EDITORS: 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. The interest has also been fueled 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 using ontologies. The fourth IJCAI workshop on Semantic Machine Learning seeks to 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. The workshop programme includes i) invited keynote from Dr. Amy Shi-Nash, Commonwealth Bank, Australia, ii) 4 paper sessions with oral presentations from international research groups, and iii) an invited panel on Value Aid in incorporating Structured, Semantic Knowledge Bases into Machine Leaning Approaches, with renowned research leaders from Academia and Industry as panelists. We wish to express our deep appreciation to the programme committee members and the additional reviewers who shared their valuable time and expertise in support of the SML17 review process. Special thanks to our advisory committee members Prof. Amit Sheth, Prof. Fausto Giunchiglia, and Prof. Timos Sellis for their constant encouragement and guidance in the organization. We also wish to express our gratitude to our supporting organizations: The Institute for Infocomm Research (A*STAR), Swinburne University and George Mason University. Date: 20th August, 2017, Sunday
Address: 445 Swanston Street, Melbourne, Victoria, 3000 • Evan Dennison Livelo, Andrea Nicole Ver, Jedrick Chua, John Paul Yao and Charibeth Cheng. A Hybrid Agent for Automatically Determining and
Extracting the 5Ws of Filipino News Articles. • Heng Chen, Yongjuan Zhang, Chunhong Lin, Liwen Zhang and Tao Chen.
Construction of Viral Hepatitis Bilingual Bibliographic Database, Mining of Viral Hepatitis Related Protein Text and Integrating with Uniprot Protein
Database. • Yang Gao, Linjing Wei, Heyan Huang and Qian Liu. Topical Sentence
Embedding for Query Focused Document Summarization. • Luis Palacios, Yue Ma, Gaëlle Lortal, Claire Laudy and Chantal Reynaud.
Data Driven Concept Refinement to Support Avionics Maintenance. • Andreea Salinca. Convolutional Neural Networks for Sentiment Classification on Business Reviews. • Ritesh Ratti, Himanshu Kapoor, Shikhar Sharma and Anshul Solanki.
Semantic extraction of Named Entities from Bank Wire text.
== LUNCH BREAK ==
• Abdullah Alharbi, Yuefeng Li and Yue Xu. Enhancing Topical Word
Semantic for Relevance Feature Selection. 14:30 15:30 15:30 16:10 16:10 16:40 16:40 17:40
Speaker: Amy Shi-Nash, PhD
Head of Data Science, Commonwealth Bank, Australia Title: How can Machine Learning/AI help Banks and Customers
Topic: Value Aid in incorporating Structured, Semantic Knowledge Bases into Machine Leaning Approaches
Panelists: • Prof. Dimitrios Georgakopoulos, Swinburne University of Technology,
Australia • A/Prof. Xiuzhen (Jenny) Zhang, RMIT University, Australia • Dr. Truyen Tran, Lecturer, Deakin University, Australia • Dr. Yuan-Fang Li, Senior Lecturer, Monash University, Australia • Prof. Arkady Zaslavsky, CSIRO
== COFFEE BREAK ==
• Yang SHAO. Several simple neural networks for evaluating semantic textual similarity. • Fenglong Ma, Radha Chitta, Saurabh Kataria, Jing Zhou, Palghat Ramesh, Tong Sun and Jing Gao. Long-Term Memory Networks for Question Answering.
== CONCLUDING REMARKS ==
How can Machine Learning / AI help Banks and Customers Amy is an executive leader with a proven track record of creating value and competitive advantage through data-driven culture and innovation. As the Head of Data Science at Commonwealth Bank, she is responsible for driving strategic data science capability, enable business transformation and differentiated customer experience. Prior to CBA, Amy was the founding member and Chief Data Science Officer of DataSpark, Singtel’s data analytics spin-off. Responsible for driving data-led innovation and creating new revenue steams by combining telco data with advanced analytics and big data technology. Amy is a Science Board Member of i-Com and since 2013 is Industry Track Program Committee Member of ACM KDD. She is a frequent public speaker, a co-inventor and co-author of multiple Patents and Publications. Amy holds a Ph.D in data mining, a Master in AI and an MBA.