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Lean Semantic Web Syllabus

[Work in progress]

Also see Bibliography


  • Goal
  • TBD

Section 1 Data Representation

  • What is data and structure?
  • Value of unstructured data
  • Value of structured data
  • Cost of data modeling
  • The art of readable knowledge
  • Naming resources
  • Locating resources
  • Relating resources
  • Syntax: XML, JSON, YAML, RDF, Python etc.
  • Implementation: some Python (RDFLib...)

Section 2 Databases

  • Cost of modeling and indexing semantics in database
  • Use relational DB for semantic modeling
  • Use key-value store to represent semantic relations: Redis etc.
  • Document database: MongoDB, Elastic Search etc
  • Graph database: TinkerPop stack, Neo4j, OrientDB
  • Graph batch processing: Pregel, Hama, GraphChi etc
  • Querying triples (with RDB, document db, graph db, or dedicated triple store)
  • Implementation: some Python

Section 3 Search and Findability

  • Database vs search engine
  • Cost of inverted index
  • Extend inverted index to model semantic relations
  • Understanding user queries: from keywords to sentences
  • Faceted search: Elastic Search and Solr
  • Graph search
  • Implementation: some Python (ESClient...)

Section 4 Data Exchange and Integration

  • Portablility
  • Protocol Buffers and Thrift
  • Email and MIME
  • XMPP and Google Wave Protocol
  • REST API design
  • Some most important data APIs
  • Implementation: some Python (JSON...)

Section 5 Inference

  • Cost of reasoner and index (IR, DB, KB)
  • Just-in-time knowledge
  • Practical rule modeling
  • Inference as graph operations
  • Inference using databases
  • Inference using functional programming
  • Implementation: some Python (Pydatalog, Fuxi)

Section 6 Knowledge Extraction

  • Cost of knowledge extraction
  • Data cleaning
  • Structure extraction
  • Shallow parsing
  • Entity extraction
  • Relation extraction
  • Implementation: some Python (NLTK...)

Section 7 Visualization

  • Cognitive background
  • Exhibit and others
  • D3 (and other JavaScript lib)
  • NetworkX and (and other Python lib)
  • Implementation: some Python (mapplotlib...) and Javascript

Section 8 User Interaction

  • It's about people, not machine
  • Guided data exploration and discovery: why semantics is part of the solution
  • Query formulating
  • Faceted Browser
  • Mobile search, Voice interface and personal assistants
  • Implementation: some Python (ElasticSearch, Redis, RDF store based)

Section 9: Big Data and Lean Data

  • Measuring semantics in data
  • Small is beautiful in knowledge
  • The rule of knowledge growth on Web
  • Small knowledge: in-memory graph models
  • Big knowledge: knowledge bases on clusters
  • Big data
  • Datasets: Freebase, DBPedia, LOGD, Factual etc.
  • Platforms: EC2, and some others
  • Implementation: some Python (boto, starcluster)

Section 10 Lean Application Development

  • Build, measure, learn
  • Lean Canvas
  • MVP
  • Build: mockup strategies, pretotyping, prototyping
  • Measure: key metrics, but not vanity metrics
  • Learn: why^5
  • Semantic Wordpress/Drupal/Wiki, etc,
  • Data-oriented Programming
  • Implementation: some Python (mwclient) and some wiki


  • Table of Python tools
  • TBD