AAAI 2019 Spring Symposium on

Combining Machine Learning with Knowledge Engineering

March 25–27, 2019 @ Stanford University, Palo Alto, California, USA

Join us...

... 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.

Important Dates

  • Extended submission due: 18th of November 2018
  • Notification of authors: 7th of December 2018
  • Registration for authors and invitees: 8th of February 2019
  • Camera-ready: 22nd of February 2019
  • Registration for others (FCFS): 1st of March 2019
  • Symposium: 25-27 of March 2019

Registration

Registration is open for all accepted authors, invited speakers, symposium participants, and other invited attendees.

Motivation

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.

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