Teaching People to Teach Machines
Thematix has gained a reputation as a teacher of note in the area of semantics, natural language processing and ontologies. Our “Ontology 101” is a hit at trade shows, year after year. You may enjoy paging through a version of this presentation here:
Introduction to Semantics and Ontology
We can bring our long experience teaching graduate computer science students and industry groups to your firm, to educate a variety of staff in becoming familiar with ontologies and semantic technologies, covering a broad range of topics, including an overview of best practices, tools and techniques used to build semantically enabled systems. For this broad audience, we typically deliver the following syllabus:
- Introduction
- Origins and evolution of semantic technologies
- The business case for semantics
- Enterprise use cases, applications in science, healthcare and pharma
- Emerging, high-profile applications and uses – for question answering, integration, interoperability, data governance, recommendation systems, etc.
- Ontology Engineering Overview
- Ontology basics – definitions, underlying logic fundamentals
- Business requirements and use case driven approach
- Development methodology including conceptual modeling, terminology work, vocabulary and logic languages, ontology development, provenance, evaluation and maintenance
- State-of-the-Art
- Semantic technologies and machine learning
- Current applications survey
- Industrial applications
- Pharma applications
- Significance and implications for Merck
- Possible future directions
Ontology Engineering Training
For more immersive course, content is drawn, in part, from an advanced, graduate degree level course (see https://tw.rpi.edu/web/Courses/Ontologies/2016 for an overview of the semester-long course), with roughly 1.5 to 2 days on the ontology engineering methodology, best practices, and hands-on exercises. This is a fairly aggressive syllabus, and will require coordination in advance to ensure that participants have at least a couple of tools loaded on their machines and access to the internet during the sessions.
- Process
- Principles of Business Architecture
- Requirements Gathering using Methods from Business Architecture and Use Case Analysis
- Terminology Extraction and Analysis
- Conceptual Modeling
- Ontology Development
- RDF, RDFS, OWL Language Concepts
- Introduction to Formal Logic, with focus on Description Logics
- Best Practices in Ontology Development, including Common Patterns, Anti-Patterns
- Common Architecture Challenges – Namespaces, Modularization
- Using Tools to Check Your Ontology: Syntax, Semantics, and Regression Testing
- Advanced Ontology Engineering Practices
- Provenance, References, and Evidence Collection
- Change Management
- Ontology Evaluation, Reuse and Extension
- Ontology Evolution
- Question Answering Using RDF, Triple Stores and SPARQL
- RDF and Triple Stores
- RDF and RDF serializations (XML, TRTL, N3)
- Triple Stores, HTTP endpoint
- SPARQL
- SPARQL with reasoning assistance (A-Box queries)
- OWL 2 Profiles: EL, QL, RL
- Reasoning as query re-writing
- SPARQL queries against the T-Box
- Practical Applications for SPARQL and Triple Stores
- Testing Ontologies
- Big Data
- Language Processing
- Named Entity Recognition
- Information Extraction, Text Analytics
- Query Answering, Document Summarization
- NLP Technologies
- Dictionaries
- Vector Spaces
- Hidden Markov Models
- Supervised Learning Engines
- NLP Pipeline Architectures
- Morphology
- Syntax
- Pragmatics
- Practical Applications and Resources
- Advanced NLP Application Overview (dialogue, translation,…)
- System architecture
- Data Integration, Migration and Management, including R2RML
- Development strategies, feasibility assessment
- Advanced Topics
- Question answering
- Decision support and recommendation systems, using reference and operational data, streaming data, and so forth
- Applying Ontology to NLP
- Reasoning Beyond Description Logics (g. Flora 2, Answer Sets, Bayesian reasoning, Prolog)