Process Ontology and Knowledge Graph Series v1.0
From Semantic Web to Process Knowledge Reasoning - Structuring and Utilizing Chemical Plant Knowledge
Series Overview
This series is a comprehensive 5-chapter educational content that allows you to learn process ontology and knowledge graphs from basics to practice. You will master Semantic Web technologies using RDF/OWL, process equipment ontology modeling, knowledge graph construction from plant data, and knowledge reasoning using SPARQL, enabling you to implement practical chemical process knowledge management systems.
Features:
- ✅ Practice-oriented: 35 executable Python code examples (using rdflib, owlready2)
- ✅ Systematic structure: 5-chapter structure for progressive learning from Semantic Web basics to process knowledge reasoning
- ✅ Industrial applications: Complete implementation of P&ID analysis, plant data integration, and knowledge base construction
- ✅ Latest technologies: RDF/OWL 2.0, SPARQL 1.1, rdflib/owlready2 integration framework
Total learning time: 140-170 minutes (including code execution and exercises)
How to Study
Recommended Learning Order
For beginners (learning Semantic Web for the first time):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Time required: 140-170 minutes
Database experienced (SQL or NoSQL experience):
- Chapter 1 (quick review) → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Time required: 110-140 minutes
Ontology experienced (OWL/RDF knowledge):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Time required: 90-110 minutes
Chapter Details
Chapter 1: Fundamentals of Ontology and Semantic Web
Learning content
- RDF (Resource Description Framework) Basics
- Triple structure (Subject-Predicate-Object)
- URI and resource identification
- RDF/XML and Turtle notation
- RDF operations with rdflib
- RDFS (RDF Schema) Concepts
- Class hierarchy (rdfs:Class, rdfs:subClassOf)
- Property definition (rdfs:Property)
- Domain and range
- Property hierarchy
- SPARQL Basic Queries
- Basic pattern matching
- SELECT/CONSTRUCT/ASK/DESCRIBE
- FILTER conditional expressions
- Aggregate functions and grouping
- Chemical Process Knowledge Representation Examples
- RDF representation of equipment and their connections
- Triple representation of substances and their properties
- Graph structure of process variables
Learning objectives
- ✅ Understand RDF triple structure and semantics
- ✅ Able to describe RDF graphs in Turtle notation
- ✅ Able to create and manipulate RDF data with rdflib
- ✅ Able to query RDF graphs with SPARQL
- ✅ Able to represent chemical process knowledge in RDF
Chapter 2: Process Ontology Design and OWL Modeling
Learning content
- OWL (Web Ontology Language) Concepts
- Classes (owl:Class) and instances
- Object properties (relationships)
- Data properties (attributes)
- OWL 2 expressiveness
- Property Constraints and Class Axioms
- Cardinality constraints (Exactly, Min, Max)
- Value constraints (someValuesFrom, allValuesFrom)
- Intersection, union, complement classes
- Equivalent classes and disjoint classes
- Process Equipment Ontology
- Equipment class hierarchy (Reactor, HeatExchanger, Separator, etc.)
- Inter-equipment connections (hasInput, hasOutput)
- Physical attributes (temperature, pressure, flow rate)
- Operating conditions and specifications
- Complete Chemical Process Ontology
- Chemical and Phase
- Process Flow (Stream)
- Control system (ControlLoop, Sensor, Actuator)
- Abnormal events (Alarm, Event)
Learning objectives
- ✅ Able to define OWL classes and properties
- ✅ Able to express knowledge rigorously using property constraints
- ✅ Able to design hierarchical ontology of chemical equipment
- ✅ Able to implement OWL ontology with owlready2
- ✅ Able to define axioms necessary for process knowledge reasoning
Chapter 3: Knowledge Graph Construction from Process Data
Learning content
- Entity Extraction and Triple Generation
- Equipment name extraction from text
- RDF conversion of CSV/JSON data
- Integration of pandas and rdflib
- Batch triple generation patterns
- Automatic Relationship Extraction
- P&ID (Piping and Instrumentation Diagram) analysis
- Connection relationship extraction from process flow diagrams
- Estimation of inter-equipment dependencies
- Validation through graph matching
- Sensor Data Integration
- RDF representation of time-series data
- Association of measurement values and metadata
- Graph representation of sensor networks
- Real-time data stream processing
- Integration of Historical Data and Knowledge Graph
- Pattern extraction from past operation data
- Association of abnormal events and their causes
- Graph representation of maintenance history and trouble cases
- Complete plant knowledge base construction
Learning objectives
- ✅ Able to convert CSV/JSON data to RDF triples
- ✅ Able to build equipment connection graphs from P&ID information
- ✅ Able to integrate time-series sensor data into knowledge graphs
- ✅ Able to extract knowledge from historical data and graph it
- ✅ Able to build knowledge graphs of large-scale plant data
Chapter 4: Process Knowledge Reasoning and Inference Engine
Learning content
- RDFS Reasoning
- Subclass reasoning (rdfs:subClassOf)
- Subproperty reasoning (rdfs:subPropertyOf)
- Domain and range reasoning
- RDFS reasoning implementation in rdflib
- OWL Reasoning
- Class subsumption reasoning
- Property chain reasoning
- Symmetry, transitivity, inverse properties
- HermiT/Pellet reasoning engine integration
- SPARQL Reasoning Queries
- Transitive closure queries (SPARQL Property Paths)
- Aggregation and subqueries
- New triple generation with CONSTRUCT
- Implementation of complex reasoning patterns
- Process Knowledge Reasoning in Practice
- Abnormality propagation path reasoning
- Automatic discovery of equipment dependencies
- Knowledge reasoning for process optimization
- Root Cause Analysis
Learning objectives
- ✅ Understand principles of RDFS/OWL reasoning
- ✅ Able to make implicit knowledge explicit using reasoning engine
- ✅ Able to extract complex relationships with SPARQL Property Paths
- ✅ Able to reason about process abnormality propagation paths
- ✅ Able to implement reasoning queries for root cause analysis
Chapter 5: Implementation and Integrated Applications
Learning content
- Knowledge Graph Storage
- Triple Store (Virtuoso, GraphDB, Blazegraph)
- Integration with rdflib Persistent Store
- Performance optimization for large-scale graphs
- Indexing and query optimization
- REST API and Web Services
- SPARQL Endpoint implementation with Flask/FastAPI
- GraphQL interface
- JSON-LD conversion
- Authentication and access control
- Visualization and User Interface
- Graph visualization with NetworkX
- Interactive display with Cytoscape.js
- Dashboard integration
- Dynamic generation of process flow diagrams
- Integrated Application Development
- Plant abnormality diagnosis system
- Equipment maintenance knowledge base
- Operation support system
- Digital twin integration
Learning objectives
- ✅ Able to build large-scale knowledge graphs using Triple Store
- ✅ Able to publish SPARQL Endpoint via REST API
- ✅ Able to visually represent knowledge graphs
- ✅ Able to implement abnormality diagnosis system for real processes
- ✅ Able to complete process ontology projects
Overall Learning Outcomes
Upon completion of this series, you will have acquired the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Understand theoretical foundations of RDF/OWL Semantic Web technologies
- ✅ Know principles and design patterns of ontology modeling
- ✅ Understand mechanisms of SPARQL reasoning queries
- ✅ Know process knowledge structuring and reasoning methods
Practical Skills (Doing)
- ✅ Able to create and manipulate RDF graphs with rdflib
- ✅ Able to implement OWL ontologies with owlready2
- ✅ Able to write complex reasoning queries in SPARQL
- ✅ Able to convert process data to knowledge graphs
- ✅ Able to operate Triple Store and publish APIs
Application Ability (Applying)
- ✅ Able to build knowledge bases for real plants
- ✅ Able to automatically generate knowledge graphs from P&ID
- ✅ Able to implement abnormality diagnosis systems with knowledge reasoning
- ✅ Able to integrate digital twins and knowledge graphs
- ✅ Able to lead Semantic Web projects as a process engineer
FAQ (Frequently Asked Questions)
Q1: Is prior knowledge of RDF or OWL necessary?
A: Not essential, but basic knowledge of databases (SQL) or graph theory will speed up understanding. This series is designed so that beginners can learn progressively.
Q2: What are the differences from traditional relational databases?
A: RDF has a flexible graph structure, making schema changes easy. Also, reasoning functions can make implicit knowledge explicit, and integration of different data sources is natural. On the other hand, RDBMS is superior for transaction processing and high-speed search of large amounts of data.
Q3: What Python libraries are required?
A: Mainly rdflib, owlready2, pandas, NetworkX, and SPARQLWrapper are used. All can be installed via pip.
Q4: What is the relationship with the Process Control and Monitoring Series?
A: The control logic and anomaly detection covered in the Process Control Series can be complemented by knowledge reasoning in this series. Knowledge graphs enable root cause analysis and optimization of control systems.
Q5: Can this be applied to actual plants?
A: Yes. Chapter 5 covers a complete workflow for application to real plants through practical integrated applications. However, careful design of security and data governance is necessary.
Next Steps
Recommended Actions After Series Completion
Immediate (within 1 week):
1. ✅ Publish integrated application from Chapter 5 on GitHub
2. ✅ Evaluate opportunities to build knowledge graphs for your company's plant
3. ✅ Try representing simple equipment connection graphs in RDF
Short-term (1-3 months):
1. ✅ Validate knowledge graph with P&ID data
2. ✅ Deploy Triple Store to production environment
3. ✅ Build prototype of abnormality diagnosis system
4. ✅ Consider integration with digital twin
Long-term (6 months or more):
1. ✅ Build and operate company-wide knowledge base
2. ✅ Integrate AI agents and knowledge graphs
3. ✅ Academic presentations and paper writing
4. ✅ Career building as Semantic Web specialist
Feedback and Support
About This Series
This series was created as part of the PI Knowledge Hub project under Dr. Yusuke Hashimoto at Tohoku University.
Created on: October 26, 2025
Version: 1.0
We Welcome Your Feedback
We look forward to your feedback to improve this series:
- Typos, errors, technical mistakes: Please report via GitHub repository Issues
- Improvement suggestions: New topics, additional code examples you'd like to see, etc.
- Questions: Parts that were difficult to understand, sections requiring additional explanation
- Success stories: Projects using what you learned from this series
Contact: yusuke.hashimoto.b8@tohoku.ac.jp
License and Terms of Use
This series is published under the CC BY 4.0 (Creative Commons Attribution 4.0 International) license.
What you can do:
- ✅ Free viewing and downloading
- ✅ Use for educational purposes (classes, study sessions, etc.)
- ✅ Modification and derivative works (translation, summary, etc.)
Conditions:
- 📌 Author credit must be displayed
- 📌 When modified, you must indicate so
- 📌 Contact in advance for commercial use
Details: CC BY 4.0 License Full Text
Let's Begin!
Are you ready? Start with Chapter 1 and begin your journey into the world of process ontology and knowledge graphs!
Chapter 1: Fundamentals of Ontology and Semantic Web →
Revision History
- 2025-10-26: v1.0 Initial release
Your journey to structure process knowledge begins here!