Biological Knowledge Graphs

Storing data as Knowledge Graphs

• Graph-based data model

• When data does not fit into a fixed relation model

• Based on Semantic Web and Linked Data technologies

• Interlinking data across multiple data sources

1989, CERN Switzerland

Tim Berners-Lee
inventor of the World Wide Web

The Web's foundational ideas

• Data is linked to each other

  Across heterogeneous systems (decentralized)

• Browser programs

  Visualises data and helps users navigating the Web

• Everyone can read data

  Using Web browsers

• Open standards

  Anyone can implement tools (browsers, ...) on top of it

1990-... World-wide adoption

Not just for researchers anymore

The Web is a global information space

a.k.a. The World Wide Web (WWW)

Mostly used by humans through Web browsers

Web is focused on humans

• Web pages show information

  Visualized using Web browsers

• Clicking on links

  To discover new information

• Search information

  Using search engines such as Google, Bing, ...

Achieving tasks requires manual effort

• Will it rain next week?

  1. Find a weather prediction website
  2. Select your location
  3. Navigate to next week

• Book a trip for a group of people

  • Comparing agendas
  • Comparing interests: nature, musea, ...
  • Regional temperatures
  • Geopolitical status
  • ...

What if intelligent agents
could do these tasks for us?

I Have a Dream

A Web where intelligent
agents are able to achieve
our day-to-day tasks.
2001

The Semantic Web will bring structure to the meaningful content of Web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users.

The dream: Getting there step by step

2015: Agents are being introduced that can perform simple tasks

Google Assistant, Alexa, Siri, ...

How do these agents work?

→ Execute Queries over Knowledge Graphs

Query = Structured Question
Knowledge Graph = Structured information

→ Structured questions over structured information

A Knowledge Graph is
a collection of structured information

Semantic Web technologies

• RDF

  Standard for representing and exchanging graph data
  Knowledge Graphs are collections of such graph data

• SPARQL

  Standard for querying over RDF data

Generative AI models

2022: LLMs offer human-like conversations based on crawled Web data

Differences to Knowledge Graphs:

• Adaptive understanding of unstructured data
• Inconsistent answers and hallucinations
• Black box

RDF enables data exchange on the Web

• RDF

  Resource Description framework

• RDF 1.0

  Recommended by W3C since 1999

• RDF 1.1

  Recommended by W3C since 2014

• RDF 1.2

  Upcoming in 2025

Facts are represented as RDF triples

Multiple RDF triples form RDF datasets

:Alice :knows :Bob .
:Alice :knows :Carol .
:Alice :name "Alice" .
:Bob :name "Bob" .
:Bob :knows :Carol .

Multiple RDF triples form RDF datasets

RDF Resources are identified by IRIs

• IRI

  Internationalized Resource Identifier

• Global identifiers

  Can be used by different data sources → interlinking!

• IRIs can be URLs

  A URL is an IRI that can be dereferenced (e.g. looked up in Web browsers).
  Allows additional information to be looked up for a resource.

Multiple syntaxes exist for RDF

Turtle

PREFIX dbr: <http://dbpedia.org/resource/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>

dbr:Alice foaf:knows dbr:Bob .
dbr:Alice foaf:name "Alice" .

JSON-LD

{
    "@context": {
        "dbr": "http://dbpedia.org/resource/",
        "foaf": "http://xmlns.com/foaf/0.1/"
    },
    "@id": "dbr:Alice",
    "foaf:knows": "dbr:Bob",
    "foaf:name": "Alice"
}

SPARQL querying over RDF datasets

• SPARQL: language to read and update RDF datasets via declarative queries.
• Different query forms:
  
  
  • SELECT: selecting values in tabular form → focus of this presentation
  • CONSTRUCT: construct new triples
  • ASK: check if data exists
  • DESCRIBE: describe a given resource
  • INSERT: insert new triples
  • DELETE: delete existing triples

Specification: https://www.w3.org/TR/sparql-query/

Find all artists born in York

• SELECT ?name ?deathDate WHERE {
    ?person a dbpedia-owl:Artist;
            rdfs:label ?name;
            dbpedia-owl:birthPlace [ rdfs:label "York"@en ].
    FILTER LANGMATCHES(LANG(?name),  "EN")
    OPTIONAL { ?person dbpprop:dateOfDeath ?deathDate. }
  }
  

How do query engines process a query?

RDF dataset + SPARQL query
↓
...
↓
query results

How do query engines process a query?

RDF dataset + SPARQL query
↓
SPARQL query processing
↓
query results

Basic Graph Patterns enable graph pattern matching

• Basic Graph Pattern (BGP)

  A collection of triple patterns.

•     ?person a dbpedia-owl:Artist.
      ?person rdfs:label ?name.
      ?person dbpedia-owl:birthPlace ?birthPlace.
  

• Triple Pattern

  A triple in which any component may be a variable.
  Variables start with ?, followed by a label. (e.g. ?name)

• More complex operators are possible

  OPTIONAL, UNION, FILTER, ...

Query results representation

• Solution Mapping

  Mapping from a set of variable labels to a set of RDF terms.

• Solution Sequence

  A list of solution mappings.

Steps in SPARQL query processing

• 1. Parsing

  Transform a SPARQL query string into an algebra expression

• 2. Optimization

  Transform algebra expression into a query plan

• 3. Evaluation

  Executes query plan to obtain query results

Publishing Knowledge Graphs as SPARQL Endpoints

SPARQL endpoint: API that accepts SPARQL queries, and replies with results.

• Most popular way to publish Knowledge Graphs

  Alternatives are data dumps and Linked Data Documents

• Very powerful

  Very complex queries can be formulated with SPARQL

• Power comes with a cost

  SPARQL endpoints can require very powerful servers

Popular biological SPARQL endpoints

• Uniprot: Protein sequences (210+ billion triples)

  https://sparql.uniprot.org/

• Rhea: Chemical and transport reactions (5+ million triples)

  https://sparql.rhea-db.org/

Federation over multiple SPARQL endpoints

Data across multiple datasources can be joined in a single query

• Find all proteins linked to arachidonate (CHEBI:32395)

  Joins Uniprot and Rhea

• Retrieve human enzymes that metabolize sphingolipids and are annotated in ChEMBL

  Joins Uniprot, Rhea, and ChEMBL

Conclusions

• Biological Knowledge Graphs use (Semantic) Web technologies

  RDF, SPARQL, …

• Knowledge Graphs can be interlinked

  Thanks to RDF's global identifiers

• Federated querying

  Combining data across multiple Knowledge Graphs
  Complex queries can be slow