Cyc 201: Advanced Topics in Ontology
Development and Efficient Inference
Click here to register for this course.
This is a two-day course, open to those who have previously completed Cyc 101 and are
seeking a deeper understanding of some of the more complex aspects of knowledge representation,
machine reasoning, and natural language processing. This course is also very useful for those who are
about to embark on a project using Cyc and needing to better understand specific capabilities and how
to best apply them in their application. Review of relevant use cases provides a concrete understanding
of some of the concepts learned during Cyc 101.
The exact course content will be tuned to the needs of the attendees,
but typically includes topics such as:
- Temporal Qualification in Cyc
- The nature of HLMts (time interval and time parameter)
- 3D and 4D objects in Cyc (timely and timeless relations)
- Argument constraints and temporal qualification
- 'Mixed' temporal rules and contextual lifting
- Advanced Inference Rules
- Pragmatics and Efficiency in Inference
- Interacting with Enterprise Data
- Control of Knowledge Acquisition
- Situational Modeling in Cyc
- Scripting assertions and script-related reasoning
- Generalization and decomposition: type-level modeling of actions
- Reasoning about quantity change in Cyc
- Causality
- Quoting and Meta-Reasoning in Cyc
- The 'Quote' operator and issues with deploying same
- Types of CycLExpression
- Inference-controlling vocabulary (CycInferencHeuristicPredicates, etc.)
- Faceting Collections in Cyc: How to Do It and Why
- Positioning Knowledge in Context Space
- Dimensions in Context Space
- Managing and reasoning with microtheories
- Modal Logic and the Knowledge Base
- The Cyc KB and other Popular Representation/Reasoning Systems
- Cyc and Description Logics
- Cyc and IKRIS
- Cyc and OWL
- KB Quality Assurance and Maintenance
- Common Sense Tests
- KB Regression Testing
In addition, this course will expose you to some of advanced Cyc tools for entering, finding, inspecting,
and modifying KB content and offer hands-on practice with them. These tools include areas such as NL parsing
and generation, integration with structured- and semi-structured data sources, and visualization interfaces.
Prerequisite courses:
Follow-on courses:
