Semantic Web in Practice

The Big Vision

Sir Tim Berners-Lee

The web of human-readable document is being merged with a web of machine understandable data. The potential of the mixture of humans and machines working together and communication through the web could be immense.

•  The Web as a huge decentralised database (knowledge base)

•  Machine-accessible data

Web of Data as an upgrade of The Web of Documents

A Web for Machines

Web of Data Web of Documents

Data can be interconnected with typed links, similarly as Web pages are connected with hyperlinks

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Now The Academic Age of the Semantic Web

Ontologies

Description Logic Rules

RDF

OWL

2001

Linked Data

Social Semantic Web

Semantic Open Data

Semantic Markup

Knowledge Graphs

SW Now: Semantic Open Data

Linked Open Data

Publishers: Governments, Institutions, Universities

Name things using URIs

Provide data over HTTP

Publish data using open structured data standards

Link to data published by others

SW Now: Semantic Open Data

DBPedia Semantic version of Wikipedia with rich data about over 3.5M concepts of human knowledge. Open Source. The core of the Web of data.

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SW Now: Knowledge Graphs

Big players have their own Knowledge Graphs

Google Knowledge Graph powers factual responses to Web searches.

Wolfram Alpha, that powers some of Siri searches, relies on own curated data sources.

Facebook Open Graph combines concepts of human knowledge and social data

SW Now: Semantic Markup

Provide metadata about things that you want people to like. Facebook integrates it in their Open Graph.

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H TML 5 microdata

Provide metadata about your Web pages and Google will index them better.

Main USES

Question Answering

Semantic Search

Semantic Recommender Systems

Semantic Marketing

Question Answering

Siri

Semantic Search

Facebook Graph Search

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Semantic Recommender Systems

Seevl uses data graphs to calculate artist similarity and recommend music. It helps users discover unknown artists that they may like, and does it better then standard collaborative-filtering systems.

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When  You  Believe  

Vocals  Aretha  Franklin  

Finding interesting paths in the graph instead of statistical co-

occurrence

Answer: , because Mary is buying vegetables for her vegetarian lunch

Semantic Marketing

?

Answer: because Mary’s basket contains same elements as Bob’s

C ol laborative Filtering (data-mining)

Mary

Bob

Semantic Marketing

User shopping baskets

Semantic Marketing helps develop better

understanding of users preferences and

motivations

Sépage puts to use the years of research and experience with using Semantic Graphs to produce Semantic Marketing solutions for the future of the Web, making it more smart and more adapted to growing users expectations.

Licence Attribution - Partage dans les Mêmes Conditions 3.0 France

Images Internet – Yellow from the Noun Project Document, The noun project Video, Dmitry Baranovskiy, The noun Project User, Lynn D. Christensen, The Noun Project Lemon, Claudio Gomboli, The Noun Project Carrot, Kate Vogel, Then Noun Project Cucumber, Ashwini Sukhdeve, The Noun Project Tomato, Okan Benn, The Noun Project Pumpkin, Adam Mullin, The Noun Project

Thank you

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