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SML 2019 : ICSC-2019 Semantic Machine Learning Workshop

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Link: https://ist.gmu.edu/~hpurohit/events/sml19/
 
When Jan 30, 2019 - Feb 1, 2019
Where California, USA
Submission Deadline Dec 7, 2018
Notification Due Dec 15, 2018
Final Version Due Dec 21, 2018
Categories    semantic computing   semantic web   machine learning   artificial intelligence
 

Call For Papers

5th International Workshop on Semantic Machine Learning (SML-2019)

Co-located with IEEE ICSC-2019

Jan 30 – Feb 1, 2019

Newport Beach, California, USA


Website: http://ist.gmu.edu/~hpurohit/events/sml19

Submissions due: Dec 07, 2018 (extended)


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Aim and Scope

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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), a field that formalizes and investigates computational learning, 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 from diverse knowledge bases.

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 various layers of ML. For instance, deep learning can be considered as an approach to model representational semantics in vector space using deep neural architectures. An example of an approach using domain semantics for ML include the ontology-based ML methods, often investigated by the Data Mining researchers and bioinformaticians, and also by the Semantic Web and Semantic Computing community. The latter community, in particular, has made significant progress recently in establishing widely-accepted semantic technologies and standards that not only can facilitate greater industry adoption but can also enable incorporation of reasoning and inference in ML. Furthermore, advanced ML research can assist in addressing the limitations of background knowledge bases, including: a.) quality of structured knowledge and evolution, b.) sparsity of knowledge base attributes, and c.) heterogeneity of information representations across knowledge bases.

This workshop will build upon the lessons from previous successful workshops for Semantic Machine Learning, including the last one at IJCAI 2017. This year’s focus is to generate interest towards making Machine Learning knowledgeable in terms of incorporating structured knowledge from various application domains and enhance the learning process at different stages of information processing.

The event will bring together researchers and practitioners working on different aspects of semantic ML, to share their experiences, exchange new ideas for applying semantic ML in various application domains as well as to identify key emerging topics for future directions.



Topics

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Research papers are invited on all aspects of Semantic Machine Learning, including but not limited to the following:

Semantic Modelling for ML
Semantics and Deep Learning
Ontology-based ML
Using Linked Open Data and other Semantic Graphs for ML
Link prediction from large graphs
ML for Constructing and Maintaining Semantic Knowledge Bases
Design, Development & Reuse of Semantic Resources for ML
Semantic Reasoning and Inference in ML
Semantic Feature Engineering
Representational Semantics in ML
Semantics and Transfer Learning
Dynamic Knowledge graph
Scalability in Semantic ML
Theory and Analysis of Semantic ML
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 site: https://easychair.org/conferences/?conf=sml19



Author Instructions:

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Manuscripts should be prepared according to the IEEE Author Guidelines (Check IEEE ICSC-19 Formatting Guidelines for LaTex Styles and Word Template: http://ieee-icsc.org). Submissions must be in English and provided as a PDF file. The length of manuscripts can be up to 6 pages. Work-in-process, Demo or Position papers may be shorter in length (2-4 pages) but, if accepted, are required to be expanded to 6 pages based on reviews.

For more details: http://ist.gmu.edu/~hpurohit/events/sml19

Each manuscript will be judged on its originality, significance, technical quality, relevance, and presentation and 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 implies 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.



Important Dates

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Paper Submission: Dec. 07, 2018 (extended)

Author Notification: Dec. 18, 2018

Camera ready: Dec. 21, 2018

(All deadlines are 11:59PM Hawaii time.)



Workshop Organization

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Chairs:

Rajaraman Kanagasabai, Institute for Infocomm Research (I2R), Singapore

Hemant Purohit, George Mason University, USA

Ahsan Morshed, Swinburne University of Technology, Melbourne, Australia



Advisory Committee:

Prof. Amit Sheth, Wright State University, USA

Prof. Fausto Giunchiglia, University of Trento, Trento, Italy

Prof. Timos Sellis, Swinburne University of Technology, Australia

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