Welcome to the 5th SML workshop event!
The previous workshop at IJCAI-2017 was successfully held with a lot interesting disucssion during paper presentations and the panel. More: link)

* Final Program announced, Jan 14 2019.
* Accepted paper list is out! Congrats to the authors. Dec 24 2019.

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.


- 16:30
Welcome and Workshop Introduction
Paper Presentations
- 16:45
Using Argumentative Semantic Feature for Summarization
Sakala Venkata Krishna Rohit and Manish Shrivastava.
- 17:00
Constructing and Maintaining Corpus-driven Annotations
Felix Kuhr and Ralf Möller.
- 17:15
Question Answering for Suicide Risk Assessment using Reddit
Amanuel Alambo, Manas Gaur, Usha Lokala, Ugur Kursuncu, Krishnaprasad Thirunarayan, Amelie Gyrard, Randon S. Welton, Jyotishman Pathak, and Amit Sheth.
- 17:30
Towards Next Generation Knowledge Graphs for Disaster Management
Hemant Purohit, Rajaraman Kanagasabai, and Nikhil Deshpande.
- 18:00
SML Panel Discussion

Prospects of integrating Data Semantics & Background Knowledge into Machine Learning

Panelists: Dr. Ajay Bansal (ASU), Dr. Giovanni Pilato (CNR), Dr. Oshani Seneviratne (RPI), Dr. Banafsheh Rekabdar (SIU)

Dr. Ajay Bansal

Arizona State University, USA

Dr. Giovanni Pilato

Italian National Research Council (CNR), Italy

Dr. Oshani Seneviratne

Rensselaer Polytechnic Institute, USA

Dr. Banafsheh Rekabdar

Southern Illinois University, USA
- 18:15
Workshop Summary and Future Planning

Accepted Papers

  1. Felix Kuhr and Ralf Möller. Constructing and Maintaining Corpus-driven Annotations.
  2. Sakala Venkata Krishna Rohit and Manish Shrivastava. Using Argumentative Semantic Feature for Summarization.
  3. Amanuel Alambo, Manas Gaur, Usha Lokala, Ugur Kursuncu, Krishnaprasad Thirunarayan, Amelie Gyrard, Randon S. Welton, Jyotishman Pathak, and Amit Sheth. Question Answering for Suicide Risk Assessment using Reddit.
  4. Hemant Purohit, Rajaraman Kanagasabai, and Nikhil Deshpande. Towards Next Generation Knowledge Graphs for Disaster Management.

Call for Papers

Research papers are invited on all aspects of Semantic Machine Learning, including but not limited to the following:
  • Semantic Modelling for Machine Learning
  • Semantics and Deep Learning
  • Ontology-based Machine Learning
  • Using Linked Open Data and other Semantic Graphs for Machine Learning
  • Link prediction from large graphs
  • Machine Learning for Constructing and Maintaining Semantic Knowledge Bases
  • Design, Development & Reuse of Semantic Resources for Machine Learning
  • Semantic Reasoning and Inference in Machine Learning
  • Semantic Feature Engineering
  • Representational Semantics in Machine Learning
  • Semantics and Transfer Learning
  • Scalability in Semantic Machine Learning
  • Theory and Analysis of Semantic Machine Learning
  • Demos and Case Studies
  • Applications to Web, Social Media, Mobile, Language Technologies, Vision, Healthcare, etc.
Work-in-progress, industry applications/experiences and position papers are also welcome. Please submit your paper using the SML-2019 EasyChair (https://easychair.org/conferences/?conf=sml19) site. See below for author instructions.

Author Instructions

Manuscripts should be prepared according to the IEEE ICSC Author Guidelines (Go Here for Formatting Guideline, LaTex Styles and Word Template: https://www.ieee-icsc.org/submission). Submissions must be in English and provided as a PDF file. The length of manuscripts can be upto 6 pages (with additional 3 pages if required to show significant contribution, upon request). Work-in-process, Demo or Position papers may be shorter in length (3-4 pages) but, if accepted, are required to be expanded up to 6 pages based on reviews.

Each manuscript will be judged on its originality, significance, technical quality, relevance, and presentation and it will be peer reviewed. Authors are required to certify that their paper represents original work and is previously unpublished.

Submitting a paper to SML-2019 workshop means that if the paper is accepted, at least one author will register and attend the conference to present the paper.

Prospective authors are strongly encouraged to get in touch with the chairs and express their interest and seek clarifications on their queries early.

The accepted papers will be included in the ICSC-2019 conferece proceedings for the workshops.

Workshop Organization


Advisory Committee

Program Committee

  • Oshani Seneviratne, Rensselaer Polytechnic Institute, USA
  • Sujan Perera, IBM Research, USA
  • Vinh Nguyen, National Library of Medicine - NIH, USA
  • Shreyansh Bhatt, Kno.e.sis - Wright State University, USA
  • Prakruthi Karuna, George Mason University, USA


Important Dates

Paper Submission: Dec 10 (extended)
Author Notification: Dec 18 (extended)
Camera ready: Dec 21
Workshop Date: Feb 1