Improving Machine Learning using Background Knowledge Ontology Summit 2017 Track B – 15 March

Track B : Using Background Knowledge to Improve Machine Learning Results Track Co-champions: Mike Bennett Andrea Westerinen Blog Page http://ontologforum.org/index.php/Blog:Improving_Machine_Lear ning_using_Background_Knowledge

Motivations Machine Learning (ML) is based on defining and using mathematical models to perform tasks, predict outcomes, make recommendations, etc. Initial models can be specified by a data scientist, and/or constructed through combinations of supervised and unsupervised learning and pattern analysis Challenges with this: If no background knowledge is employed, the ML results may not be understandable There is a bewildering array of model choices and combinations Background knowledge could improve the quality of ML results by using reasoning techniques to select learning models and prepare the training and examined data (reducing large, noisy data sets to manageable, focused ones)

Summit Graphical Overview

Track B Positioning

Objective The objective of this Ontology Summit 2017 track is to understand: Challenges in using different kinds of background knowledge in machine learning Role of ontologies, vocabularies and other resources to improve machine learning results Design/construction/content/evolution/... requirements for an ontology to support machine learning

Today’s Session We explore the problem space by way of a targeted presentation on learning for decision support We want to start a conversation on the kinds of ontologies needed for ML, and their "ground rules" and requirements We will also explore other aspects of an ontology- driven natural language architecture: The application of semantics to neural learning functionality The possible role of statistical analysis Where does the human fit in the loop and what do they do? These issues will set the scene for the second Track B session on April 12.

Some Use Cases Parsing regulatory texts Parsing informal chatter To identify hot spots for the organization to address To identify new and emerging requirements Parsing informal chatter Intelligence – who is connected to whom? Compliance – investor chatter, malfeasance Drawing inferences from text in company filings What do we know about relations between entities? What do we know about exposures, risks etc.?

Considerations How is natural language analysed What are the steps / pieces getting from there to “Meaning”? The role of ontologies Last week in Track A we heard about generating ontologies from natural language texts In this track we want to understand how or whether one can use ontologies to improve results from unstructured text Kinds of ontology or knowledge resource to use Formal ontology (FOL e.g. OWL) Vocabulary or thesaurus etc. Other formalisms Role of synonyms / near synonyms etc.

Ontologies What does the ontology need to do And how does this determine what kinds of ontology can be used in this way? For instance: given a loose, human use of a generic word like “deal” Do we identify the precise concept of “deal” at one of its most general in the ontology Actually Deal is a synonym for 2 financial concepts: an offering or a trade Do we populate the ontology with near-synonyms that may be used to refer to a concept even when they don’t refer precisely? For this, any ontology used needs to have a good taxonomy Support multiple facets; deep specific to general hierarchy Additional metadata helps Synonyms (giving heteronyms when looked at the other way!) Broader and narrower near-synonyms (SKOS?) Synecdochal usages – where a part is used to describe the whole or vice versa Banking example: use of early, verbose draft material from FIBO meant there were more words in a given context (where a class or property is the context), making it easier to disambiguate different usages How does this influence the style of design of an ontology, or the selection of existing ones? E.g. do formal restrictions play a role in the use of an ontology in machine learning? If not, where does the meaning come from?

Other Pieces of the Puzzle Linguistic Analysis How do we get from the range of words in English or Chinese or Arabic, to the core noun and verb concepts in an ontology? Fuzzy and imprecise human usages Statistical analysis Whether “Bank” is a part of a river or a financial institution ontology/ies as the reference point “This is what Bank means in this context” Additional contextual content including metadata Machine learning What about neural learning functions? If it all happens in a “Black box” can we understand meaning for any of it?

Today’s Session The overall theme for both of today’s presentations is how to achieve situational awareness from unstructured source material Different kinds of situation / time Different kinds of source material Different kinds of knowledge resource / ontology The question: how do we quickly and effectively get actionable information from the data available Split second decision making in real time Business compliance / reaction Forensic investigation Humans, ontologies, AI and the balance of these

Today’s Presentations Simon Davidson (Psonify) The Investigator's Toolkit – Deriving Immediate, Actionable Insights from Unstructured Data What we will learn Extraction of core terms from language Using an ontology for natural language processing Demo of the Investigative Toolkit Getting actionable insights from what is in the text Ken Baclawski (Northeastern University) Combining Ontologies and Machine Learning to Improve Decision Making Decision making – combining machine learning and other techniques Benefits – quality, understandability of decisions, adaptability Ontologies in decision making Human in the loop – integrating humans and computers Application domains

Over to our first speaker… Please put questions in the “Soaphub” chat window use the Bluejeans chat for logistics only These will form the basis for discussion later in the sessions Presenters will also respond to those questions in the chat once they are done speaking This session is being recorded and both the recording and the chat transcript will be made available