This document provides an introduction to bio-ontologies and the semantic web. It discusses what ontologies are and how they are used in the bio domain through initiatives like the OBO Foundry. It introduces key semantic web technologies like RDF, URIs, Turtle syntax, and SPARQL query language. It provides examples of ontologies like the Gene Ontology and how ontologies can be represented and queried using these semantic web standards.
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Seminar Presentation for PMB Department, UC Berkeley for Love Data Week. Subject is how to prepare publications and associated data sets for maximum reuse.
Connecting life sciences data at the European Bioinformatics InstituteConnected Data World
Tony Burdett's slides from his talk at Connected Data London. Tony is a Senior Software Engineer at The European Bioinformatics Institute. He presented the complexity of data at the EMBL-EBI and what is their solution to make sense of all this data.
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Data integration is intrinsic to how modern research is undertaken in areas such as genomics, drug development and personalised medicine. To better enable this integration a large number of biomedical ontologies have been developed to provide standard semantics for describing metadata. There are now several hundred biomedical ontologies in widespread use that describe concepts such as genes, molecules, drugs and diseases. This amounts to millions of terms that are interconnected via relationships that naturally form a graph of biomedical terminology.
The Ontology Lookup Service (OLS) (http://www.ebi.ac.uk/ols) integrates over 160 ontologies and provide a central point for the biomedical community to query and visualise ontologies. OLS also provide a RESTful API over the ontologies that is used in high-throughput data annotation pipelines. OLS is built on top of a Neo4j database that provides efficient indexes for extracting ontological relationships. We have developed generic tools for loading RDF/OWL ontologies into Neo4j where the indexes are optimised for serving common ontology queries. We are now moving to adopt graph database more widely in applications relating to ontology mapping prediction and recommendation systems for data annotation.
These slides were presented at the "graph databases in life sciences workshop". There is an accompanying Neo4j guide that will walk you through importing data into Neo4j using web services form a number of databases at EMBL-EBI.
https://github.com/simonjupp/importing-lifesci-data-into-neo4j
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Ron Daniel and Corey Harper of Elsevier Labs present at the Columbia University Data Science Institute: https://www.elsevier.com/connect/join-us-as-elsevier-data-scientists-present-at-columbia-university
Query-Load aware partitioning of RDF datasets using standard fragmentation techniques for relational databases aimed to provide an insight of the advantages of a proper fragmentation scheme in big semantic databases for efficient query processing.
How to make your published data findable, accessible, interoperable and reusablePhoenix Bioinformatics
Seminar Presentation for PMB Department, UC Berkeley for Love Data Week. Subject is how to prepare publications and associated data sets for maximum reuse.
Connecting life sciences data at the European Bioinformatics InstituteConnected Data World
Tony Burdett's slides from his talk at Connected Data London. Tony is a Senior Software Engineer at The European Bioinformatics Institute. He presented the complexity of data at the EMBL-EBI and what is their solution to make sense of all this data.
Facilitating semantic alignment of EMBL-EBI services using ontologies and semantic web technology. Presentation at the BioHackathon Symposium 2016, Japan.
Data integration is intrinsic to how modern research is undertaken in areas such as genomics, drug development and personalised medicine. To better enable this integration a large number of biomedical ontologies have been developed to provide standard semantics for describing metadata. There are now several hundred biomedical ontologies in widespread use that describe concepts such as genes, molecules, drugs and diseases. This amounts to millions of terms that are interconnected via relationships that naturally form a graph of biomedical terminology.
The Ontology Lookup Service (OLS) (http://www.ebi.ac.uk/ols) integrates over 160 ontologies and provide a central point for the biomedical community to query and visualise ontologies. OLS also provide a RESTful API over the ontologies that is used in high-throughput data annotation pipelines. OLS is built on top of a Neo4j database that provides efficient indexes for extracting ontological relationships. We have developed generic tools for loading RDF/OWL ontologies into Neo4j where the indexes are optimised for serving common ontology queries. We are now moving to adopt graph database more widely in applications relating to ontology mapping prediction and recommendation systems for data annotation.
These slides were presented at the "graph databases in life sciences workshop". There is an accompanying Neo4j guide that will walk you through importing data into Neo4j using web services form a number of databases at EMBL-EBI.
https://github.com/simonjupp/importing-lifesci-data-into-neo4j
Semi-automated Exploration and Extraction of Data in Scientific TablesElsevier
Ron Daniel and Corey Harper of Elsevier Labs present at the Columbia University Data Science Institute: https://www.elsevier.com/connect/join-us-as-elsevier-data-scientists-present-at-columbia-university
Query-Load aware partitioning of RDF datasets using standard fragmentation techniques for relational databases aimed to provide an insight of the advantages of a proper fragmentation scheme in big semantic databases for efficient query processing.
This paper was presented at the COST Digital Humanities Conference: Reassembling the Republic of Letters. My brief was to give a general, introductory discussion of the history, limits and future of encoded text, particularly XML and particularly the Text Encoding Initiative.
Knowledge Technologies: Opportunities and ChallengesFariz Darari
How to be one step ahead of leveraging knowledge technologies for your apps!
When: Dec 8, 2017
Where: Fl. 6, Multimedia Tower, Central Jakarta
Thanks to Ragil for the invitation!
https://doi.org/10.6084/m9.figshare.11854626.v1
Presented at Dutch National Librarian/Information Professianal Association annual conference 2011 - NVB2011
November 17, 2011
Lecture at the advanced course on Data Science of the SIKS research school, May 20, 2016, Vught, The Netherlands.
Contents
-Why do we create Linked Open Data? Example questions from the Humanities and Social Sciences
-Introduction into Linked Open Data
-Lessons learned about the creation of Linked Open Data (link discovery, knowledge representation, evaluation).
-Accessing Linked Open Data
First steps towards publishing library data on the semantic webhorvadam
First steps towards publishing library data on the semantic web. Implementing:
CoolUri
RDFDC
SKOS
RDF database and SPARQL interface
Content negotiation
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
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The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
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unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
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4. What is an ontology ?
• Ontology = a specification of a
conceptualization (Gruber 1993)
• In practice: controlled vocabularies
– Disambiguation (e.g. Bank, Running)
– Language/species independence
• Very useful in biology – complex hierarchies of
terms
5. Ontologies in the bio Domain
• OBO Foundry - open Biological and
Biomedical Ontologies
• Common principles
• List of ontologies at
http://www.obofoundry.org
• OBO is also a data format .obo
6. SideTrack – The Gene Ontology
• The mother of bio-ontologies: the GO
– Oldest bio – ontology
– Many practical applications:
• Cross species studies
• Overrepresentation studies (RNASeq)
• GO is an OBO ontology
8. SideTrack – The Gene Ontology
• Relationships between terms:
– Subsumption: is_a
– Partonomic: part_of
• These terms are transitive
• Terms form a DAG (directed, acyclic graph)
• Some information can be inferred
16. • Representation of triples
– Basic data format: RDF/XML
– All data expressed in RDF (Resource Description
Framework)
– Several compatible syntaxes: TTL (Terse Triple
Language) most human readable
Resource Description Framework
22. IRI’s and Literals
• Terms can be either IRI’s, Literals or blank nodes
• IRI = Internationalized Resource Identifier
• Unique id – a virtual URI
– Example: <http://bioinformatics.be/terms#martijn>
– There is no requirement for resolving
– Now: Open Data initiatives: please do use resolvable
URI’s http://linkeddata.org
– Unique identifiers can be registered on
http://identifiers.org
23. Introduction
• Literals: can be typed, allowed types from the
XSD namespace:
– E.g. “This is a string example”^^xsd:string
– E.g. “5”^^xsd:integer
• IRI’s are used for entities and attributes
• Literals are used for attribute values that
aren’t entities
30. Graphs
• Triples are building blocks of Graphs
• Combining sets of triples allows the
construction of arbitrarily complex graphs
b4x:martijn b4x:karmeliethas_favorite_beer
34. Add meaning !
• Reuse terms from existing, well defined
vocabularies – ontologies (foaf, dc, go, so)
• Describe new terms = Ontologies
• Contain
– A crisp human definition
– Some machine readable facts
35. Metadata
• Ontologies are also described in RDF
– RDFS: RDF - Schema
– OWL: Web Ontology Language
– Also expressed in RDF
• For clarity, file extension can be .rdfs or .owl
40. Semantic Technologies
• Inference
– Enhance dataset using knowledge from metadata
(e.g. rdfs, owl)
• Types of inference engines
– RDFS inference
• RDFS entailment regime
– OWL inference
• Under active research
• Engines exist for specific subsets of OWL (OWL-DL)
44. DuckTyping
• Watch out with inference !
Example: You want to express that people can
have lengths
b4x:length a rdf:Property;
rdfs:domain foaf:Person;
rdfs:range xsd:integer.
45. DuckTyping
• Problem:
ex:VW_Transporter b4x:length “600”^xsd:integer.
• Would infer that VW_Transporter is a Person !
• This is called DuckTyping
If it looks like a duck, swims like a duck, and
quacks like a duck, then it probably is a duck
48. Storing RDF
• As an RDF file for download
• In a Triplestore
– Database optimised for storing triples
– Examples: BlazeGraph, Fuseki, Sesame
49. Semantic Technologies
• Querying over RDF data: SPARQL
• Cool features:
– Distributed querying = actual distribution of data
and computing resources
– SPARQL/Update: modify data
• SPARQL endpoints: SPARQL over HTTP
50. SPARQL Query Syntax
• First example:
SELECT ?subject ?predicate ?object WHERE {
?subject ?predicate ?object.
}
(Generally not a good idea as it will pull down
the whole dataset)
Binding variables
Graph matching
54. SPARQL Query Syntax
• Find all classes:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?class ?label WHERE {
?class a rdfs:Class.
?class rdfs:label ?label.
}
(This will only retrieve classes that have a label)
55. SPARQL Query Syntax
• Find all classes:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?class ?label WHERE {
?class a rdfs:Class.
OPTIONAL {
?class rdfs:label ?label.
}
}
56. SPARQL Query Syntax
• Find all classes that contain “duck” in the
label:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?class ?label WHERE {
?class a rdfs:Class.
?class rdfs:label ?label.
FILTER( CONTAINS (str(?label) , “duck” ) )
}
57. SPARQL Query Syntax
• Make it case insensitive:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?class ?label WHERE {
?class a rdfs:Class.
?class rdfs:label ?label.
FILTER( CONTAINS ( UCASE(str(?label)) , “DUCK” ) )
}
58. SPARQL Query Syntax
• Search in specific graph:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?class ?label
FROM <http://example.org/animals>
WHERE {
?class a rdfs:Class.
?class rdfs:label ?label.
FILTER( CONTAINS ( UCASE(str(?label)) , “DUCK” ) )
}
59. SPARQL Query Syntax
• Search in specific graph:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?class ?label WHERE {
GRAPH <http://example.org/animals> {
?class a rdfs:Class.
?class rdfs:label ?label.
FILTER( CONTAINS ( UCASE(str(?label)) , “DUCK” ) )
}
}
60. SPARQL Query Syntax
• Can also search for graphs :
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?g WHERE {
GRAPH ?g {
?class a rdfs:Class.
?class rdfs:label ?label.
FILTER( CONTAINS ( UCASE(str(?label)) , “DUCK” ) )
}
}
62. • Basic data element = a Triple
– A mini sentence
– Contains three Terms:
– Subject Predicate Object
• Example:
<http://xmpl/entities#martijn>
<http://xmpl/relations#has_favorite_beer>
<http://xmpl/entities#karmeliet>.
Take home Summary
67. Interoperability between OBO and
Semantic Technologies
• Originated from two separate academic worlds
• Computing applications of OBO mainly
consistency checking and overrepresentation
analysis
• Semantic Technologies: much broader toolset
• Interoperability ?
– Direct offering in both formats
– Automated mappings
• Migration towards semantic toolkits
68. Where to find ontologies
• OBO Foundry
• Bioportal; NCBO
• Biogateway
• Bio2RDF
69. Where to find RDF data
• Google for SPARQL endpoint
• => e.g. EBI databases
• Non biological: DBpedia
70. How about Tim Berners Lee’s vision
• We’re not there yet, but for bio data we’re
getting quite close
– The explicitome
– Crowd sourcing
– Nanopublications
81. • From a web interface
• Using http
– HTTP GET
– HTTP POST : for larger query strings
– Headers determine response type (JSON, XML, HTML)
http://…/sparql?default-graph-uri=<http://graphName>&query=URLENCODEDQUERYSTRING
Running SPARQL