A talk by Ashleigh Faith
Director of Platform Knowledge Graph and Semantic Search, EBSCO
Slides available here
Machine learning is only as good as the data in which it is trained on, or the assets it is used to enrich. For categorical and named entities, we tend to use many of the same open resources for our models but there are problems with this.
What’s more, if we use unstructured text to train our models, we have to rely on methods that tend to strip out context like bag of words, co-occurrence and clustering.
Using knowledge modeling techniques, like taxonomies, ontologies, and knowledge graph, you can either retain the context during your extraction and feature process, or use these models to add the context to your ML and analytics.
Knowledge graphs are mostly used for processes and trend analysis such as fraud detection, data mesh, customer 360, supply chain and cyber security, to name a few. But knowledge graphs are also rich resources for contextual ML such as search, autoclassification, disambiguation, and data catalogs.
Join me in this Masterclass to walk through the methods you can use to 1. Assess open data sources for contextual modeling, and 2. Harness context of data through knowledge modeling which can be used in ML use cases.
Both assessment and modeling will use machine learning categorical data for examples.
Get hands-on experience:
Assessing external and internal categorical and named entity data sources for machine leaning application
Modeling structured and unstructured categorical data as a taxonomy and then adding semantic connections, to create a contextual model to help with your machine learning use cases. Example use cases are:
Autoclassification and feature extraction
Data catalog terminology alignment
Digital Asset Management modeling
Presentation and Discussion:
Why does context matter in machine learning and how can knowledge modeling help?
Which knowledge model, and for what purpose?
Adding or retaining context in ML
How to assess open data sources- walkthrough of medical subject headings (MeSH), Wikidata, WordNet, and Getty Vocabularies, to name a few.
Take the gathered data and morph it into a knowledge model, taxonomy to knowledge graph
Using web protégé (you will need a web protégé account- its free)
*Note, if you have used Protégé before and found it daunting, don’t worry. This class will show you shortcuts and tricks so it is easier to use, and how you can use this modeling in your preferred modeling tools
This class will be highly collaborative and interactive so please come prepared to discuss.
The first section will focus on reviewing the problem and methods and the second section will be focused on walking through the method in practice.
Participants will walk through each sample data source as a group and discuss the assessment criteria, and how to interpret that criteria for your own use case.
Next, we will walk through taking sample data and creating a taxonomy and then adding relationships to start a knowledge model. We will do this together using a shared model on WebProtege.
Lastly, we will discuss how you can implement models like this in your ML models and pipelines.
Beginner to Intermediate
Sample the videos below, totalling 2.5 hours of watch time if each were to be watched from start to finish, but you shouldn’t need to review them all nor should you feel the need to watch from start to finish.
Making a Product Taxonomy Part 1: 7 Tips to Prep: https://youtu.be/qHR4O5XTy4U
Making a Product Taxonomy Part 2: A Taxonomy for Jewelery: https://youtu.be/c-e3mVx93Xw
Step-by-Step, No-Code Taxonomy Model ANYONE Can Learn: https://youtu.be/bLNWyfc2jvQ
What is a URI?: https://youtu.be/l6amUpDMJ4s
8 Tips to Decide if Knowledge Graph is Right For You: https://youtu.be/cfQ155oH3PY
Step-by-Step, No-Code "Ontology" ANYONE Can Learn: https://youtu.be/zXCmbdo9iiY
The Semantic Dynamic Duo: Linked Data and Knowledge Graphs: https://youtu.be/9FcRWisqSw0
Knowledge Graphs and Machine Learning in Library/DAM Use Cases: https://youtu.be/8PX4gwB3glk