This document provides an introduction to the semantic web presented by Aidan Hogan. It begins with various definitions and explanations of what the semantic web is, including comparisons to a machine readable web, standards, ontologies, linked data, and naming everything. It then discusses why the semantic web is needed due to the growing amount of data being produced and the inability of humans alone to understand it all. Finally, it provides examples of how the semantic web could help solve problems by linking related data together from various sources.
Very basic introductory talk about the Semantic Web, given to undergraduate and posgraduate students of Universidad del Valle (Cali, Colombia) in September 2010
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High Performance Machine Learning in R with H2OSri Ambati
This document summarizes a presentation by Erin LeDell from H2O.ai about machine learning using the H2O software. H2O is an open-source machine learning platform that provides APIs for R, Python, Scala and other languages. It allows distributed machine learning on large datasets across clusters. The presentation covers H2O's architecture, algorithms like random forests and deep learning, and how to use H2O within R including loading data, training models, and running grid searches. It also discusses H2O on Spark via Sparkling Water and real-world use cases with customers.
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This document discusses various techniques for finding and exploiting vulnerabilities during a penetration test when vulnerabilities are marked as "low" or "medium" in severity. It argues that penetration testers and clients should not rely solely on vulnerability scanners and should thoroughly investigate even lower severity issues. Specific techniques mentioned include exploiting default credentials on services like VNC, exploiting exposed admin interfaces found through tools like Metasploit, taking advantage of browsable directories with backups or other sensitive files, exploiting SharePoint misconfigurations, exploiting HTTP PUT or WebDAV configurations, exploiting Apple Filing Protocol, and exploiting trace.axd to view request details in .NET applications. The document emphasizes finding overlooked vulnerabilities and keeping "a human in the mix" rather than full reliance
Very basic introductory talk about the Semantic Web, given to undergraduate and posgraduate students of Universidad del Valle (Cali, Colombia) in September 2010
The document discusses the history and development of the World Wide Web, including how Sir Tim Berners-Lee created the first web server and client in 1990 at CERN, and how this allowed for a common language for sharing information between computers through HTML, URLs, and HTTP. It also covers how search engines like Google index billions of web pages through crawling, hashing techniques, building lexicons and inverted indexes to allow for efficient searching and ranking of results.
High Performance Machine Learning in R with H2OSri Ambati
This document summarizes a presentation by Erin LeDell from H2O.ai about machine learning using the H2O software. H2O is an open-source machine learning platform that provides APIs for R, Python, Scala and other languages. It allows distributed machine learning on large datasets across clusters. The presentation covers H2O's architecture, algorithms like random forests and deep learning, and how to use H2O within R including loading data, training models, and running grid searches. It also discusses H2O on Spark via Sparkling Water and real-world use cases with customers.
The document discusses various approaches to data analytics and common pitfalls. It provides examples of recommendation systems at Netflix and Pandora that achieved success by focusing on the business goals rather than just the technology. It also warns against complexifying systems and architectures unnecessarily over time and refusing to remove outdated components. Overall it advocates embracing complexity but also avoiding duct tape solutions, and designing systems with the intended use and business goals in mind rather than getting attached to specific technologies.
This document discusses various techniques for finding and exploiting vulnerabilities during a penetration test when vulnerabilities are marked as "low" or "medium" in severity. It argues that penetration testers and clients should not rely solely on vulnerability scanners and should thoroughly investigate even lower severity issues. Specific techniques mentioned include exploiting default credentials on services like VNC, exploiting exposed admin interfaces found through tools like Metasploit, taking advantage of browsable directories with backups or other sensitive files, exploiting SharePoint misconfigurations, exploiting HTTP PUT or WebDAV configurations, exploiting Apple Filing Protocol, and exploiting trace.axd to view request details in .NET applications. The document emphasizes finding overlooked vulnerabilities and keeping "a human in the mix" rather than full reliance
Presentation given during a tour of Australia, in May 2009. The targeted audience are people who are already familiar with the fundamentals of Semantic Web, and this presentation gives an overview of what is happening at W3C
Machine Learning for Smarter Apps - Jacksonville MeetupSri Ambati
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This document provides an overview of a web engineering course. The course objectives are to develop dynamic, database-driven websites using tools like PHP frameworks. Students will learn to create responsive interfaces, web applications with Laravel, and web services. The course consists of lectures, labs, assignments, and exams. Key topics include HTML, CSS, JavaScript, PHP, frameworks, and web development tools.
Mark Hughes Annual Seminar Presentation on Open Source Tracy Kent
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Node-RED is a visual tool for wiring together hardware devices, APIs, and online services to build IoT applications. It provides a browser-based drag-and-drop interface for creating flows that connect these different event sources. The lightweight runtime can run on edge devices or in the cloud and can be expanded through additional nodes. Node-RED uses flow-based programming and is open source with an active community.
All good things scale - ohs 2020 - 03.13.2020Amanda Wozniak
This document summarizes the key ingredients needed for open source hardware tools and projects to scale successfully. It discusses how tools like Eclipse and Git improved accessibility through intuitive user interfaces and collaboration features. It emphasizes that automation, testing, and accessible design principles are crucial to allow novices to contribute meaningfully. Open source projects also benefit from stable, mature platforms and libraries as well as from altruistic contributions of expertise. The document envisions continued progress in open source hardware tools that integrate hierarchical design, testing, and concurrent collaboration on schematics and PCBs.
EclipseCon France 2015 - Science TrackBoris Adryan
Software is increasingly playing a big part in scientific research, but in most cases the growth is organic. The life time of research software is often as short as the duration of a postdoctoral contract: Once the researcher moves on, custom-written niche code is frequently not well documented, components are not reusable, and the overall development effort is likely lost.
This is a case study in looking at the evolution of software for research in the field of genomics within my research group at the Department of Genetics at Cambridge University. While our research questions changed over the past decade, we moved from Perl code and regular expressions to R and statistical analysis, and from there to agent-based simulations in Java. Not only will I discuss the languages and tools used as well as the processes and how they have evolved over the years. It also covers the factors that influence the nature of the growth, such as funding, but also how 'open source' as a default has changed our development work. We also take a look into the future to see how we predict the software usage will grow.
Also, in presenting the problems and discussing possible solution, this talk will look at the role institutions play in helping address these issues. In particular the Software Sustainability Institute (SSI, http://software.ac.uk/) works in the UK to promote the development, maintenance and (re)use of research software.
The Eclipse Foundation, with the Science Working Group, works to facilitate software sharing and reuse. How can organisations like the SSI and Eclipse align their strategies and activities for maximum effect?
This document discusses how semantic web technologies like RDF and SPARQL can help navigate complex bioinformatics databases. It describes a three step method for building a semantic mashup: 1) transform data from sources into RDF, 2) load the RDF into a triplestore, and 3) explore and query the dataset. As an example, it details how Bio2RDF transformed various database cross-reference resources into RDF and loaded them into Virtuoso to answer questions about namespace usage.
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The document discusses creating semantic mashups by bridging Web 2.0 and the Semantic Web. It provides examples of how semantic data can be used flexibly across different domains and applications. The key benefits of semantic data include increased utility of applications by helping users complete tasks more easily, and allowing for greater efficiency by reusing existing data sources where possible.
Creating Semantic Mashups Bridging Web 2 0 And The Semantic Web Presentation 1jward5519
The document discusses creating semantic mashups by bridging Web 2.0 and the Semantic Web. It provides examples of how semantic data can be used flexibly from multiple sources to build applications and demonstrations. The key advantages of semantic data include increased utility of applications by helping users accomplish tasks more easily, and allowing for greater efficiency by reusing existing data sources where possible.
Open data is a crucial prerequisite for inventing and disseminating the innovative practices needed for agricultural development. To be usable, data must not just be open in principle—i.e., covered by licenses that allow re-use. Data must also be published in a technical form that allows it to be integrated into a wide range of applications. The webinar will be of interest to any institution seeking ways to publish and curate data in the Linked Data cloud.
This webinar describes the technical solutions adopted by a widely diverse global network of agricultural research institutes for publishing research results. The talk focuses on AGRIS, a central and widely-used resource linking agricultural datasets for easy consumption, and AgriDrupal, an adaptation of the popular, open-source content management system Drupal optimized for producing and consuming linked datasets.
Agricultural research institutes in developing countries share many of the constraints faced by libraries and other documentation centers, and not just in developing countries: institutions are expected to expose their information on the Web in a re-usable form with shoestring budgets and with technical staff working in local languages and continually lured by higher-paying work in the private sector. Technical solutions must be easy to adopt and freely available.
Intro to Machine Learning with H2O and AWSSri Ambati
Navdeep Gill @ Galvanize Seattle- May 2016
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Technical Challenges and Approaches to Build an Open Ecosystem of Heterogeneo...Ricard de la Vega
Echoes provides open, easy and innovative access to digital cultural assets from different institutions and is available in several languages. Within a single and integrated platform, users have access to a wide range of information on archaeology, architecture, books, monuments, people, photography etc. This can be explored using different criteria: concepts, digital objects, people, places and time. The platform can be installed for a region or a theme.
Echoes has developed tools that allow to analyze, clean and transform data collections to Europeana Data Model (EDM). Also tools to validate, enrich and publish heterogeneous data to a normalized data lake that can be exploited as linked open data and used with different data visualizations.
The document describes a semantic recommendation system for helping customers select fish for an aquarium. The system takes into account various criteria like temperature, predator/prey relationships between fish, food requirements, ecosystem needs, size, and color preferences. It integrates data from multiple sources and uses semantic technologies like ontologies and linked data to make personalized recommendations based on a user's needs and preferences. The system aims to connect people interested in fish keeping through a social network application.
SyrtAPI is a new entertainment platform that combines music and book data from multiple sources like MusicBrainz and NYTimes reviews. It uses Linked Data and SPARQL queries to extract lyrics and reviews and recommend songs that match the content of the books. The team learned about using Linked Data vocabularies and linking datasets while building the prototype, which currently retrieves 25 lyrics and 10 book reviews through its pipeline. Future work includes adding more data sources, developing a mobile app, and using natural language processing to better analyze texts.
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This is a case study in looking at the evolution of software for research in the field of genomics within my research group at the Department of Genetics at Cambridge University. While our research questions changed over the past decade, we moved from Perl code and regular expressions to R and statistical analysis, and from there to agent-based simulations in Java. Not only will I discuss the languages and tools used as well as the processes and how they have evolved over the years. It also covers the factors that influence the nature of the growth, such as funding, but also how 'open source' as a default has changed our development work. We also take a look into the future to see how we predict the software usage will grow.
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
31. The
Dessert
Problem
• Requirements:
– Must
be
a
dessert
– Must
be
citrus-‐free
– Must
be
gluten-‐free
– Ingredients
available
in
local
supermarket(s)
– Cheap
(students)
– Must
be
delicious
33. Dessert
Algorithm:
Naive
candidates := ∅
for all recipe-‐site in google-‐results
for all dessert-‐recipe in recipe-‐site
if dessert-‐recipe type looks-‐delicious
suitable := true
for all ingredient in dessert-‐recipe
if searchNutri5on(ingredient) type wheat or lemon or lime …
suitable := false
else if searchShops(ingredient) type null or expensive
suitable := false
end
end
if suitable add dessert-‐recipe to candidates end
end
end
end
return candidates
34. candidates := ∅
for all safe-‐cheap-‐local-‐dessert-‐recipe in magical-‐sem-‐web-‐results()
if safe-‐cheap-‐local-‐dessert-‐recipe type looks-‐delicious
add safe-‐cheap-‐local-‐dessert-‐recipe to candidates end
end
end
return candidates
Dessert
Algorithm:
Utopia
38. Music!
38
• Provision
of
a
music-‐based
portal.
• Bring
together
a
number
of
disparate
components
of
data-‐oriented
content:
1. Musical
content
(streaming
data
&
downloads)
2. Music
and
arIst
metadata
3. Review
content
4. Visual
content
(pictures
of
arHsts
&
albums)
41. Internet
41
Source:
hAp://www.evoluHonodheweb.com
The
growth
of
the
Internet
42. • There
is
a
wealth
of
informaHon
on
the
Web.
• It
is
aimed
mostly
towards
consumpHon
by
humans
as
end-‐users:
• Recognize
the
meaning
behind
content
and
draw
conclusions,
• Infer
new
knowledge
using
context
and
• Understand
background
informaHon.
The
Web
42
43. The
Web
43
• Billions
of
diverse
documents
online,
but
it
is
not
easily
possible
to
automaHcally:
• Retrieve
relevant
documents.
• Extract
informaHon.
• Combine
informaHon
in
a
meaningful
way.
• Idea:
• Also
publish
machine
processible
data
on
the
web.
• Formulate
quesHons
in
terms
understandable
for
a
machine.
• Do
this
in
a
standardized
way
so
machines
can
interoperate.
• The
Web
becomes
a
Web
of
Data
• This
provides
a
common
framework
to
share
knowledge
on
the
Web
across
applicaHon
boundaries.
44. The
Web:
Evolution
44
Web
of
Documents
Web
of
Data
"Documents"
Hyperlinks
Typed
Links
"Things"
46. Web
Technology
Basics
46
HTML
–
HyperText
Markup
Language
• Language
for
displaying
web
pages
and
other
informaHon
in
a
web
browser.
• HTML
elements
consist
of
tags
(enclosed
in
angle
brackets),
aOributes
and
content.
HTTP
–
HyperText
Transfer
Protocol
• FoundaHon
of
data
communicaHon
for
the
WWW.
• Client-‐server
protocol.
• Every
interacHon
is
based
on:
request
and
response.
47. Web
Technology
Basics
47
Uniform
Resource
Identifier
(URI)
• Compact
sequence
of
characters
that
idenHfies
an
abstract
or
physical
resource.
• Examples:
ldap://[2001:db8::7]/c=GB?objectClass?one
mailto:John.Doe@example.com
news:comp.infosystems.www.servers.unix
tel:+1-‐816-‐555-‐1212
telnet://192.0.2.16:80/
urn:oasis:names:specification:docbook:dtd:xml:4.1.2
http://dbpedia.org/resource/Karlsruhe
50. Semantics
on
the
Web
50
SemanHc
Web
Stack
Berners-‐Lee
(2006)
SyntaHc
basis
Basic
data
model
Simple
vocabulary
(schema)
language
Expressive
vocabulary
(ontology)
language
Query
language
ApplicaHon
specific
declaraHve-‐knowledge
Digital
signatures,
recommendaHons
Proof
generaHon,
exchange,
validaHon
51. Semantics
on
the
Web
51
SemanHc
Web
Stack
Berners-‐Lee
(2006)
RDF
–
Resource
Description
Framework
52. Semantics
on
the
Web
52
RDF
–
Resource
Description
Framework
• RDF
is
the
basis
layer
of
the
SemanHc
Web
stack
‘layer
cake’.
• Basic
building
block:
RDF
triple.
• Subject
–
a
thing
(idenHfied
by
URI)
• Predicate
–
a
relaHonship
(idenHfied
by
URI)
• Object
–
another
thing
(idenHfied
by
URI)
or
a
literal
• Subject
has
relaHonship
Predicate
to
Object
• (Tom_Scholz
has
relaHonship
lead_singer
to
Boston)
53. Why
URIs?
53
RDF
–
Resource
Description
Framework
• Subject
has
relaHonship
Predicate
to
Object
• (Tom_Scholz
has
relaHonship
lead_singer
to
Boston)
• URIs:
– Avoid
ambiguity!
– Enable
linking
(discussed
later)
– Enable
dereferencing
(discussed
later)
?
?
54. Semantics
on
the
Web
54
RDF
–
Resource
Description
Framework
(Example)
<http://musicbrainz.org/artist/b10bbbfc-‐
cf9e-‐42e0-‐be17-‐e2c3e1d2600d#_>
<http://www.w3.org/2002/07/owl#sameAs>
<http://dbpedia.org/resource/The_Beatles>.
<http://musicbrainz.org/artist/b10bbbfc-‐
cf9e-‐42e0-‐be17-‐e2c3e1d2600d#_>
<http://xmlns.com/foaf/0.1/name>
"The
Beatles"
.
URIs
are
given
in
angle
brackets
in
N-‐Triples.
Literals
are
given
in
quotes
in
N-‐Triples.
In
N-‐Triples
every
statement
is
terminated
with
a
full
stop.
55. Semantics
on
the
Web
55
RDF
Graphs
• Every
set
of
RDF
asserHons
can
then
be
drawn
and
manipulated
as
a
(directed
labelled)
graph:
• Resources
–
the
subjects
and
objects
are
nodes
of
the
graph.
• Predicates
–
each
predicate
use
becomes
a
label
for
an
arc,
connecHng
the
subject
to
the
object.
Subject
Object
Predicate
56. RDF
Graphs
(Example)
56
<http://musicbrainz.org/artist/b10bbbfc-‐cf9e-‐42e0-‐be17-‐e2c3e1d2600d#_>
<http://dbpedia.org/resource/The_Beatles>
"The
Beatles"
Semantics
on
the
Web
<http://www.w3.org/2002/07/owl#sameAs>
<http://xmlns.com/foaf/0.1/name>
57. RDF
Graphs
(Example)
57
<http://musicbrainz.org/artist/b10bbbfc-‐cf9e-‐42e0-‐be17-‐e2c3e1d2600d#_>
<http://www.w3.org/2002/07/owl#sameAs>
<http://xmlns.com/foaf/0.1/name>
<http://dbpedia.org/resource/The_Beatles>
"The
Beatles"
Semantics
on
the
Web
<http://www.w3.org/1999/02/22-‐rdf-‐syntax-‐ns#type>
<http://dbpedia.org/ontology/Music_Artist>
58. Semantics
on
the
Web
58
RDF
Blank
Nodes
• RDF
graphs
can
also
contain
unidenHfied
resources,
called
blank
nodes:
• Blank
nodes
can
group
related
informaHon,
but
their
use
is
oden
discouraged
[]
<hOp://www.w3.org/
2003/01/geo/
wgs84_pos#Point>
<http://www.w3.org/1999/02/
22-‐rdf-‐syntax-‐ns#type>
<hAp://www.w3.org/
2003/01/geo/
wgs84_pos#lat>
<hAp://www.w3.org/
2003/01/geo/
wgs84_pos#long>
49.005
8.386
59. Semantics
on
the
Web
59
RDF
Turtle
• Turtle
is
a
syntax
for
RDF
more
readable.
• Since
many
URIs
share
same
basis
we
use
prefixes:
@prefix
rdf:<http://www.w3.org/1999/02/22-‐rdf-‐syntax-‐ns#>.
@prefix
rdfs:<http://www.w3.org/2000/01/rdf-‐schema#>.
@prefix
owl:<http://www.w3.org/2002/07/owl#>.
@prefix
mo:<http://purl.org/ontology/mo/>.
@prefix
dbpedia:<http://dbpedia.org/resouce/>.
And
(someHmes)
a
unique
base:
@base
<http://musicbrainz.org/>.
60. Semantics
on
the
Web
60
RDF
Turtle
• Also
has
a
simple
shorthand
for
class
membership:
@base
<http://musicbrainz.org/>.
@prefix
mo:<http://purl.org/ontology/mo/>.
<artist/b10bbbfc-‐cf9e-‐42e0-‐be17-‐e2c3e1d2600d#_>
a
mo:MusicGroup.
Is
equivalent
to:
<http://musicbrainz.org/artist/b10bbbfc-‐cf9e-‐42e0-‐be17-‐e2c3e1d2600d#_>
<http://www.w3.org/1999/02/22-‐rdf-‐syntax-‐ns#type>
<http://purl.org/ontology/mo/MusicGroup>.
61. RDF
Turtle
• When
mulHple
statements
apply
to
same
subject
they
can
be
abbreviated
as
follows:
<artist/b10bbbfc-‐cf9e-‐42e0-‐be17-‐e2c3e1d2600d#_>
rdfs:label
"The
Beatles";
owl:sameAs
dbpedia:The_Beatles
,
<http://www.bbc.co.uk/music/artists/
b10bbbfc-‐cf9e-‐42e0-‐be17-‐e2c3e1d2600d#artist>
.
Same
subject
&
predicate
Semantics
on
the
Web
61
Same
subject
62. RDF
Turtle
• Turtle
also
provides
a
simple
syntax
for
datatypes
and
language
tags
for
literals,
respecHvely:
<recording/5098d0a8-‐d3c3-‐424e-‐9367-‐1f2610724410#_>
a
mo:Signal;
rdfs:label
"All
You
Need
Is
Love"
;
mo:duration
"PT3M48S"^^xsd:duration
.
dbpedia:The_Beatles
dbpedia-‐owl:abstract
"The
Beatles
were
an
English
rock
band
formed
(…)
"@en,
"The
Beatles
waren
eine
briHsche
Rockband
in
den
(…)
"@de
.
Semantics
on
the
Web
62
63. Semantics
on
the
Web
63
RDF/XML
• Common
misconcepHon:
RDF
is
not
exoHc
XML!
– RDF
is
triples!
• RDF/XML
widespread,
but
not
very
nice
<mo:MusicArtist>
rdf:about="http://musicbrainz.org/artist/b10bbbfc-‐cf9e-‐42e0-‐be17-‐e2c3e1d2600d#_">
<foaf:name>The
Beatles</foaf:name>
<owl:sameAs
rdf:resource="http://dbpedia.org/resource/The_Beatles"/>
</mo:MusicArtist>
64. Describing
Data
64
Vocabularies
• CollecHons
of
defined
properIes
and
classes
of
resources.
• Classes
group
together
similar
resources.
• mo:MusicArIst,
foaf:Person,
geo:Point
• ProperHes
denote
relaHonships
• mo:member,
foaf:name,
rdf:type
• Terms
from
well-‐known
vocabularies
should
be
reused
wherever
possible.
• New
terms
should
only
be
defined
when
you
cannot
find
required
terms
in
exisHng
vocabularies.
65. Describing
Data
65
Vocabularies
A
set
of
well-‐known
vocabularies
has
evolved
in
the
SemanHc
Web
community.
Some
of
them
are:
Vocabulary
DescripIon
Classes
and
RelaIonships
Friend-‐of-‐a-‐Friend
(FOAF)
Vocabulary
for
describing
people.
foaf:Person,
foaf:Agent,
foaf:name,
foaf:knows,
foaf:member
Dublin
Core
(DC)
Defines
general
metadata
aAributes.
dc:FileFormat,
dc:MediaType,
dc:creator,
dc:descripHon
SemanHcally-‐Interlinked
Online
CommuniHes
(SIOC)
Vocabulary
for
represenHng
online
communiHes.
sioc:Community,
sioc:Forum,
sioc:Post,
sioc:follows,
sioc:topic
Music
Ontology
(MO)
Provides
terms
for
describing
arHsts,
albums
and
tracks.
mo:MusicArHst,
mo:MusicGroup,
mo:Signal,
mo:member,
mo:record
Simple
Knowledge
OrganizaHon
System
(SKOS)
Vocabulary
for
represenHng
taxonomies
and
loosely
structured
knowledge.
skos:Concept,
skos:inScheme,
skos:definiHon,
skos:example
66. Describing
Data
66
Vocabularies
More
extensive
lists
of
well-‐known
vocabularies
are
maintained
by:
• W3C
SWEO
Linking
Open
Data
community
project
hAp://
www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/CommonVocabularies
• Mondeca:
Linked
Open
Vocabularies
hAp://labs.mondeca.com/dataset/lov
• Library
Linked
Data
Incubator
Group:
Vocabularies
in
the
library
domain
hAp://www.w3.org/2005/Incubator/lld/XGR-‐lld-‐vocabdataset-‐20111025
68. Later
on
...
68
SemanHc
Web
Stack
Berners-‐Lee
(2006)
RDF-‐S
–
RDF
Schema
69. Later
on
...
69
SemanHc
Web
Stack
Berners-‐Lee
(2006)
OWL
–
Web
Ontology
Language
70. Later
on
...
70
SemanHc
Web
Stack
Berners-‐Lee
(2006)
SPARQL
–
*
Protocol
and
RDF
Query
Language
71. Acknowledgements
• Alexander
Mikroyannidis
• Alice
CarpenHer
• Andreas
Harth
• Andreas
Wagner
• Andriy
Nikolov
• Barry
Norton
• Daniel
M.
Herzig
• Elena
Simperl
• Günter
Ladwig
• Inga
Shamkhalov
• Jacek
Kopecky
• John
Domingue
• Juan
Sequeda
• Kalina
Bontcheva
• Maria
Maleshkova
• Maria-‐Esther
Vidal
• Maribel
Acosta
• Michael
Meier
• Ning
Li
• Paul
Mulholland
• Peter
Haase
• Richard
Power
• Steffen
Stadtmüller
71
People
who
have
contributed
to
creaHng
Euclid
training
content: