The Digital Cavemen of Linked Lascaux
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Transcript of The Digital Cavemen of Linked Lascaux
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The Digital Cavemenof Linked LascauxRuben Verborgh
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The Lascaux paintings are 17,300 years old.
How long will your records last?
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by Banksy
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by Moyan Brenn
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SUSTAINABILITY
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SUSTAINABILITYa threat to the Semantic Web
lack of a longterm plan for
=
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SUSTAINABILITYmaking promises you can keep
=
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SUSTAINABILITYa dialog becoming a contract
=
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SUSTAINABILITYremaining constant under change
=
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How can we promise to remain constant in a changing world?
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Changes
Constants
Promises
The Digital Cavemenof Linked Lascaux
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Changes
Constants
Promises
The Digital Cavemenof Linked Lascaux
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Changes
Data models
Technology
Interfaces
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Changes
Data models
Technology
Interfaces
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The oldest data model is a simple table.
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
van Hooland, S. and Verborgh, R. “Linked Data for Libraries, Archives and Museums” (Facet, 2014)
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Tables do not cope well with changes in data or schema.
Title Artist Born Died
The Thrill is Gone B. B. King 1925 2015
Riding with the King John Hiatt 1952
Riding with the King B. B. King 1925
… … … …
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Relational databases providea multi-dimensional table model.
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
van Hooland, S. and Verborgh, R. “Linked Data for Libraries, Archives and Museums” (Facet, 2014)
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Databases cope with data changesbut schema changes are harder.
Title ArtistThe Thrill is Gone 1
Riding with the King 2Riding with the King 1
… …
ID Name Born Died
1 B. B. King 1925 2015
2 John Hiatt 1952
… … … …
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There is no interoperabilitywith other databases.
Title ArtistThe Thrill is Gone 1
Riding with the King 2Riding with the King 1
… …
Wikipedia?
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XML allows reuse of schemasand identifiers.
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
van Hooland, S. and Verborgh, R. “Linked Data for Libraries, Archives and Museums” (Facet, 2014)
![Page 23: The Digital Cavemen of Linked Lascaux](https://reader033.fdocuments.in/reader033/viewer/2022051520/58f9b3b0760da3da068bd8a3/html5/thumbnails/23.jpg)
XML schema evolution remains a tough nut to crack.
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
?
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The RDF datamodel is flexiblefor changes in data and schema.
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
van Hooland, S. and Verborgh, R. “Linked Data for Libraries, Archives and Museums” (Facet, 2014)
![Page 25: The Digital Cavemen of Linked Lascaux](https://reader033.fdocuments.in/reader033/viewer/2022051520/58f9b3b0760da3da068bd8a3/html5/thumbnails/25.jpg)
RDF involves a trade-offbetween flexibility and reuse.
customontology
reuse ontologies
perfect match
perfect interoperability
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So far for change within models…what about change between them?
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
![Page 27: The Digital Cavemen of Linked Lascaux](https://reader033.fdocuments.in/reader033/viewer/2022051520/58f9b3b0760da3da068bd8a3/html5/thumbnails/27.jpg)
There’s no ultimate model.They co-exist. Change is inherent.
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
![Page 28: The Digital Cavemen of Linked Lascaux](https://reader033.fdocuments.in/reader033/viewer/2022051520/58f9b3b0760da3da068bd8a3/html5/thumbnails/28.jpg)
Changes
Data models
Technology
Interfaces
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Even if your data doesn’t change, technology does.
What happens to your data?
new software versions
new software manufacturers
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Is your softwareholding your data hostage?
Is your software the owner of your data?
Intentional or unintentional vendor lock-in?
Or are you?
Can you get your data out at any moment you want?
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The Cooper-Hewitt Design Museum had trouble getting their own data.
Data in The Museum System
flexible, but complex relational design
no export button
Website had more flexible demands
complex manual queries to liberate data
parallel CMS to drive website
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Changes
Data models
Technology
Interfaces
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The Web has been designedwith change in mind.
Individual links are allowed to breakso the entire Web does not.
—Tim Berners-Lee
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The Web is in rapid evolution but continues on working.
What year is it? Then your users need…
1995 – HTML 2.0
2000 – XML
2008 – JSON
2012 – HTML 5
2015 – RDF ?
2017 – … ?
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At least HTML seems constant,so the human Web is safe.
http://bib.org/books/978-1-85604-964-1/
around 2005: made in HTML 4
around 2015: made in HTML 5
Markup changes, the identifier does not.
Tim Berners-Lee called these “Cool URIs”.
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Web APIs for machines suffer from changes on many levels.
http://api.bib.org/v2/viewBookDetails.php?id=978-1-85604-964-1&format=json &apikey=WSDGU56VP
How does this identifier cope with change?
How long does this identifier work unchanged?
!
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http://api.bib.org/v2/viewBookDetails.php?id=978-1-85604-964-1&format=json &apikey=WSDGU56VP
!
!
!
Web APIs for machines suffer from changes on many levels.
dependency on server technology
dependency on API version
dependency on representation
dependency on API key
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Plenty of excuses exist to change machine interfaces.
But our new server does it faster!
But our new API has different features!
But XML is obsolete now so we need JSON!
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Even funnier are the excuses for requiring API keys.
But we need to rate limit!
But we need to track automated access!
But we need to protect our data!
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Once and for all: API keys do not help with these.
But we need to rate limit!
But we need to track automated access!
But we need to protect our data!
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Once and for all: API keys do not help with these.
Your HTML interface is still open!
JSON is a convenience, not a necessity.
Anybody can still do whatever they wantby scraping HTML pages with the same data.
Protect your data, not just one interface.
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Yet other possible changes still appear to be a concern.
Remain constant if your server changes?
Remain constant if your API changes?
Remain constant if data models change?
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Changes
Constants
Promises
The Digital Cavemenof Linked Lascaux
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Constants
URIs
Ontologies
Resources
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Constants
URIs
Ontologies
Resources
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The RDF model is drivenby unique identifiers.
S
O
P
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Constants allow clientsto establish a shared meaning.
S
O
P
http://bib.org/books/978-1-85604-964-1/
http://bib.org/authors/7356/
http://purl.org/dc/terms/creator
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Human semantics are in conceptsand their meaning to the world.
S
O
P
a book
a person
written by
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Machine semantics are in symbolsand their structural interrelations.
S
O
P
http://digybe.wpq/dgjyj-dgu7945
http://aole.wqq/mobd1.tihz
http://yudgy.jdu/DHH8DHBtkixhj
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We need to be very careful about our choice of symbols.
S
O
P
http://bib.org/books/978-1-85604-964-1/
http://bib.org/authors/7356/
http://purl.org/dc/terms/creator
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We need to be very careful about our choice of symbols.
http://bib.org/books/978-1-85604-964-1/
http://bib.org/authors/7356/
Is this a bookor a description of a book?
:printDate "2014-06-11":lastModified "2015-11-25"
Is this a person or a document?
:birthDate "1987-02-28":size "17kB"
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Although designed for machines,the example only works for humans.
S
O
P
http://bib.org/books/978-1-85604-964-1/
http://bib.org/authors/7356/
http://purl.org/dc/terms/creator
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Because, somehow, Web APIs make machine access different.
S
O
P
http://api.bib.org/v2/viewBookDetails.php?id=978-1-85604-964-1&format=json &apikey=WSDGU56VP
http://api.bib.org/v2/viewAuthorProfile.php?id=7356&format=json&apikey=WSDGU56VP
http://purl.org/dc/terms/creator
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That’s why it’s a problem ifmachines need different identifiers.
S
O
P
http://api.bib.org/v2/viewBookDetails.php?id=978-1-85604-964-1&format=json &apikey=WSDGU56VP
http://api.bib.org/v2/viewAuthorProfile.php?id=7356&format=json&apikey=WSDGU56VP
http://purl.org/dc/terms/creator
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Only this triple is a global constant.The other is volatile and local.
S
O
P
http://bib.org/books/978-1-85604-964-1/
http://bib.org/authors/7356/
http://purl.org/dc/terms/creator
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Constants
URIs
Ontologies
Resources
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Fortunately, we don’t have to pick all the constants ourselves.
Ontologies provide identifiers of concepts that are designed to be reused.
They are necessary to make RDF work.
They are necessary to create queries,especially over multiple datasources.
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Of course, we get the benefits only if we actually reuse.
Why have our own my:writtenBy property when dc:creator already exists?
Maybe we have a more specific meaning?
We can still relate both properties with RDF.
But if we all use derivatives of the constants,what is the value of these constants?
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Authors are not always in control: external semantic drift happens.
foaf:knows was bidirectional…
spec: “some level of reciprocity”
An foaf:knows Pete ⇒ Peter foaf:knows An
…until somebody modeled Twitter followers
Pete follows Angela Merkel ⇒ Pete knows Angela
Yet Angela doesn’t know Pete…
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Getting close to Derrida… but we’re not philosophers.
There are only two hard things in Computer Science:cache invalidation and naming things.
—Phil Karlton
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Constants
URIs
Ontologies
Resources
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The constants you can touch are the constants you can trust.
No matter how hard technology changes, the books we describe remain the same.
Any mechanism of identification should based on domain resources, not on inevitably changing technology.
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The “success” storyof the Web API community.
3 FOSTERING REUSABILITY THROUGH A SELF-DESCRIPTIVE BOTTOM-UP APPROACH
3 Fostering reusability through a self-descriptive bottom-up approach
Lacking better measurements, the Web api community has been heading the same quantity-over-quality course that hascharacterized the first years of the Linked Data initiative. An often-quoted fact in Web api papers and articles is the everincreasing number of Web apis (Figure 1), which is supposed to be an indicator of the ecosystem’s excellent health. How-ever, as Linked Data researchers have become painfully aware, quantity only loosely correlates with quality or usefulness.Perhaps for Web apis, the correlation between quantity and utility could even be negative. Few other communities wouldpride themselves on the existence of more than 12.000 di↵erent micro-protocols to achieve essentially the same thing:communicating between clients and servers over http. Of course, each application has its own domain and domain-specific vocabulary, but does that also warrant an entirely di↵erent way of exposing this, especially when we have rdf asa uniform data model? Each di↵erent api currently requires a di↵erent client, given the lack of a uniform api descriptionformat to explain the api’s response structure and functionality. Clearly, this approach to Web apis is a dead end.
2005 2007 2009 2011 2013 2015
Special.
1861,263
2,418
5,018
7,182
10,302
12,559
number of indexed Web ���s
Figure 1: The increasing number of Web apis is often named an indicator of their success, while the overgrowth of such custommicro-protocols is unnecessary—and detrimental to the development of generic Web api clients. (data: programmableweb.com)
In order for machines to use information autonomously, it has to be composed out of pieces they can recognize andinterpret. The rdf model achieves this by identifying each of the triple components by reusable iris, which have a meaningbeyond the scope that mentions them. Furthermore, the Linked Data principles mandate the use of httpurls, which turnthese components into a↵ordances toward relevant information. For instance, given the following rdf triple:
<http://dbpedia.org/resource/Bill_Clinton> <http://xmlns.com/foaf/0.1/knows>
<http://dbpedia.org/resource/Al_Gore>.
the knowledge of the foaf:knows predicate is su�cient for a machine to determine that this relation is symmetric, and thatdbpedia:Bill_Clinton and dbpedia:Al_Gore are instances of foaf:Person—even though it might have never encoun-tered any of those iris before. Furthermore, should the foaf:knows property be unfamiliar, its iri can be dereferenced tofind this information expressed in ontological predicates. Knowledge of these predicates in turn allows an interpretationof foaf:knows and hence the aforementioned derivation. We herein recognize two characteristics in particular:
• The information is structured in a bottom-up way: machines interpret a larger unit of information through its piecesinstead of interpreting the pieces through the whole (while humans are capable of doing both simultaneously).
• Each piece in the unit is self-descriptive: anything needed to interpret a piece is contained within itself, with its iriacting as both an identifier and a direct handle towards additional interpretation mechanisms. No external resourceis required beforehand, given the knowledge of a limited set of basic concepts.
This sharply contrasts with current practice for Web apis. Machines are assumed to interpret each api operation in its en-tirety, as such smaller pieces do not exist, and api descriptions—if present—are external documents that must be collectedand interpreted before consumption is possible. While this does not imply the inviability of such an approach, it raisesserious doubt as to whether that is the most e↵ective strategy towards automated Web api consumption by generic clients.
number of indexed Web APIs in ProgrammableWeb
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Just imagine we had15,000 different data models.
3 FOSTERING REUSABILITY THROUGH A SELF-DESCRIPTIVE BOTTOM-UP APPROACH
3 Fostering reusability through a self-descriptive bottom-up approach
Lacking better measurements, the Web api community has been heading the same quantity-over-quality course that hascharacterized the first years of the Linked Data initiative. An often-quoted fact in Web api papers and articles is the everincreasing number of Web apis (Figure 1), which is supposed to be an indicator of the ecosystem’s excellent health. How-ever, as Linked Data researchers have become painfully aware, quantity only loosely correlates with quality or usefulness.Perhaps for Web apis, the correlation between quantity and utility could even be negative. Few other communities wouldpride themselves on the existence of more than 12.000 di↵erent micro-protocols to achieve essentially the same thing:communicating between clients and servers over http. Of course, each application has its own domain and domain-specific vocabulary, but does that also warrant an entirely di↵erent way of exposing this, especially when we have rdf asa uniform data model? Each di↵erent api currently requires a di↵erent client, given the lack of a uniform api descriptionformat to explain the api’s response structure and functionality. Clearly, this approach to Web apis is a dead end.
2005 2007 2009 2011 2013 2015
Special.
1861,263
2,418
5,018
7,182
10,302
12,559
number of indexed Web ���s
Figure 1: The increasing number of Web apis is often named an indicator of their success, while the overgrowth of such custommicro-protocols is unnecessary—and detrimental to the development of generic Web api clients. (data: programmableweb.com)
In order for machines to use information autonomously, it has to be composed out of pieces they can recognize andinterpret. The rdf model achieves this by identifying each of the triple components by reusable iris, which have a meaningbeyond the scope that mentions them. Furthermore, the Linked Data principles mandate the use of httpurls, which turnthese components into a↵ordances toward relevant information. For instance, given the following rdf triple:
<http://dbpedia.org/resource/Bill_Clinton> <http://xmlns.com/foaf/0.1/knows>
<http://dbpedia.org/resource/Al_Gore>.
the knowledge of the foaf:knows predicate is su�cient for a machine to determine that this relation is symmetric, and thatdbpedia:Bill_Clinton and dbpedia:Al_Gore are instances of foaf:Person—even though it might have never encoun-tered any of those iris before. Furthermore, should the foaf:knows property be unfamiliar, its iri can be dereferenced tofind this information expressed in ontological predicates. Knowledge of these predicates in turn allows an interpretationof foaf:knows and hence the aforementioned derivation. We herein recognize two characteristics in particular:
• The information is structured in a bottom-up way: machines interpret a larger unit of information through its piecesinstead of interpreting the pieces through the whole (while humans are capable of doing both simultaneously).
• Each piece in the unit is self-descriptive: anything needed to interpret a piece is contained within itself, with its iriacting as both an identifier and a direct handle towards additional interpretation mechanisms. No external resourceis required beforehand, given the knowledge of a limited set of basic concepts.
This sharply contrasts with current practice for Web apis. Machines are assumed to interpret each api operation in its en-tirety, as such smaller pieces do not exist, and api descriptions—if present—are external documents that must be collectedand interpreted before consumption is possible. While this does not imply the inviability of such an approach, it raisesserious doubt as to whether that is the most e↵ective strategy towards automated Web api consumption by generic clients.
number of indexed Web APIs in ProgrammableWeb
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Find resources in your domain and assign them an identifier.
http://bib.org/books/978-1-85604-964-1/
http://bib.org/authors/7356/
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It’s just like building a web site.When a user comes, serve HTML.
http://bib.org/books/978-1-85604-964-1/
UGET
HTML
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It’s just like building a web site.When a client comes, serve JSON.
http://bib.org/books/978-1-85604-964-1/
CGET
JSON
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It’s just like building a web site.When a client comes, serve RDF.
http://bib.org/books/978-1-85604-964-1/
CGET
RDF
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Content negotiation exists for a long time in HTTP.
http://bib.org/books/978-1-85604-964-1/
CGET
RDF
Resource
Representation
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This allows constant URIseven with future changes.
http://bib.org/books/978-1-85604-964-1/
CGET
RDF 2.0
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It enables different users andmachines to talk about things.
http://bib.org/books/978-1-85604-964-1/
CU
C
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The best API is no API. Your website is already an API.
Developers like to build complicated APIs.
API keys are especially cool to build.
Every feature and change comes with a high cost.
If you ask for an API, you’ll get one.
Ask for new representations of your resources instead.
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Changes
Constants
Promises
The Digital Cavemenof Linked Lascaux
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Promises
Web Data
Integration
Scalability
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Promises
Web Data
Integration
Scalability
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The Semantic Web promiseddata on the Web.
85,567,007,302 triples from 3,426 datasets
LODStats
38,606,408,765 from 657,896 entries
LOD Laundromat
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How much of this datacan we readily access?
data dumps
Linked Data documents
SPARQL endpoints
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A data dump means downloading everything and querying locally.
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A data dump means downloading everything and querying locally.
When was the last timeyou downloaded the full Wikipedia just because you had one question?
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Dumps are not Web querying. It’s kind of like giving up.
Semantic Web ⇒ Semantic Basement?
What advantage do we havecompared to Big Data?
Still the RDF data model…
But the major difference is Web.
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Linked Data documents allow you to traverse a dataset.
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Linked Data documents allow you to traverse a dataset.
That’s similar to what we also do:consume information on Wikipedia by following links.
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Much Linked Data is availableusing the well-known principles.
Servers publish a light-weight interface.
Clients follow their noseto retrieve information.
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Linked Data documents allow query evaluation on the Web.
# Other books by the same author SELECT DISTINCT ?book WHERE { books:85604 dc:creator ?author. ?book dc:creator ?author. }
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Some queries are hardor impossible to evaluate.
# Books about Hamburg SELECT DISTINCT ?book ?author WHERE { ?book dc:subject dbpedia:Hamburg. ?book dc:creator ?author.}
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SPARQL endpoints allow you to ask any question you want.
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SPARQL endpoints allow you to ask any question you want.
When was the last timeyou expected Wikipedia to answer specific questions automatically for you?
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A public SPARQL endpoint happily answers this query.
# Other books by the same author SELECT DISTINCT ?book WHERE { books:85604 dc:creator ?author. ?book dc:creator ?author. }
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A public SPARQL endpoint also happily answers this query.
# Books about Hamburg SELECT DISTINCT ?book ?author WHERE { ?book dc:subject dbpedia:Hamburg. ?book dc:creator ?author.}
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A public SPARQL endpoint also happily answers this query…SELECT DISTINCT ?drug ?drug1 ?drug2 ?drug3 ?drug4 ?d1 WHERE { ?drug1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/drugCategory> <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugcategory/antibiotics> . ?drug2 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/drugCategory> <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugcategory/antiviralAgents> . ?drug3 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/drugCategory> <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugcategory/antihypertensiveAgents> . ?drug4 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/drugCategory> <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugcategory/anti-bacterialAgents> . ?drug1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/target> ?o1 . ?o1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/genbankIdGene> ?g1 . ?o1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/locus> ?l1 . ?o1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/molecularWeight> ?mw1 . ?o1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/hprdId> ?hp1 . ?o1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/swissprotName> ?sn1 . ?o1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/proteinSequence> ?ps1 . ?o1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/generalReference> ?gr1 . ?drug <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/target>?o1 . ?drug2 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/target> ?o2 . ?o1 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/genbankIdGene> ?g2 . ?o2 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/locus> ?l2 . ?o2 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/molecularWeight> ?mw2 . ?o2 <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/hprdId> ?hp2 .
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There’s a price to pay for beingthe most expressive HTTP interface.
The majority of public SPARQL endpoints has less than 95% uptime.
This means we cannot query themfor more than 1.5 days each month.
This means we cannot rely on themto build Linked Data applications.Buil-Aranda – Hogan – Umbrich – Vandenbussche SPARQL Web-Querying Infrastructure: Ready for Action?
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Promises
Web Data
Integration
Scalability
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The main promise of Linked Datais integration, preserving semantics.
1.1. INTRODUCTION 7
Tabular data Relational model
Meta-markup languages RDF
Each data item is structured asa line of field values. Fields arethe same for all items; a headerline can indicate their name.
Data are structured as tables, each ofwhich has its own set of attributes.Records in one table can relate to oth-ers by referencing their key column.
XML documents have a hierarchicalstructure, which gives them a tree-like appearance. Each element canhave one or more children; there isexactly one root element.
Each fact about a data item is expressedas a triple, which connects a subject toan object through a precise relationship.This leads to graph-structured data thatcan take any shape.
header
row
columnrelation
key column
attributes
table/entity
root
parent
child
siblings
propertysubject
object
Figure 1.1: Schematic comparison of the four major data models
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Integration is the promise. But does it work on the Web?
data dumps
Linked Data documents
SPARQL endpoints
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With data dumps, we justbuild a bigger basement.
How far do we go?
How do we keep data up to date?
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With Linked Data documents, we keep on following our nose.
There are no dataset boundaries.
Some queries will remain hard.
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With public SPARQL endpoints, problems become worse.
1 endpoint has 95% availability.
1.5 days down each month
2 endpoints have 90% availability.
3 days down each month
3 endpoints have 85% availability.
4.5 days down each month
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Promises
Web Data
Integration
Scalability
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Can we think differentlyabout Linked Data on the Web?
high server costlow server cost
datadump
SPARQLendpoint
high availability low availabilityhigh bandwidth low bandwidthout-of-date data live data
low client costhigh client cost
Linked Datadocuments
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Can we think differentlyabout Linked Data on the Web?
datadump
SPARQLendpoint
Linked Datadocuments
? ?
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Let us combine the lessons onchanges, constants, and promises.
An interface that withstands change,
simple enough so it doesn’t break
complex enough to query.
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Let us combine the lessons onchanges, constants, and promises.
Data dumps contain too much.
SPARQL endpoint results are too specific.
Linked Data documents are unidirectional.
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Each interface divides a dataset into Linked Data Fragments.
Data dumps: 1 huge fragment
SPARQL endpoints: ∞ specific fragments
Linked Data: 1 fragment per subject
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Can we find a new interfacewith a sustainable balance?
Triple Pattern Fragments: 1 fragment per subject / predicate / object
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Browse a dataset by triple pattern—no less, no more.
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Machines can accessthe exact same interface as RDF.
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Triple Pattern Fragments extendLinked Data documents with forms.
That’s even more similar to what we do: consume information on the Wikipedia by following links and using forms.
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Machines solve complex queries by breaking them down.
# Other books by the same author SELECT DISTINCT ?book WHERE { books:85604 dc:creator ?author. ?book dc:creator ?author. }
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Machines solve complex queries by breaking them down.
# Books about Hamburg SELECT DISTINCT ?book ?author WHERE { ?book dc:subject dbpedia:Hamburg. ?book dc:creator ?author.}
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Promises can be kept, becausethe interface is intelligently light.
Publishing Linked Data that can be queried on the Webis realistic because the workload is divided.
The server doesn’t even need a triplestore.
Since the client is in charge,querying multiple sources is easy.
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Promises are negotiated contracts so they always involve trade-offs.
Querying will be slower.
clients send many requests to answer a query
Query times are more consistent.
0.3 secs with a SPARQL endpoint… 95% of time
3 secs with Triple Pattern Fragments… 99.9% of time
Experiment with more complex interfaces.
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Make your Linked Data queryable on the Web.
Several open-source implementations: linkeddatafragments.org/software/
Query one or multiple sources online: client.linkeddatafragments.org
Example: bit.ly/harvard-hamburg
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Changes
Constants
Promises
The Digital Cavemenof Linked Lascaux
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Identify the constants,separate them from changes.
Satisfy Linked Data needs with promises you can keep.
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Simple enough to be usable,
complex enough to be useful.
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Sustainability meanspromising the simplestuseful complexity.
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@RubenVerborgh ruben.verborgh.org