SML 2016 - 3rd Workshop on Semantic Machine Learning (SML 2016)

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.
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
Design, Development & Reuse of Semantic Resources for ML
Semantic Reasoning and Inference in Machine Learning
Semantic Feature Engineering
Representational Semantics in ML
Semantics and Transfer Learning
Scalability in Semantic ML
Theory and Analysis of Semantic ML
Demos and Case Studies
Applications to Web, Social Media, Mobile, NLP, Vision, Healthcare, etc.
Work-in-progress, industry applications/experiences and position papers are also welcome. Please submit your paper using the SML 2016 EasyChair site. See below for author instructions.