Five more reasons to lean on a world-class semantic platform

5

About this webinar

The power of knowledge graphs

Better Together: Unstructured and Structured Data

1

3

2PoolParty Platform: Overview

4 PoolParty 5: What’s new?

What’s next?The Linked Data Path5

About

Semantic Web Company founded in 2001

25 experts in linked data technologies

PoolParty Platform launched in 2009

Serving customers from all over the world

EU- & US-based consulting services

Andreas Blumauer, MScITCEO of Semantic Web Company

Find me/us on:

https://www.linkedin.com/in/andreasblumauer

https://ablvienna.wordpress.com/

http://www.slideshare.net/semwebcompany

https://twitter.com/semwebcompany

Our network: Customers & Partners

Some of our Customers

● Credit Suisse● Boehringer Ingelheim● Roche● Wolters Kluwer● BMJ Publishing Group● Canadian Broadcasting Corporation (CBC)● World Bank Group● Inter-American Development Bank (IADB)● International Atomic Energy Agency (IAEA)● The Pokémon Company● Healthdirect Australia● Ministry of Finance (A)● Wood Mackenzie● Red Bull Media House● Council of the E.U.● TC Media● American Physical Society● Education Services Australia● Pearson● Norwegian Directorate of Immigration● REEEP● Techtarget

Finance / Automotive / Publisher / Health Care / Public Administration / Energy / Education

Selected Partners

● EBCONT● EPAM Systems● iQuest● PwC● Tenforce● OpenLink Software● Ontotext● MarkLogic● Gravity Zero● Altotech● Wolters Kluwer● Taxonomy Strategies● Digirati

We all have one goal in mind: Make machines smart enough so that they can help us to find those needles in the haystack, which are really relevant to us.

The power ofknowledge graphs.

1

The power of knowledge graphs: Agility, flexibility, complexity

doc doc doc

Norway France Austria Canada

doc

Norway France Austria Canada

doc

Show me all documents about

European countriesTraditional approach Graph-based approach

doc doc doc

The power of knowledge graphs:Agility, flexibility, complexity

doc doc doc

Europe,Norway

Europe,France

Europe,Austria

America,Canada

doc

Norway France Austria Canada

doc

Show me all documents about

European countries

Europe

Traditional approach Graph-based approach

doc doc doc

The power of knowledge graphs:Agility, flexibility, complexity

doc doc doc

Europe,Norway

Europe,France

Europe,Austria

America,Canada

doc

Norway France Austria Canada

doc

Show me all documents about

European countries

Europe

Traditional approach Graph-based approach

Show me all documents about EU

member countries

doc doc doc

Norway France Austria Canada

The power of knowledge graphs:Agility, flexibility, complexity

doc doc doc

Europe,Norway

EU,Europe,France

EU,Europe,Austria

America,Canada

doc doc doc doc doc

Show me all documents about

European countries

Europe

Traditional approach Graph-based approach

Show me all documents about EU

member countries

EU

Norway France Austria Canada

The power of knowledge graphs:Agility, flexibility, complexity

doc doc doc

Europe,Norway

French,EU,

Europe,France

EU,Europe,Austria

French,America,Canada

doc doc doc doc doc

Show me all documents about

European countries

Europe

Traditional approach Graph-based approach

Show me all documents about EU

member countries

French-speaking?

French-speaking

EU

Norway France Austria Canada

The power of knowledge graphs:Agility, flexibility, complexity

doc doc doc

Europe,Norway

French,EU,

Europe,France

EU,Europe,Austria

French,America,Canada

doc doc doc doc doc

Show me all documents about

European countries

Europe

Traditional approach Graph-based approach

Show me all documents from EU member countries

French-speaking?

French-speaking

EU

Metadata per document1. No or little network effects2. No reuse of metadata3. Metadata resides in silos4. Data quality hard to measure5. Not machine-readable

Knowledge about metadata1. Explicit knowledge models2. Reusable and measurable3. Metadata is machine-processable4. Standards-based metadata5. Linkable metadata opens silos

Information integration: Healthdirect Australia

Over 120 information partners and sources

Great variety of category and metadata systems

One central vocabulary hub:Australian Health Thesaurus (AHT)

Single point of access incl. harmonized search facets:

http://www.healthdirect.gov.au/

Semantic as a Service

2

PoolParty Platform: Overview

PoolParty core components

Bain Capital is a venture capital company based in Boston, MA.Since inception it has invested in hundreds of companies including AMC Entertainment, Brookstone, and Burger King. The company was co-founded by Mitt Romney.

See how it works:PoolParty components & workflows

works on

basis for

● reference taxonomies● linked data sources● text reference corpora

enrich

basis for

Taxonomist/Ontologist

Developer

● Confluence, WordPress SharePoint, Drupal, ...

● search engine● database

is user ofContentManager

enrich annotate

basis foranalyzesuses API

PoolParty core components

Create and maintaintaxonomies and knowledge graphs

“URIfication” of all types of things

Linking and mapping between taxonomies and various graphs

Access and integrate knowledge graphs via API (Read/Write)

Support of collaborative workflows around taxonomy management

RDFization of text

Extraction of ‘things’ (entities) from text based on graphs

Extraction of (free) terms and phrases from any kind of text

Categorization of documents

Ultra-fast auto completion based on controlled vocabularies

Integrated with highly scalable graph databases

Integration layer to improve content and collaboration systems

Existing integrations with SharePoint, Drupal, Confluence

Provision of user-dialogues to support semi-automatic tagging

URI-based Semantic Indexing

Faceted search based on taxonomies

Content recommender services

Better Together: Unstructured and Structured Data.3

Towards a Linked Data based search

Bringing structure to text: PoolParty GraphSearch

PoolParty Enterprise Server + GraphSearch System Architecture

Classified documents +

Taxonomy

● Structured autocomplete● Precise Search facets● Dynamic topic pages● Fully RDF compatible● All queries via SPARQL

sdsdd

ssdsd

asdsd

sadsa

ddsds

sdsddssdsd asdsdsadsaddsds

sdsddssdsd asdsdsadsaddsds

sdsddssdsd asdsdsadsaddsds

sdsddssdsd asdsdsadsaddsds

sdsddssdsd asdsdsadsaddsds

Complex Queries based on Linked Data

SELECT DISTINCT ?personname ?picture ?countryname ?hdi ?picture

WHERE

{

?person skos:prefLabel ?personname .

?country skos:prefLabel ?countryname .

?person a dbpedia:Person .

?country a dbpedia:Country .

?person skos:related ?country .

?country <http://dbpedia.org/property/hdi> ?hdi .

FILTER ( ?hdi < 0.6)

OPTIONAL

{

?person foaf:depiction ?picture .

}

} ORDER BY DESC(?hdi)

I want to explore medical research trends in relation to regional prosperity.

Organizing data in graphs and using links

Graph nervous_system_diseases-abstracts

Graph en.dbpedia.org

Graph www.nlm.nih.gov/mesh

Graph www.geonames.org

PoolParty Semantic IntegratorSystem Architecture

Classified documents + Linked taxonomies +Knowledge graphs

● Dynamic filter criterias● BI-like interface● Large scale RDF store● Fully RDF compatible● All queries via SPARQL

saddsd

s

sadd

sds

adsaddsds

dsad

dsds

saddsds

dsad

dsds

saddsd

s

sadd

sds

adsaddsds

dsad

dsds

saddsds

dsad

dsds

What’s new?

4

5

Five more reasons to get started with PoolParty. Try it out now!

Get your PoolParty 5 Thesaurus Server & Entity Extractor trial:

http://www.poolparty.biz/test-demo/

Highly precise entity extraction

Domain-specific extraction, highly performant, language-agnostic, disambiguation rules, REST API

Why Disambiguation is important?Example

Brian Johnson (born 5 October 1947) is an English singer and songwriter. Since 1980, he has been the lead singer of Australian rock band AC/DC, with whom he was inducted into the Rock and Roll Hall of Fame in 2003.

Brian Johnson (born December 7, 1990) is an American professional baseball pitcher in the Boston Red Sox organization. Johnson was part of the No. 1 recruiting class by Baseball America at the University of Florida

Brian Johnson?

Brian Johnson

Brian Johnson

Brian Johnson in the news● http://www.pressherald.com/2015/02/08/on-baseball-low-flying-johnson-on-red-sox-radar/● http://bosoxinjection.com/2015/01/24/red-sox-prospect-brian-johnson-undervalued-asset/

● http://www.blocku.com/2015/2/8/8002765/early-look-at-utah-footballs-2016-recruiting-class● http://www.ksl.com/?nid=635&sid=33335577

● http://www.nydailynews.com/entertainment/music/ac-dc-singer-teleprompter-grammys-opening-article-1.2107723● http://ultimateclassicrock.com/acdc-bon-scott-final-show/

Providing context in the knowledge graph

Activating disambiguation

Deep integration with SharePoint, Drupal & Confluence

Semi-automatic tagging, semantic search integration, content recommendation - all based on your own enterprise vocabularies!

Semantic Search integrated with Confluence

PoolParty Integration with Confluence Blueprints

⇒ Solution for Semantic Records Management

Fully integrated web crawler

Make use of text corpus analysis: Retrieve documents from various sources, like RSS or from websites

Web Crawler extracts candidate terms from any website

Refined look & feel

Refined look & feel improves user experience and usability. Enjoy working with PoolParty!

Full-blown ontology management & semantic reasoning

From SKOS taxonomies to full-blown ontologies: PoolParty supports various levels of knowledge modeling

From Simple SKOS to large knowledge graphs

your data, e.g. Excel

your docs

Generate 1st version of SKOS taxonomy

Edit,extend & curate taxonomy

Extend schema, apply ontologies, use SKOS-XL

Link and map between taxonomies and LD graphs

- Taxonomy Editing- Collaborative workflows- Free term extraction- Tag recommender- Quality Checker

- Reuse of existing vocabularies- Corpus Analysis- Excel import- XML import- Linked data harvester

your CMS

- Reuse existing ontologies- Create custom schemes- Apply SKOS-XL- Apply ontologies on your SKOS taxonomy

- Automatic mapping between taxonomies- Linked Data frontend- Link to other LD graphs, e.g. DBpedia or Geonames

What’s next - the “Linked Data Path”

1Build capacities for internalknowledge about linked data

4 DevelopKnowledge Models

2Identify & understand strategic dimension of linked data

3Define crisp & feasible use cases

5 Put aspect of 'reusability of information' in the Centre

7 Implementation of the use cases with focus on learning curve of knowledge carrier

6Focus on central use case: integrated views on business objects

8Communicate the strategic

dimension of linked data

Dive into your Data Pools.