welcome to the 7th SML workshop event!
The earlier workshops at ICSC and IJCAI-2017 & IJCAI-2016 were successfully held with a lot interesting disucssion during paper presentations, keynotes, and panels.


NEWS:
* Final Program announced, Jan 18 2021.
* Keynote Speaker announced, Jan 10 2021.

Aim and Scope

Learning is an important attribute of an AI system that enables it to adapt to new circumstances and to detect and extrapolate patterns. Machine Learning (ML) has seen a tremendous growth during the last few years due in part to the successful commercial deployments in products developed by major companies such as Google, Apple and Facebook. The interest has also being fuelled by the recent research breakthroughs brought about by deep learning. ML is however not a silver bullet as it is made out to be, and currently has several limitations in complex real-life situations. Some of these limitations include: i) many ML algorithms require large number of training data that are often too expensive to obtain in real-life, ii) significant effort is often required to do feature engineering to achieve high performance, iii) many ML methods are limited in their ability to exploit background knowledge, and iv) lack of a seamless way to integrate and use heterogeneous data.

Approaches that formalize data, functional and domain semantics, can tremendously aid addressing some of these limitations. The so-called semantic approaches have been increasingly investigated by various research communities and applied at different layers of ML, e.g. modeling representational semantics in vector space using deep learning architectures, and modeling domain semantics in ontology-based ML. This is complemented by the significant body of technologies and standards put together by the Semantic Web community that not only can facilitate greater industry adoption but can also enable incorporation of reasoning and inference in ML.

This workshop will bring together researchers and practitioners from all these communities working on different aspects of semantic ML, to share their experiences, exchange new ideas as well as to identify key emerging topics and define future directions.

Keynote Speaker

KeyNote
Title: Semantics of the Black-Box: Towards Knowledge-infused Learning
Speaker: Dr. Amit Sheth, AI Institute, University of South Carolina, USA
Abstract:
The recent series of innovations in deep learning have shown enormous potential to impact individuals and society, both positively and negatively. The deep learning models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of deep learning models and their over-reliance on massive amounts of data condensed into labels and dense representations pose challenges for the system’s interpretability and explainability. Furthermore, deep learning methods have not yet been proven in their ability to effectively utilize relevant domain knowledge and experience critical to human understanding. This aspect is missing in early data-focused approaches and necessitated knowledge-infused learning and other strategies to incorporate computational knowledge. Rapid advances in our ability to create and reuse structured knowledge as knowledge graphs make this task viable. In this talk, we will outline how knowledge, provided as a knowledge graph, is incorporated into the deep learning methods using knowledge-infused learning. We then discuss how this makes a fundamental difference in the interpretability and explainability of current approaches and illustrate it with examples relevant to a few domains. Bio: Prof. Amit Sheth is an Educator, Researcher, and Entrepreneur. He is the founding director university-wide AI Institute at the University of South He is a Fellow of IEEE, AAAI, AAAS and ACM. He is among the highly cited computer scientists worldwide. He has (co-)founded four companies, three of them by licensing his university research outcomes, including the first Semantic Search company in 1999 that pioneered technology similar to what is found today in Google Semantic Search and Knowledge Graph. He is particularly proud of the success of his 30 former Ph.D. advisees and >10 postdocs in academia, industry research, and entrepreneurs.


Final Schedule

Date: Jan 29, 2021 (Timezone: Pacific Standard Time)
13:45 - 14:30 Workshop Introduction and Keynote Session

  • Semantics of the Black-Box: Towards Knowledge-infused Learning
    Dr. Amit Sheth, AI Institute, University of South Carolina, USA
14:30 - 16:00 Invited Talks Session

  • 1. Occupational Health 2.0 - leveraging semantics and machine learning
    Dr. Chris Baker, University of New Brunswick, Canada

  • 2. Machine Learning Challenges: The Journey from Modelling to Real-World Decision-Making
    Dr. Shonali Krishnaswamy, Monash University, Australia

  • 3. Symbolic Representations and Reinforcement Learning
    Dr. Ravindran Balaraman, IIT Madras, India
16:00 - 17:00 Panel Session

  • Theme: Future of Semantic Machine Learning
    Panelists:
    • - Dr. Chris Baker, University of New Brunswick
    • - Dr. Ravindran Balaraman, IIT Madras;
    • - Dr. Shonali Krishnaswamy, Monash University;
    • - Dr. Amit Sheth, AI Institute, University of South Carolina

Important Dates

Workshop Date: Jan 29 2021

Workshop Organisation


Chairs