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PoolParty Semantic Platform - Overview
The document provides an overview of the Semantic Web Company (SWC), founded in 2001, and its product PoolParty, which focuses on linked data technologies. It highlights the company's client list, including notable organizations like Credit Suisse and the World Bank, as well as the capabilities of PoolParty in managing taxonomies, knowledge graphs, and content integration across various platforms. Additionally, it emphasizes the advantages of using knowledge graphs for improved information retrieval, data modeling, and semantic content management.
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PoolParty Semantic Platform - Overview
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- 2. About Semantic WebCompany (SWC) SWC was founded 2001 in Vienna, Austria Over 20 experts in linked data technologies Product: PoolParty Suite (launched 2009) Serving customers from all over the world EU- & US-based consulting services
- 3. Our network: Customers& Partners Customers ● Credit Suisse ● Daimler ● Roche ● Wolters Kluwer ● Tieto ● Canadian Broadcasting Corporation (CBC) ● World Bank Group ● 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 ● Techtarget ● Norwegian Directorate of Immigration ● REEEP ● European Commission ● Bank of America Finance / Automotive / Publisher / Health Care / Public Administration / Energy / Education Partners ● Cognizant ● EBCONT ● EPAM Systems ● iQuest ● PwC ● DTI AG ● Tenforce ● OpenLink Software ● Ontotext ● MarkLogic ● Gravity Zero ● Altotech ● Wolters Kluwer ● Term Management ● Taxonomy Strategies ● Search explained ● WAND ● Digirati ● Cognistreamer ● Linked Data Factory ● Taxonic ● semweb
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- 5. PoolParty core components Createand maintain taxonomies and knowledge graphs “URIfication” of all types of things Linking and mapping between taxonomies and various graphs Access knowledge graphs via API (Read/Write) Support of collaborative workflows around taxonomy management Extraction of ‘things’ (entities) from any kind of text based on knowledge graphs Extraction of (free) terms from any kind of text Categorization of text Ultra-fast auto completion based on knowledge graphs 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 Semantic indexing based on URIs, faceted search based on taxonomies Content recommender services
- 6. See how itworks: 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 ofContent Manager enrich annotate basis for analyzes uses API
- 7. From Simple SKOSto 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
- 9. ‘Things’ but notstrings: Building a ‘semantic knowledge graph’ http://www.mycom.com/ taxonomy/62346723 prefLabel Venice image http://www.mycom.com/ images/90546089 http://www.mycom.com/ taxonomy/97345854 prefLabel St. Mark’s Square altLabel Piazza San Marco http://www.mycom.com/t axonomy/4543567 prefLabel altLabel city square piazza has broader
- 10. The power ofknowledge graphs: Agility, flexibility, complexity doc doc doc Norway France Austria Canada doc Norway France Austria Canada doc Show me all documents about European countries Traditional approach Graph-based approach doc doc doc
- 11. The power ofknowledge 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 from European countries Europe Traditional approach Graph-based approach Show me all documents about EU member countries doc doc doc
- 12. Norway France AustriaCanada 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 from European countries Europe Traditional approach Graph-based approach Show me all documents about EU member countries EU
- 13. Norway France AustriaCanada 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 from European countries Europe Traditional approach Graph-based approach Show me all documents about EU member countries French- speaking? French- speaking EU
- 14. Norway France AustriaCanada 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 from European countries Europe Traditional approach Graph-based approach Show me all documents from EU member countries French- speaking? French- speaking EU Metadata per document Knowledge about metadata
- 15. Use Schema.org orother ontologies to extend your knowledge graph http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery http://www.mycom.com/ taxonomy/62346723 Venice prefLabel prefLabel St. Mark’s Square http://schema.org/containedIn http://schema.org/location Peggy Guggenheim Museum http://www.geonames.org/7302945 http://www.freebase.com/m/0q9rr http://dbpedia.org/resource/ Peggy_Guggenheim_Collection https://www.youtube.com/ VeniceGuggenheim http://www.mycom.com/ taxonomy/5456544 http://www.mycom.com/ taxonomy/7835488
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- 17. Make use oftext corpus analysis
- 18. Linking and mappingof vocabularies and knowledge graphs including semi-automatic batch-linking of taxonomies with other taxonomies or several knowledge graphs
- 19. Use PoolParty PowerTaggingto integrate with Enterprise Content Systems Confluence Drupal SharePoint 2013
- 20. Semantic Search inSharePoint 2013 based on PoolParty PowerTagging Based on the search enrichment the following features are available: • Autocomplete • Additional search navigators based on thesaurus structure • Query expansion All features can be configured (e.g. number of results for autocomplete) in the search web part configuration.
- 21. sOnr webMining forConfluence
- 22. sOnr webMining forDrupal Online-Demos: http://www.semantic-web.at/marketscan http://drupal.poolparty.biz/
- 23. PoolParty Applications &Demos Thesaurus Publishing Business Intelligence Content Recommendation Semantic Expert Finder Web Mining Semantic Search Semantic Tagging in SharePoint Symptom Checker
- 24. Get started! Tryit out now. Get your PoolParty Thesaurus Server & Entity Extractor trial: http://www.poolparty.biz/test-demo/
- 25. Contact points &further information Andreas Blumauer, MSc IT a.blumauer@semantic-web.at https://www.linkedin.com/in/andreasblumauer Semantic Web Company GmbH Mariahilfer Strasse 70/8, A-1070 Vienna +43-1-4021235 http://www.semantic-web.at http://www.poolparty-software.com Social Media Channels http://slideshare.net/semwebcompany http://youtube.com/semwebcompany https://www.linkedin.com/groups?home=&gid=4059165
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- 28. PoolParty at aglance - Technical Overview ● user-friendly: create & maintain vocabularies ● standards-based: based on W3C standards ● graph-based: natively built on graph databases ● embedded in ecosystem: use of linked (open) data ● best-of-breed: text mining, taxonomies & ontologies ● enterprise-ready: secure, simple to install ● integrable: connectors for SharePoint, Drupal, Confluence, WordPress, … your own CMS?
- 29. PoolParty Semantic Integrator: UnifiedViews on various data sources based on
- 30. SKOS as a‘backbone’ of knowledge graphs http://www.w3.org/2004/02/skos/
- 31. Benefit arguments Cost effectivenessThe systemic view Operating efficiency Basic argument IT-Management / Software Architect Information & Knowledge Management Business Process Management Efficient and agile data model Better reuse of existing information resources helps to save costs Better understanding of relations between things increases communication skills Unified views on business objects lead to better decisions Higher information quality Efficient handling of metadata Increased transparency on inconsistencies and contradictions Information flows adapt to the needs of the user Improved information retrieval Automatic structuring of unstructured data help to save costs Consistent use of controlled vocabularies triggers additional network effects BI-like, complex queries become possible
- 32. PoolParty Thesaurus Server froma taxonomist’s perspective ● Comfortable browser-based user interface o drag & drop o autocomplete ● Rich import/export features ● Built-in corpus management ● Quality management ● Full-blown audit trails & workflow management ● From simple taxonomies over thesauri to ontologies ● Linking of taxonomies ● Linked Data interfaces ● Visual Browser
- 33. From simple taxonomies(SKOS) to ontologies: custom schema management
- 34. PowerTagging for SharePoint Technicaloutline The PowerTagging integration adds the following features: • Automatic and manual tagging of SharePoint list items and uploaded documents • Enrichment of the SharePoint search index based on the tagged content. The search index can be enriched by: • labels of tagged concepts • relations of tagged concepts and their labels (top concepts, broader concepts, related concepts)
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- 36. Create business dashboardson the fly 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.
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- 38. PoolParty API ● navigatea thesaurus (API documentation) o retrieve schemes of a project o show top concepts of a scheme o describe a concept, get all the labels & relations ● search for concepts / autocomplete o show concepts containing a string ● suggest new concepts o integrate with a 3rd party content management system
- 39. PoolParty API Text Analyticsvia REST call or file upload ● API documentation ● Test form ● extract concepts ● categorize text http://mercury.poolparty.biz/extractor/api/extract?projectId=1DBC62B E-DB43-0001-279F-15301B94F230&language=en&numberOfTerms=0&text=... http://mercury.poolparty.biz/extractor/api/categorization?projectId= 1DBC62BE-DB43-0001-279F- 15301B94F230&language=en&numberOfTerms=0&text=...