Rizwan Saleem

Posted on Jun 4

Building a Personal Knowledge Graph for Software Engineers

#frontend #webdev #react

Building a Personal Knowledge Graph for Software Engineers

A personal knowledge graph (PKG) is a lightweight, structured map of your technical knowledge, projects, and learning intent. It helps you organize concepts, track skills, and connect ideas across domains-accelerating learning, career planning, and collaboration. This tutorial walks you through designing, implementing, and using a PKG tailored for software engineers.

Why a PKG matters for engineers

Smoother onboarding: quickly connect new tools, stacks, and domain concepts to existing knowledge.
Clear learning path: see gaps, set learning goals, and measure progress.
Better decision making: evaluate tech choices by mapping tradeoffs and dependencies.
Collaboration power: share a succinct map with teammates, mentors, or recruiters.

A PKG isn’t a static repo of bookmarks; it’s a living graph that evolves with your career.

Core concepts

Nodes: The building blocks of your PKG. Types include: concepts, skills, projects, articles, tools, patterns, and goals.
Edges: Relationships between nodes. Examples: “learns,” “implements,” “depends-on,” “used-in,” “influences,” “replaces.”
Attributes: Properties on nodes (level, proficiency, date learned, difficulty, confidence, notes).
Projections: Views or slices of the graph focused on a domain (e.g., “Frontend Performance,” “Distributed Systems,” “Machine Learning for Engineers”).
Provenance: Track sources and reasoning: links to articles, snippets, or code.

Think of it as a directed, labeled multigraph with metadata attached to nodes and edges.

Step 1: Choose your data model and storage

Option A: Lightweight local graph

Data format: JSON or YAML
Storage: plain files in a git repo for version control
Pros: simple, portable, offline
Cons: limited querying, manual wiring needed for complex relationships

Option B: Embedded graph database

Data format: Graph database (e.g., Neo4j, Dgraph, or an embedded option like ArangoDB) or a lightweight graph store
Storage: local or remote server
Pros: fast traversals, complex queries, scalable
Cons: setup/maintenance overhead

Option C: Hybrid

Use JSON/YAML for author’s primary knowledge and a graph tool for advanced queries (e.g., a local Neo4j instance with a syncing mechanism)

For many engineers starting out, a hybrid approach works well: store nodes and edges as structured YAML in a git repo, and optionally mirror to a small graph database for advanced querying later.

Suggested starter schema (conceptual):

Node types: Concept, Skill, Tool, Project, Article, Pattern, Goal
Edges: learns, uses, teaches, related-to, depends-on, implemented-in, example-of, planned-for

Example snippet (YAML):
concepts:

id: functional-programming name: Functional Programming description: "A paradigm that treats computation as the evaluation of mathematical functions and avoids changing state." level: 2 skills:
id: polyglot-programming name: Polyglot Programming description: "Ability to work across multiple languages and paradigms." level: 3 projects:
id: reactive-dashboard name: Reactive Dashboard description: "A dashboard built with reactive streams and WebSockets." technologies: [react, rx, websockets]

edges:

from: functional-programming to: polyglot-programming relationship: related-to
from: polyglot-programming to: reactive-dashboard relationship: implemented-in
from: reactive-dashboard to: frontend-performance relationship: depends-on ### Step 2: Define a minimal, extensible schema for starting

Start with a small set of node types and a couple of relationships. You can always grow the graph.

Node types (essential)
- Concept: abstract ideas or domains
- Skill: concrete abilities (e.g., "Rust memory safety," "CI/CD pipelines")
- Tool: software or platforms (e.g., "Docker," "Kubernetes")
- Project: actual work items or experiments
- Article/Note: reading notes or references
- Goal: short- and mid-term career objectives
Relationship types (essential)
- learns: Skill/Concept learned
- uses: Tool used in a Skill/Project
- implemented-in: Project implements a Skill
- depends-on: Skill/Tool requirement
- related-to: Concept-to-Concept connections
- references: Article/Note references

Example starter data (conceptual):

Node: Concept - Microservices
Node: Skill - API design
Node: Tool - Docker
Node: Project - Shopping-cart microservice
Node: Article - "Domain-Driven Design in Practice"

Edges:

Microservices related-to API design
API design depends-on Docker
Shopping-cart microservice implements API design
Shopping-cart references Domain-Driven Design in Practice ### Step 3: Set up a minimal repo structure

If you go with a local YAML/JSON approach, a compact repo helps you version, review, and share.

.pkg/
- concepts/
- functional-programming.yaml
- skills/
- polyglot-programming.yaml
- tools/
- docker.yaml
- projects/
- reactive-dashboard.yaml
- articles/
- fp-notes.md
- edges.yaml
- goals/
- learn-rust-in-2026.yaml

Example edge entry (edges.yaml):
from: functional-programming
to: polyglot-programming
relationship: related-to

Keep data human-friendly: use IDs, names, and short descriptions. Use dates for learning milestones.

Step 4: Create practical workflows

Daily micro-updates: add one small node or update an edge after a learning session (e.g., “read article X,” “implemented Y in Z project”).
Weekly review: prune stale edges, reassess goals, and highlight new domains to explore.
Milestone mapping: align PKG with career goals (e.g., “Be proficient in distributed systems within 12 months”). Attach a plan edge like goal-achieved with date.

Sample weekly ritual:

Pick one new concept or tool to add as a node.
Create at least two edges that connect it to existing nodes.
Update a goal with progress metrics. ### Step 5: Practical code examples

This example shows a small Python utility to add and query a YAML-based PKG.

File: pkg_cli.py
Dependencies: PyYAML

Code (conceptual, Python 3.9+):

import sys
import yaml
from pathlib import Path
from datetime import date

BASE = Path(".pkg")

def load(pkg_path):
path = BASE / pkg_path
if not path.exists():
return {}
with open(path, "r", encoding="utf-8") as f:
return yaml.safe_load(f) or {}

def save(pkg_path, data):
path = BASE / pkg_path
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
yaml.safe_dump(data, f, sort_keys=False)

def add_concept(concept_id, name, description):
data = load("concepts/{}.yaml".format(concept_id))
data.update({"id": concept_id, "name": name, "description": description})
save("concepts/{}.yaml".format(concept_id), data)

def add_edge(from_id, to_id, relation):
edges = load("edges.yaml") or []
edges.append({"from": from_id, "to": to_id, "relationship": relation})
save("edges.yaml", edges)

def main():
# simple CLI demo: python pkg_cli.py add-concept fp "Functional Programming" "A paradigm..."
if len(sys.argv) < 2:
print("Usage: python pkg_cli.py [args]")
return
cmd = sys.argv
if cmd == "add-concept":
add_concept(sys.argv, sys.argv, sys.argv)
elif cmd == "add-edge":
add_edge(sys.argv, sys.argv, sys.argv)
else:
print("Unknown command")

if name == "main":
main()

Usage examples:

python pkg_cli.py add-concept functional-programming "Functional Programming" "A paradigm..."
python pkg_cli.py add-edge functional-programming polyglot-programming related-to

Note: This is intentionally simple. As your PKG grows, you can switch to a graph database or add richer metadata and validation.

Step 6: Build useful views or reports

Even a small PKG benefits from focused views. Here are a few practical ones you can implement with minimal tooling:

Proficiency snapshot: list of skills with Level and last-learned date
- Output: a table or markdown list
Learning plan by quarter: identify concepts/tools to learn in the next 12 weeks
- Output: a plan table with goals, prerequisites, and success criteria
Dependency map: show which skills depend on others
- Output: a simple graph visualization (you can export to Graphviz DOT or use a JS library for a web view)
Project map: connect projects to the skills they exercise
- Output: a dashboard of projects vs. skills

Example DOT graph snippet (for Graphviz):
digraph PKG {
"Functional Programming" -> "Polyglot Programming" [label="related-to"];
"API Design" -> "Shopping-cart microservice" [label="implemented-in"];
"Docker" -> "Shopping-cart microservice" [label="used-in"];
}

This can render a visual map to inspire learning paths.

Step 7: Integrate with your daily tools

Notes apps: Link notes to corresponding PKG nodes for quick cross-referencing.
IDEs: Some editors can open a PKG as a lightweight knowledge sidebar (e.g., a Markdown/JSON view in VS Code).
Version control: Store the PKG in a dedicated branch or repo; use PRs to review changes, reinforcing deliberate learning.

If you prefer a lightweight browser-based view, you can build a small static site that reads the YAML data and renders interactive graphs with a library like D3.js or Vis.js. For a quick start, export edges to a Graphviz DOT file and view it with any Graphviz tool.

Illustration: Simple PKG view concept

Node: Microservices
- Skills: API design, distributed tracing
- Tools: Docker, Kubernetes
- Projects: Shopping-cart service
- Related Articles: Domain-Driven Design in Practice
Edges connect as: Microservices -depends-on→ API design; Shopping-cart service -uses→ Docker; API design -related-to→ Distributed tracing

This mental map helps you see how improvements in API design ripple through projects and tooling.

Step 8: Maintain quality and guardrails

Keep IDs stable: once you create a node ID, avoid renaming it; update descriptions or add new edges instead.
Be explicit about provenance: store a citation or reference for each concept or claim you add (e.g., article URL, date learned).
Prioritize readability: write clear descriptions; a PKG should be understandable to future you or a colleague.
Regular cleanup: quarterly prune of obsolete edges and consolidation of duplicates. ### Step-by-step plan to start today

1) Pick storage: YAML-based PKG in a Git repo (local-first, easy to iterate).
2) Define the first 6 node types and 6 relationship types.
3) Create a starter dataset: 5 concepts, 5 skills, 3 tools, 2 projects, 2 articles.
4) Implement a tiny CLI (or use the sample Python script) to add nodes and edges.
5) Build a simple view for yourself: export a Markdown summary of your current PKG.
6) Schedule a 30-minute weekly PKG session to add 1 concept, 1 edge, and update a goal.

Example starter dataset (illustrative)

Concepts
- id: functional-programming name: Functional Programming description: A paradigm focused on composing pure functions and avoiding shared state. level: 2
- id: distributed-systems name: Distributed Systems description: Systems designed to run on multiple machines with reliability and consistency. level: 2
Skills
- id: polyglot-programming name: Polyglot Programming description: Write code across multiple languages without paralysis. level: 1
- id: api-design name: API Design description: Designing robust, usable, and scalable interfaces. level: 2
Tools
- id: docker name: Docker description: Containerization for reproducible environments.
- id: kubernetes name: Kubernetes description: Orchestrates containers at scale.
Projects
- id: shopping-cart-ms name: Shopping Cart Microservice description: A small microservice illustrating clean API boundaries.
Articles
- id: dd-in-practice name: Domain-Driven Design in Practice url: https://example.org/dd-in-practice
Edges
- from: functional-programming to: polyglot-programming relationship: related-to
- from: polyglot-programming to: shopping-cart-ms relationship: implemented-in
- from: docker to: shopping-cart-ms relationship: used-in
- from: api-design to: shopping-cart-ms relationship: depends-on
- from: dd-in-practice to: api-design relationship: references If you’d like, I can tailor a PKG template to your current tech stack and goals (e.g., frontend-heavy, backend, or data engineering focus) and generate a ready-to-run YAML/JSON scaffold plus a tiny CLI script. Tell me your preferred focus area and whether you want a local-first setup or one that scales with a lightweight graph database.

Would you like me to draft a starter PKG tailored to your Carlisle, England context and your current tech stack, with an initial 5 concepts, 5 skills, and 3 projects?

Rizwan Saleem | https://rizwansaleem.co