Session 2, Track B: Improving Machine Learning Using Background Knowledge

Abstract

Context: Background knowledge and ontologies can be used to improve machine learning model selection, processing, data preparation and results.

Perception: Machine Learning requires knowledge of the domain being examined to define relevant hypotheses, infer missing information in data, choose relevant features, etc.

Motivation: Discuss the kinds of ontologies needed for ML, and their "ground rules" and requirements.

Please also see the Track's blog page and Meeting Page

Agenda and Speakers

• Introduction: Mike Bennett and Andrea Westerinen Slides

Speakers

• SimonDavidson, Psonify

Title: The Investigator's Toolkit – Deriving Immediate, Actionable Insights from Unstructured Data

Slides: pdf format pptx format

Abstract

In this session, Simon Davidson of Psonify demonstrates the use of the Investigator's Toolkit for deriving immediate situational awareness and actionable insights from unstructured datasets for forensic investigation. The session starts with a deep dive into the challenges in natural language processing, leading to the provisioning and use of ontologies for the relevant subject matter, as used in the IntuScan platform. As an example, a financial ontology (e.g. FIBO and extensions to that) could be used for financial regulatory compliance challenges, or a legal ontology for analysis of legal texts. This is followed by a live demonstration of the Investigator's Toolkit. The Toolkit is used in digital forensics, extracting concepts with reference to an ontology that isolates the meanings of the concepts and provides “artificial intuition” into the contexts of the subject text, giving immediately actionable insights into the content.

• Ken Baclawski (College of Computer and Information Science, Northeastern University)

Title: Combining Ontologies and Machine Learning to Improve Decision Making

Slides: pdf format

Abstract:

Decision making is fundamental for modern systems whether they are controlled by humans, computers or both. Machine Learning (ML) is an increasingly popular technique for decision making. This talk will give an overview of work at Oracle and Northeastern University that combines ontologies with ML and other techniques. Benefits of our combination include improving the quality of decisions, making decisions understandable, and improving the adaptability of decision making processes in response to changing conditions. Ontologies are especially well suited to improving decision making that includes both humans and computers. This is the case not only when humans are directly involved as system operators but also when humans are acting as regulators for autonomous systems. These techniques have been applied or proposed in a variety of domains such as customer support, healthcare, cloud services, aircraft operation, and nuclear power plants.