=Paper=
{{Paper
|id=Vol-1986/preface
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1986/SML17_preface.pdf
|volume=Vol-1986
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==None==
https://ceur-ws.org/Vol-1986/SML17_preface.pdf
IJCAI 2017 Workshop on Semantic Machine Learning
August 20, 2017
Melbourne, Australia
The 4th International Workshop in
Semantic Machine Learning (#SML) Workshop Series
EDITORS:
Rajaraman Kanagasabai, Institute for Infocomm Research, Singapore
Ahsan Morshed, Swinburne University, Melbourne, Australia
Hemant Purohit, George Mason University, USA
Welcome to #SML17
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.
Rajaraman Kanagasabai, Ahsan Morshed, Hemant Purohit
Chairs, #SML17
SML17 Organisation
Chairs:
Rajaraman Kanagasabai, Institute for Infocomm Research, Singapore
Ahsan Morshed, Swinburne University, Melbourne, Australia
Hemant Purohit, George Mason University, USA
Advisory Committee:
Prof. Fausto Giunchiglia, University of Trento, Trento, Italy
Prof. Amit Sheth, Kno.e.sis Center, Wright State University, Dayton, USA
Prof. Timos Sellis, Swinburne University of Technology, Australia
Programme Committee:
Kim Jung Jae, Institute for Infocomm Research, Singapore
Prem Jayaraman, Swinburne University of Technology, Australia
Kewen Lio, Swinburne University of Technology, Australia
Heiko Mueller, New York University, USA
Oshani Seneviratne, Oracle, USA
Md. Sumon Shahriar, Department of Health, Australia
Saeedeh Shekarpour, Kno.e.sis, Wright State University, USA
Sanjaya Wijeratne, Kno.e.sis, Wright State University, USA
Programme
Date: 20th August, 2017, Sunday
Time: 8.30 - 18.00
Venue: RMIT University Building 80 (also known as SAB or Swanston
Academic Building)
Address: 445 Swanston Street, Melbourne, Victoria, 3000
Paper Session I (3 papers: each 25 min + 5 min Q&A)
• 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.
08:30 -
• Heng Chen, Yongjuan Zhang, Chunhong Lin, Liwen Zhang and Tao Chen.
10:00
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.
10:00 -
== COFFEE BREAK ==
10:30
Paper Session II (3 papers: each 25 min + 5 min Q&A)
• Luis Palacios, Yue Ma, Gaëlle Lortal, Claire Laudy and Chantal Reynaud.
10:30 - Data Driven Concept Refinement to Support Avionics Maintenance.
12:30 • 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.
12:30 -
== LUNCH BREAK ==
14:00
Paper Session III (1 paper: 25 min + 5 min Q&A)
14:00 -
14:30 • Abdullah Alharbi, Yuefeng Li and Yue Xu. Enhancing Topical Word
Semantic for Relevance Feature Selection.
Keynote
14:30 - Speaker: Amy Shi-Nash, PhD
15:30 Head of Data Science, Commonwealth Bank, Australia
Title: How can Machine Learning/AI help Banks and Customers
Panel Discussion
Topic: Value Aid in incorporating Structured, Semantic Knowledge Bases into
Machine Leaning Approaches
15:30 - Panelists:
16:10 • 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
16:10 -
== COFFEE BREAK ==
16:40
Paper Session IV (2 papers: each 25 min + 5 min Q&A)
• Yang SHAO. Several simple neural networks for evaluating semantic textual
16:40 -
similarity.
17:40
• Fenglong Ma, Radha Chitta, Saurabh Kataria, Jing Zhou, Palghat Ramesh,
Tong Sun and Jing Gao. Long-Term Memory Networks for Question
Answering.
17:40 -
== CONCLUDING REMARKS ==
18:00
Keynote Speaker
Dr. Amy Shi-Nash
Commonwealth Bank, Australia
Title:
How can Machine Learning / AI help Banks and Customers
Bio:
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.