AAAI 2019 Spring Symposium on
Combining Machine Learning with Knowledge Engineering
March 25–27, 2019 @ Stanford University, Palo Alto, California, USA
... and work together on a combined machine learning and knowledge engineering based AI!
This symposium brings together researchers and practitioners from various communities of machine learning and knowledge engineering working together on joint AI that is explainable, compliant and grounded in domain knowledge.
- Submission due: 2nd of November 2018
- Notification of authors: 3rd of December 2018
- Registration: 8th of February 2019
- Camera-ready: 22nd of February 2019
- Symposium: 25-27 of March 2019
The symposium involves presentations of accepted full and position papers, and posters, (panel) discussions, demonstrations, plenary sessions with breakouts (if required), to foster interaction and contribution among the participants.
Among relevant topics are:
- Knowledge Representation and Reasoning
- Rule-based systems
- Semantic Web
- Machine Learning
- Deep Learning
- Neural Networks
- Knowledge Engineering and Management
- Causal Explainability
- Learning and Cause & Effect Relationships
- Learning Ontologies
- Using Knowledge to Guide Machine Learning
Machine learning helps to solve complex tasks based on real-world data instead of pure intuition. It is most suitable for building AI systems when knowledge is not known, or knowledge is tacit. Knowledge engineering, on the other hand, is appropriate for representing expert knowledge, which people are aware of and that has to be considered for compliance reasons or explanations.
Many business cases and real-life scenarios using machine learning methods, however, demand explanations of results and behavior. This is particularly the case where decisions can have serious consequences. Furthermore, application areas such as banking, insurance and medicine, are highly regulated and require compliance with law and regulations. This specific application knowledge cannot be learned but needs to be represented, which is the area of knowledge engineering.
Knowledge-based systems that make knowledge explicit often based on logic and thus can explain their conclusions. These systems typically require a higher initial effort during development than systems that use unsupervised learning approaches. However, symbolic machine learning and ontology learning approaches are promising for reducing the effort of knowledge engineering.
Because of their complementary strengths and weaknesses, there is an increasing demand for the integration of knowledge engineering and machine learning. Conclusively, recent results indicate that explicitly represented application knowledge could assist data-driven machine-learning approaches to converge faster on sparse data and to be more robust against noise.
This symposium aims for bringing together researchers and practitioners from various communities of machine learning and knowledge engineering working together on joint AI that is explainable, compliant and grounded in domain knowledge. Participants shall benefit from each other to avoid pitfalls on one hand side and provide the ground for synergetic co-operations with the aim of identifying the most promising areas of quick wins.
AAAI Spring Symposium Series
«The AAAI Spring Symposium Series is typically held during spring break (generally in March) on the west coast [and] is an annual set of meetings run in parallel at a common site. It is designed to bring colleagues together in an intimate forum while at the same time providing a significant gathering point for the AI community. The two and one half day format of the series allows participants to devote considerably more time to feedback and discussion than typical one-day workshops. It is an ideal venue for bringing together new communities in emerging fields.» (aaai.org)
Peter Clark, Allen Institute for Artificial Intelligence, Seattle, WA, USA.
Aurona Gerber, University of Pretoria, South Africa.
Knut Hinkelmann, FHNW University of Applied Sciences and Arts Northwestern Switzerland.
Doug Lenat, Cycorp Inc., Austin, TX, USA.
Andreas Martin, FHNW University of Applied Sciences and Arts Northwestern Switzerland.
Frank van Harmelen, VU University, Amsterdam, Netherlands.
We solicit full papers, position papers, and poster abstracts on topics related to the above and can include recent or ongoing research, surveys, and business/use cases. Furthermore, proposals for (panel) discussions and demonstrations are very welcome too.
- Full papers (up to 12 pages) and position papers (3 to 5 pages) will be peer-reviewed.
- Posters can be proposed by submitting an extended abstract (1 to 2 pages).
- Discussion proposals (1 to 2 pages) should contain a description of the specific topic with a list of questions and a discussion moderator. For a panel discussion, a list of agreed panel-members should be mentioned.
- Demonstration proposals (1 to 2 pages) should have a focus on research and business related to the symposium excluding undesired product presentation and advertising.
Accepted and camera-ready papers will be published on the established open access proceedings site CEUR-WS (pre-prints may be published on arXiv and Zenodo). Authors must grant a publication permission prior symposium and present the paper at the symposium to get it published.
- September 29, 2018: Second Call for Participation (CfP) - PDF, HTML & TXT.
- September 7, 2018: News article of the Cycorp, Inc.
- August 31, 2018: Blog article of the FHNW School of Business.
- August 23, 2018: WikiCFP.
- August 22, 2018: EasyChair Smart CFP.
- August 21, 2018: First Call for Participation (CfP).
- August 9, 2018: SSS-19 EasyChair submission site is open.
- July 19, 2018: Symposium proposal has been accepted.