Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Knowledge Graphs, Semantics, Pitfalls
The tutorial will be of high value to data practitioners who are (expected to be) involved in building knowledge graphs (Information Architects, Data Modelers, Knowledge Engineers, Taxonomists, Ontologists, etc), as well as those who use knowledge graphs as the basis of their analysis or applications.
The tutorial will be self-contained but participants should have some prior knowledge and experience of data modeling.
A modern web browser
After this tutorial participants will be more aware of semantic-related problems that knowledge graphs typically have, and they will be equipped with concrete strategies and techniques to tackle them in their own projects
Ever since Google announced that ” their knowledge graph allowed searching for things, not strings”, the term “knowledge graph ” has been widely adopted, both by the academia and industry, to denote any graph-like network of interrelated typed entities and concepts that can be used to integrate, share and exploit data and knowledge.
This idea of interconnected data under common semantics is actually much older and the term is a rebranding of several other concepts and research areas (semantic networks, knowledge bases, ontologies, semantic web, linked data etc). Google popularized this idea and made it more visible to the public and the industry, the result being several prominent companies, including Airbnb, Amazon, Diffbot, LinkedIn, and Uber, developing and using their own knowledge graphs for data integration, data analytics, semantic search, question answering and other cognitive applications.
To paraphrase a famous quote, though, “With Great Popularity Comes Great Responsibility”. As knowledge graphs become larger in size and scope, and are used by bigger and more diverse audiences, their ability to represent semantic information that is accurate and consensual is stressed. Typical semantic modeling mistakes that in small-scale taxonomies and ontologies are controllable and perhaps not so harmful, in large-scale knowledge graphs can become really problematic and pretty hard to contain.
This tutorial will take participants into an investigative journey in the semantics of knowledge graphs and will teach them how to recognize modeling pitfalls that undermine their quality and value. More importantly, it will provide them with concrete strategies and techniques for avoiding these pitfalls in their own work, both as developers and consumers of knowledge graphs.
The tutorial will consist of two parts. The first part will be lecture-based and will ensure that all participants share some common terminology and mindset when talking about knowledge graphs and semantics. This is important as practitioners from different backgrounds and communities (semantic web, databases, taxonomies, linguistics, etc) are accustomed to different terminologies for the same or very similar semantic modeling elements.
The second part will be highly collaborative and interactive. The participants will form small teams, each of which will be assigned a “semantics crime scene”, namely (a part of) a public knowledge graph with problematic semantics. Each team will then need to identify and share with the other teams the graph’s problems, their possible causes, and potential ways these problems could have been avoided.
* 9:30 – 9:40 – Introductions (10 min)
Goals and scope of the tutorial
* 9:40 – 10:00 – Knowledge Graphs and Semantics (50 min)
What are knowledge graphs and how they are different from databases, taxonomies and other data artifacts.
Why semantics are important
Why semantics are difficult
Basic and common semantic modeling elements
Semantic and linguistic phenomena
* 10:30 – 10:45 – Break (15 min)
* 10:45 – 12:15 – Semantics CSI (90 min)
Visiting and exploring the scene(s)
Discovering the crime(s)
Identifying the culprit(s)
Figuring how to prevent future crimes
* 12:15 – 12:30 – Conclusion