KEMBAR78
The Fast Track to Knowledge Engineering | PDF
Semantic Web Company , profile picture
Uploaded bySemantic Web Company
1,081 views

The Fast Track to Knowledge Engineering

The document provides an overview of the Semantic Web Company and its PoolParty Semantic Suite, emphasizing its focus on knowledge engineering and management. It details the capabilities of knowledge graphs, taxonomies, and ontologies in enhancing data and content architecture, along with case studies and product specifics. The suite supports semantic middleware, linked data management, and various applications in machine learning, text analytics, and content management.

Downloaded 73 times
Andreas Blumauer CEO & Managing Partner Semantic Web Company / PoolParty Semantic Suite The Fast Track to Knowledge Engineering
Introduction 2 Semantic Web Company founder & CEO of Andreas Blumauer developer and vendor of 2004 founded 7.0 current Version active at based on Vienna located part of Enterprise Knowledge Graphs manages standard for part of enriches >200serves customers editor of Taxonomies is about Ontologies standard for graduates
PoolParty Academy Three Training Programs 3 ▸ Initial Launch: September 2016 ▸ E-learning tracks: 4 (incl. Partner Track) ▸ Learners enrolled: >700 ▸ Certifications: 330 Product relatedTechnology oriented
PoolParty Academy Get certified! 4 https://www.poolparty.biz/academy/
Semantic Web Starter Kit 5
GET STARTED 6 Get your test account at www.poolparty.biz
Semantics in a nutshell Knowledge graphs Linked Data Management Text Analytics Use Cases 7
Different shades of metadata > Read more!
Core Principle The Semantic Layer completes the Four-layered Data & Content Architecture 9 > Read more!
The Semantic Web A standards-based graph of knowledge graphs 10 Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/
Google Knowledge Graph Knowledge Graphs (KG) can cover general knowledge (often also called cross-domain or encyclopedic knowledge), or provide knowledge about special domains such as biomedicine. In most cases KGs are based on Semantic Web standards, and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work. Examples: ▸ DBpedia ▸ Google Knowledge Graph ▸ YAGO ▸ OpenCyc ▸ Wikidata 11 Who is the inventor of the World Wide Web?
DBpedia, YAGO & Co. Use weakly structured sources like Wikipedia as they were a structured data source DBpedia is a frequently used Knowledge Graph covering general knowledge (often also called cross-domain or encyclopedic knowledge), also used as a central hub for entity linking to other graphs in the LOD cloud. Like YAGO, it is harvested from Wikipedia and structured by its own ontology. Just recently, new large-scale knowledge graphs like Diffbot or Unigraph were introduced. 12 Show me all the countries using Euro on a map Link to Online Demo
Semantic Search & Assistants Based on Knowledge Graphs & Knowledge Extraction 13 Perth Australia Perth is one of the most isolated major cities in the world, with a population of 2,022,044 living in Greater Perth. Australia is a member of the OECD, United Nations, G20, ANZUS, and the World Trade Organisation. Country City is a is a is located in Support complex Q&A: distance between Which cities located in the Commonwealth of Nations have a population of more than 2 mio. people? Commonwealth of Nations International Organisation is part of is a Avoid illogical answers:
Implicit Semantics ▸ Natural languages ▸ Ambiguity versus Universality ▸ Context information and background knowledge needed 14 Susan observes Mike on a tower with a telescope.
Context is King ▸ Natural languages ▸ Ambiguity versus Universality ▸ Context information and background knowledge needed 15 - Susan and Mike are persons. - Yesterday Michael bought a Celestron. - If one buys something, (s)he owns it and can use it. - Mike and Michael is the same person. - A Celestron is a telescope. Susan observes Mike on a tower with a telescope.
Taxonomies Introducing some explicit semantics ▸ Taxonomies ▸ SKOS taxonomies are concept- and resource-based knowledge models ▸ SKOS stands for Simple Knowledge Organization System and is part of the Semantic Web Standard16 skos: Concept Celestron skos:prefLabel skos: Concept skos:related Mike skos:prefLabel Michael skos:altLabel skos: Concept Susan skos:prefLabel skos:related skos: Concept Scheme skos:inScheme skos:inScheme Person skos:prefLabel skos: Concept Tower of Babel skos:prefLabel skos: Concept skos:broader Telescope skos:prefLabel skos:related
Ontologies Some more explicit semantics ▸ Ontologies ▸ Ontologies classify things and define more specific relations and attributes ▸ Locally and globally recognised ontologies can be combined ▸ Ontologies can have various levels of expressivity (RDFS, OWL)17 schema: Product Telescope schema:name foaf: Person schema:owns Mike foaf:nick Michael foaf:givenName foaf: Person Susan foaf:givenName myOnt:observes geo: Spatial Thing Tower of Babel skos:prefLabel schema: Brand schema:brand Celestron schema:name myOnt:visits > Read more!
Reasoning 18 If someone buys a Celestron, (s)he can use it as a telescope. buys uses is ais subproperty of
Reasoning over taxonomies 19 Celestron Telescope Optical device NEXSTAR SLT Take your explorations to new heights with Celestron's NexStarSLT. Available with a variety of optical tubes up to 127 mm in aperture, the NexStar SLT has something for everyone. Beginners will appreciate the intuive SkyAlign technology, which makes aligning your device's computer to the night sky as easy as centering three bright objects in the eyepiece. The NexStar SLT is a precision instrument that can grow with you in the hobby of amateur astronomy for years to come. I’m looking for documents about Optical Devices skos:broader skos:broader is a owl:TransitiveProperty
Use Case Faceted Search based on Taxonomies 20
Resolving Language Problems 21 “While most people can deal with linguistic features as synonyms, homographs, polyhierarchies, and even with far more peculiar characteristics of natural languages, machines often struggle with automatic sense-making because of the lack of a semantic knowledge model that can be used programmatically.” > Read more!
‘Things’ but not Strings: Using a ‘Semantic Knowledge Graph’ http://www.my.com/ taxonomy/62346723 prefLabel Retina image http://www.my.com/ images/90546089 http://www.my.com/ taxonomy/ 97345854 prefLabel Funduscope altLabel Ophthalmoscope http://www.mycom.com /taxonomy/4543567 prefLabel Diagnostic Equipment has broader
The power of knowledge graphs: Agility, extensibility, precision doc doc doc Retinoscope Endoscope Flowmeter Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Show me all documents about Diagnostic Equipment Traditional approach Graph-based approach doc doc doc
The power of knowledge graphs: Agility, extensibility, precision doc doc doc Diagnostic Equipment, Retinoscope Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Show me all documents about Diagnostic Equipment Diagnostic Equipment Traditional approach Graph-based approach doc doc doc
The power of knowledge graphs: Agility, extensibility, precision doc doc doc Diagnostic Equipment, Retinoscope Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Funduscopes doc doc doc Show me all documents about Diagnostic Equipment
The power of knowledge graphs: Agility, extensibility, precision doc doc doc Funduscope, Diagnostic Equipment, Retinoscope Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Funduscopes doc doc doc Show me all documents about Diagnostic Equipment Funduscope
The power of knowledge graphs: Agility, extensibility, precision doc doc doc Ophthalmoscope, Funduscope, Diagnostic Equipment, Retinoscope Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Ophthalmoscopes doc doc doc Show me all documents about Diagnostic Equipment Funduscope
The power of knowledge graphs: Agility, extensibility, precision doc doc doc Optical Instruments, Ophthalmoscope Funduscope, Diagnostic Equipment, Retinoscope Optical Instruments, Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Funduscopes doc doc doc Show me all documents about Diagnostic Equipment Funduscope Optical Instruments Optical instruments?
The power of knowledge graphs: Agility, extensibility, precision doc doc doc Optical Instruments, Ophthalmoscope Funduscope, Diagnostic Equipment, Retinoscope Optical Instruments, Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Funduscopes doc doc doc Show me all documents about Diagnostic Equipment Funduscope Optical Instruments Optical instruments? Metadata per document 1. No or little network effects 2. No reuse of metadata 3. Metadata resides in silos 4. Data quality hard to measure 5. Not machine-readable Knowledge about metadata 1. Explicit knowledge models 2. Reusable and measurable 3. Metadata is machine-processable 4. Standards-based metadata 5. Linkable metadata opens silos
How to build a Knowledge Graph? Anatomy of an Enterprise Knowledge Graph 30
Things and URIs Venice St. Mark’s Square Peggy Guggenheim Museum http://my.com/1 http://my.com/2 http://my.com/3
Labels and basic relations: Taxonomies and Thesauri prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum prefLabel Piazza altLabel Town Square related related prefLabel broader > Read more!
Classes, specific relations, restrictions: Ontologies and Custom Schemas prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace broader
Metadata and Graph annotations prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace CC BY-SA 3.0 broader
Entity linking and schema mappings: Links to other graphs prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace CC BY-SA 3.0 broader
Linking to data and documents stored in other systems prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square broader Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace CC BY-SA 3.0 The Peggy Guggenheim Collection is a modern art museum on the Grand Canal in the Dorsoduro sestiere of Venice, Italy.
Linked Data Life Cycle How to build Enterprise Knowledge Graphs? 37 > Read more!
Use Case: Integrated Views on Business Data > Try it out! 38 Learn more about it: Structured and Unstructured Data Linked
Knowledge Graphs as input for Machine Learning 39 Unstructured Data Structured Data Knowledge Graphs Machine Learning Semantic Layer Cognitive Applications > Read more!
Core Principle The Semantic Layer completes the Four-layered Data & Content Architecture 40 > Read more!
PoolParty Hands-on Session Try it out! 41
Fact sheet: PoolParty PoolParty Semantic Suite ▸ Most complete Semantic Middleware on the Global Market ▸ Semantic AI: Fusion of Knowledge Graphs, NLP, and Machine Learning ▸ Linked Data Management along the whole Data Life Cycle ▸ W3C standards compliant ▸ First release in 2009 ▸ Current version 7.0 ▸ Over 200 installations world-wide ▸ On-premises or cloud-based ▸ KMWorld listed PoolParty as Trend-Setting Product 2015, 2016 and 2017 ▸ www.poolparty.biz 42 PoolParty supports the management of semantic knowledge graphs and linked data governance along the entire data lifecycle.
Solutions based on PoolParty 43 Process Automation ▹ Enhanced Machine Learning ▹ Text Mining & NLP ▹ Document Classification Enhanced Customer Experience ▹ Recommender Systems ▹ SEO ▹ Smart Helpdesk Solutions ▹ Chatbots and Q&A engines Knowledge Management ▹ Semantic Search ▹ Personalization ▹ Knowledge Discovery Portals Information Management ▹ Semantic Content Management ▹ Metadata Management ▹ Masterdata Management Knowledge Engineering ▹ Taxonomy Management ▹ Ontology Management ▹ Knowledge Graph Management ▹ Data Visualization Agile Data Integration ▹ Linked Data ▹ Integrating heterogeneous data ▹ Entity Linking
Functions and Components 44
Selected Customer References and Partners SWC head- quarters 45 Selected Customer References ● Credit Suisse ● Boehringer Ingelheim ● Roche ● adidas ● The Pokémon Company ● Fluor ● Harvard Business School ● Wolters Kluwer ● Philips ● Nestlé ● Electronic Arts ● Springer Nature ● Pearson - Always Learning ● Healthdirect Australia ● World Bank Group ● Canadian Broadcasting Corporation ● Oxford University Press ● International Atomic Energy Agency ● Siemens ● Singapore Academy of Law ● Inter-American Development Bank ● Council of the E.U. ● AT&T Selected Partners ● Enterprise Knowledge ● Mekon Intelligent Content Solutions ● Soitron ● Accenture ● EPAM Systems ● BAON Enterprises ● Findwise ● Tellura Semantics ● HPC ● Minerva Intelligence ● Make it a Triple US East US West AUS/ NZL UK We work with Global Fortune Companies, and with some of the largest GOs and NGOs from over 20 countries.
How PoolParty’s ontology and custom schema management plays together with taxonomies 46 Taxonomy Ontology Ontology 1 from library Ontology 2 (imported) Ontology 3 (custom-made) Custom Schema
Corpus analysis results in a network of concepts and terms 47 I need support to continuously extend our taxonomy / controlled vocabulary! skos: Concept Reference Corpus - Websites - PDF, Word, … - Abstracts from DBpedia - RSS Feeds skos: Concept skos: Concept Term 1 Term 3 Term 7 Term 8 Term 6 Term 4 Term 2 Term 5 - Relevant terms and phrases - Relevancy of concepts - co-occurence between concepts and terms - co-occurence between terms and terms
Autotagging & Consistent Tagging based on controlled vocabularies 48
PoolParty Semantic Integrator - at a glance Watch Tutorial 49 Deep Data Analytics Semantic Search Semantic Integrator Unstructured Data Structured Data ETL / Monitoring / Scheduling
BASIC FUNCTIONALITIES PoolParty’s core competencies at a glance 50
Place your screenshot here 51Maintaining Vocabularies Taxonomies and controlled vocabularies are maintained by using the SKOS standard of W3C. The intuitive user interface provides comfortable control elements like drag & drop or autocomplete. A tree view on the taxonomy plays a central part in navigation and orientation.
Place your screenshot here 52SKOS Editor The SKOS View on a concept allows the management of labels (e.g. synonyms), hierarchies and non-hierarchical relations, and mappings to other vocabularies. Also more complex actions like merging of concepts, moving of subtrees or the creation of poly-hierarchies are supported. PoolParty fully covers the SKOS standard of W3C incl. SKOS-XL and SKOS Collections.
Place your screenshot here 53History & Audit Trails Every change being made on a concept of a thesaurus is stored and can be tracked. A full history containing the author, timestamp and action being taken can be displayed for each concept and for the whole project. Recovery and rollback can be managed by PoolParty’s snapshot mechanism.
Place your screenshot here 54Linking & Mapping The same concept can occur in several taxonomies and can be put in different contexts. PoolParty provides a comfortable dialogue for the semi-automatic linking between concepts from several thesauri. Additionally, concepts can also be mapped to linked data sources like DBpedia or Geonames, or even to non-RDF sources provided by you.
Place your screenshot here 55User Management & Roles User Management is based on user accounts, roles, and groups. User authentication can be integrated with LDAP. PoolParty’s security layer is based on Spring Security. PoolParty’s API is fully integrated with the security layer.
Place your screenshot here 56Workflows Approval (or rejection) of changes on a thesaurus can be governed by workflows. Several roles in the PoolParty system have different rights to apply changes, reject or approve those. A clearly structured dashboard helps taxonomists not to loose track of all the tasks that need to be performed.
57 Taxonomy Linking SKOS based Taxonomy Management Workflows Import Excel SELECTED VIDEOS > PoolParty on YouTube
ADVANCED FUNCTIONALITIES Efficient taxonomy management and text mining based on PoolParty 58
Place your screenshot here 59Entity Extraction PoolParty’s API provides a rich set of methods for text mining and entity extraction. This ultra-fast service makes use of your controlled vocabularies, therefore it is highly accurate for your specific domain. The service will improve over time and learns from reference text corpora. It supports over 40 languages and comes with a powerful disambiguation algorithm.
Place your screenshot here 60Semantic Classifier Text Classification based on Machine Learning and Semantic Knowledge Models. PoolParty Semantic Classifier combines machine learning algorithms (SVM, Deep Learning, Naive Bayes, etc.) with Semantic Knowledge Graphs. The combined approach improves the classification results by up to 3% as compared to traditional term-based approaches.
Place your screenshot here 61Corpus Analysis PoolParty can automatically analyze reference text corpora. The calculation of a statistical model of a ‘typical vocabulary’ of a specific domain helps to suggest candidate concepts for the expansion of a taxonomy. By this means, the quality of term extraction improves over time and potential relations between concepts and terms can be suggested by the system.
Place your screenshot here 62Custom Schemes & Ontologies SKOS is based on a simple schema. This can be expanded by additional custom schemes. Custom schemes can be created with help of PoolParty’s ontology & schema editor. For an increased interoperability, PoolParty provides a rich set of preconfigured ontologies like schema.org or FOAF.
Place your screenshot here 63Quality Management & Import Validation Data quality and especially the quality of metadata is key to a more efficient information management. PoolParty Server provides several built-in quality checks (e.g. to avoid circularities). Checks can be executed when imports are made, at run-time or at any time to generate a quality report.
Place your screenshot here 64Linked Data The use of Linked Data standards increases interoperability of your knowledge graphs & metadata. With PoolParty, each thesaurus and ontology can be provided as a Linked Data graph. In return, every linked data source can potentially be used to enrich a thesaurus. PoolParty supports scenarios like ‘Enterprise Linked Data’ as well as ‘Linked Open Data’.
Place your screenshot here 65Linked Data Orchestration With UnifiedViews, data processing tasks can be modelled as pipelines: Make use of the intuitively usable graphical interface. Versatile data integration platform: Link data from internal and external data sources in a central NoSQL linked data warehouse. Custom plugins: Your pipelines are highly customizable by creating your own data processing units (DPUs).
Place your screenshot here 66GraphSearch Semantic search at the highest level: PoolParty Graph Search Server combines the power of graph databases and SPARQL engines with features of ‘traditional’ search engines. Document search and visual analytics: Benefit from additional insights through interactive visualizations of reports and search results derived from your data lake by executing sophisticated SPARQL queries.
67 Corpus Analysis Custom Schemes & Ontologies Entity Extraction UnifiedViews SELECTED VIDEOS > PoolParty on YouTube
INTEGRATION WITH A CMS Benefiting from a Semantic Layer 68
INTEGRATING POOLPARTY ALONGSIDE THE CONTENT LIFE CYCLE 69
SharePoint and PoolParty at a Glance > Learn more 70
Autotagging & Consistent Tagging based on controlled vocabularies 71
Semantic Search for SharePoint and Office 365 72
INTEGRATION WITH MARKLOGIC Benefiting from a Full Semantics Stack 73
TWO INTEGRATION SCENARIOS 74 DAM/CMS Option 1: Concepts are derived from taxonomy and tagging is stored together with the asset in the DAM/CMS http://apple.com/macmini.jpg http://apple.com/graph/1234 PoolParty API Option 2: Concepts are derived from taxonomy, and tagging event is stored in a Linked Data Store by tying together assets with concepts from graph. DAM/CMS http://apple.com/macmini.jpg http://apple.com/graph/1234 PoolParty API http://apple.com/macmini.jpg http://apple.com/macmini.jpg http://apple.com/graph/1234 LD Store Wed 3 May, 2017User4711 DAM/CMS API Pool Party Pool Party
MarkLogic and PoolParty at a Glance 75
YOUR BENEFIT 76 Semantic as a Service Standards-based technology Precise document classification Semantic Middleware for Enrichment and Linking + = FULL SEMANTICS STACK Fast Time to Results Ask Anything Universal Index Trusted Data and Transactions Enterprise-Grade Security Scale-Out Commodity Hardware Lightning Fast and Real-Time Operational and Transactional Enterprise NoSQL Database Data Integration Intelligent Search Deep Analytics Data Enrichment Data Governance Graph-based metadata management Superior user friendliness Beyond search
MarkLogic / PoolParty Demo Application > Try it out! 77 Learn more about MarkLogic and PoolParty as a bundle
Some Use Cases that make use of PoolParty 78
Semantic Web Starter Kit 79
CONNECT Andreas Blumauer CEO, Semantic Web Company ▸ andreas.blumauer@semantic-web.com ▸ https://www.linkedin.com/in/andreasblumauer ▸ https://twitter.com/semwebcompany ▸ https://ablvienna.wordpress.com/ 80 © Semantic Web Company - http://www.semantic-web.com and http://www.poolparty.biz/

More Related Content

PDF
Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling
bySemantic Web Company
 
PDF
Leveraging Taxonomy Management with Machine Learning
bySemantic Web Company
 
PDF
PoolParty GraphSearch - The Fusion of Search, Recommendation and Analytics
bySemantic Web Company
 
PDF
Leveraging Knowledge Graphs in your Enterprise Knowledge Management System
bySemantic Web Company
 
PDF
PoolParty 6.0 - Climbing the Semantic Ladder
bySemantic Web Company
 
PDF
PoolParty Semantic Classifier
bySemantic Web Company
 
PDF
Semantics as the Basis of Advanced Cognitive Computing
bySemantic Web Company
 
PPTX
BrightTALK - Semantic AI
bySemantic Web Company
 
Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling
bySemantic Web Company
 
Leveraging Taxonomy Management with Machine Learning
bySemantic Web Company
 
PoolParty GraphSearch - The Fusion of Search, Recommendation and Analytics
bySemantic Web Company
 
Leveraging Knowledge Graphs in your Enterprise Knowledge Management System
bySemantic Web Company
 
PoolParty 6.0 - Climbing the Semantic Ladder
bySemantic Web Company
 
PoolParty Semantic Classifier
bySemantic Web Company
 
Semantics as the Basis of Advanced Cognitive Computing
bySemantic Web Company
 
BrightTALK - Semantic AI
bySemantic Web Company
 

What's hot

PPTX
PROPEL . Austrian's Roadmap for Enterprise Linked Data
bySemantic Web Company
 
PDF
My Spark Journey
byData Science Thailand
 
PPTX
How Big Data ,Cloud Computing ,Data Science can help business
byAjay Ohri
 
PDF
Using Knowledge Graphs to Predict Customer Needs and Improve Quality
byNeo4j
 
PPTX
Linked data introduction w exempel
byKerstin Forsberg
 
PDF
Big Data Career Path | Big Data Learning Path | Hadoop Tutorial | Edureka
byEdureka!
 
PDF
Success stories with Connected Data
byConnected Data World
 
PDF
Using hadoop for big data
byData Science Thailand
 
PPT
Lift your data_inspire2012
byEURECOM
 
PPT
Calais @ the Palo Alto Semantic Web Meetup
byKrista Thomas
 
PDF
Powerful Information Discovery with Big Knowledge Graphs –The Offshore Leaks ...
byConnected Data World
 
PDF
Data Science Application in Business Portfolio & Risk Management
byData Science Thailand
 
PDF
MarkLogic Overview and Use Cases
byIoan Toma
 
PDF
Data science workshop
byHortonworks
 
PDF
Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...
byConnected Data World
 
PPTX
MarkLogic Overview, Ron Avnur, MarkLogic
bymug-fr
 
PDF
ITWS Capstone Lecture (Spring 2013)
byRensselaer Polytechnic Institute
 
PDF
Python for Data Science | Python Data Science Tutorial | Data Science Certifi...
byEdureka!
 
PDF
AI-SDV 2021: Francisco Webber - Efficiency is the New Precision
byDr. Haxel Consult
 
PDF
Human in the loop: a design pattern for managing teams working with ML
byPaco Nathan
 
PROPEL . Austrian's Roadmap for Enterprise Linked Data
bySemantic Web Company
 
My Spark Journey
byData Science Thailand
 
How Big Data ,Cloud Computing ,Data Science can help business
byAjay Ohri
 
Using Knowledge Graphs to Predict Customer Needs and Improve Quality
byNeo4j
 
Linked data introduction w exempel
byKerstin Forsberg
 
Big Data Career Path | Big Data Learning Path | Hadoop Tutorial | Edureka
byEdureka!
 
Success stories with Connected Data
byConnected Data World
 
Using hadoop for big data
byData Science Thailand
 
Lift your data_inspire2012
byEURECOM
 
Calais @ the Palo Alto Semantic Web Meetup
byKrista Thomas
 
Powerful Information Discovery with Big Knowledge Graphs –The Offshore Leaks ...
byConnected Data World
 
Data Science Application in Business Portfolio & Risk Management
byData Science Thailand
 
MarkLogic Overview and Use Cases
byIoan Toma
 
Data science workshop
byHortonworks
 
Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...
byConnected Data World
 
MarkLogic Overview, Ron Avnur, MarkLogic
bymug-fr
 
ITWS Capstone Lecture (Spring 2013)
byRensselaer Polytechnic Institute
 
Python for Data Science | Python Data Science Tutorial | Data Science Certifi...
byEdureka!
 
AI-SDV 2021: Francisco Webber - Efficiency is the New Precision
byDr. Haxel Consult
 
Human in the loop: a design pattern for managing teams working with ML
byPaco Nathan
 

Similar to The Fast Track to Knowledge Engineering

ODT
Riding The Semantic Wave
byKaniska Mandal
 
PDF
Smart cities no ai without ia
byFredric Landqvist
 
PPTX
PoolParty Semantic Platform - Overview
bySemantic Web Company
 
PPTX
Knowledge Graph Introduction
bySĂśren Auer
 
PDF
Introduction to the Semantic Web
byMarin Dimitrov
 
PPTX
SharePoint Saturday New york City - The importance of metadata #spsnyc
byVincent Biret
 
PDF
Nordic health data metadata
byFredric Landqvist
 
PDF
111 Blumauer - A Five-Star Rating System for Semantic Search
byAndreas Blumauer
 
PDF
Andreas Blumauer: A Five-Star Rating System for Semantic Search
bySemantic Web Company
 
PPT
Web 3.0 Emerging
byJames Hendler
 
PDF
Understanding Information Architecture
byScott Abel
 
PPTX
NISO/NFAIS Joint Virtual Conference: Connecting the Library to the Wider Wor...
byNational Information Standards Organization (NISO)
 
PDF
The technical case for a semantic web
byTony Dobaj
 
PPTX
SemTech 2011 Semantic Search tutorial
byPeter Mika
 
PPTX
How the Web can change social science research (including yours)
byFrank van Harmelen
 
PDF
Ontologies Fmi 042010
byMariana Damova, Ph.D
 
PDF
Structured Data: It's All About the Graph!
byRichard Wallis
 
PDF
Structured Data: It's All about the Graph | Richard Wallis, Data Liberate
byClick Consult (Part of Ceuta Group)
 
PPTX
Ontology for Knowledge and Data Strategies.pptx
byMike Bennett
 
PDF
Introduction to Knowledge Graphs and Semantic AI
bySemantic Web Company
 
Riding The Semantic Wave
byKaniska Mandal
 
Smart cities no ai without ia
byFredric Landqvist
 
PoolParty Semantic Platform - Overview
bySemantic Web Company
 
Knowledge Graph Introduction
bySĂśren Auer
 
Introduction to the Semantic Web
byMarin Dimitrov
 
SharePoint Saturday New york City - The importance of metadata #spsnyc
byVincent Biret
 
Nordic health data metadata
byFredric Landqvist
 
111 Blumauer - A Five-Star Rating System for Semantic Search
byAndreas Blumauer
 
Andreas Blumauer: A Five-Star Rating System for Semantic Search
bySemantic Web Company
 
Web 3.0 Emerging
byJames Hendler
 
Understanding Information Architecture
byScott Abel
 
NISO/NFAIS Joint Virtual Conference: Connecting the Library to the Wider Wor...
byNational Information Standards Organization (NISO)
 
The technical case for a semantic web
byTony Dobaj
 
SemTech 2011 Semantic Search tutorial
byPeter Mika
 
How the Web can change social science research (including yours)
byFrank van Harmelen
 
Ontologies Fmi 042010
byMariana Damova, Ph.D
 
Structured Data: It's All About the Graph!
byRichard Wallis
 
Structured Data: It's All about the Graph | Richard Wallis, Data Liberate
byClick Consult (Part of Ceuta Group)
 
Ontology for Knowledge and Data Strategies.pptx
byMike Bennett
 
Introduction to Knowledge Graphs and Semantic AI
bySemantic Web Company
 

More from Semantic Web Company

PDF
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...
bySemantic Web Company
 
PDF
Deep Text Analytics - How to extract hidden information and aboutness from text
bySemantic Web Company
 
PDF
Linking SharePoint Documents with Structured Data
bySemantic Web Company
 
PDF
Semantic AI
bySemantic Web Company
 
PDF
Taxonomies put in the right place
bySemantic Web Company
 
PDF
Structured Content Meets Taxonomy
bySemantic Web Company
 
PDF
PoolParty Semantic Suite - Release 6.0 (Technical Overview)
bySemantic Web Company
 
PDF
Taxonomy Quality Assessment
bySemantic Web Company
 
PDF
Taxonomy-Driven UX
bySemantic Web Company
 
PDF
PoolParty Semantic Suite - Release 5.5
bySemantic Web Company
 
PDF
PowerTagging for Sharepoint and Office 365
bySemantic Web Company
 
PDF
From SKOS over SKOS-XL to Custom Ontologies
bySemantic Web Company
 
PDF
PoolParty Semantic Suite: Solutions for Sustainable Development: The Climate ...
bySemantic Web Company
 
PPTX
PoolParty Semantic Suite - Solutions for Sustainable Development - weadapt.or...
bySemantic Web Company
 
PPTX
Dynamic Semantic Publishing
bySemantic Web Company
 
PDF
PoolParty Semantic Suite - Management Briefing
bySemantic Web Company
 
PDF
PoolParty Semantic Suite - Functional Overview
bySemantic Web Company
 
PPTX
SKOS in the Public Sector
bySemantic Web Company
 
PDF
SKOS - An Overview
bySemantic Web Company
 
PDF
SKOS - Some Use Cases
bySemantic Web Company
 
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...
bySemantic Web Company
 
Deep Text Analytics - How to extract hidden information and aboutness from text
bySemantic Web Company
 
Linking SharePoint Documents with Structured Data
bySemantic Web Company
 
Semantic AI
bySemantic Web Company
 
Taxonomies put in the right place
bySemantic Web Company
 
Structured Content Meets Taxonomy
bySemantic Web Company
 
PoolParty Semantic Suite - Release 6.0 (Technical Overview)
bySemantic Web Company
 
Taxonomy Quality Assessment
bySemantic Web Company
 
Taxonomy-Driven UX
bySemantic Web Company
 
PoolParty Semantic Suite - Release 5.5
bySemantic Web Company
 
PowerTagging for Sharepoint and Office 365
bySemantic Web Company
 
From SKOS over SKOS-XL to Custom Ontologies
bySemantic Web Company
 
PoolParty Semantic Suite: Solutions for Sustainable Development: The Climate ...
bySemantic Web Company
 
PoolParty Semantic Suite - Solutions for Sustainable Development - weadapt.or...
bySemantic Web Company
 
Dynamic Semantic Publishing
bySemantic Web Company
 
PoolParty Semantic Suite - Management Briefing
bySemantic Web Company
 
PoolParty Semantic Suite - Functional Overview
bySemantic Web Company
 
SKOS in the Public Sector
bySemantic Web Company
 
SKOS - An Overview
bySemantic Web Company
 
SKOS - Some Use Cases
bySemantic Web Company
 

Recently uploaded

PDF
Buying, Verified Coinbase Accounts A Complete Guide .pdf
byTinVejos
 
PPTX
Morgan (Kumpulan Tabel).pptx Morgan (Kumpulan Tabel).pptx
byDhika Adhi Pratama
 
PDF
WORLD BANK - Keys to Energy-Efficient Shipping
byEl Estrecho Digital
 
PDF
Epicentrum Tourism Data August 2025 Bali
bysaravian2
 
DOCX
Ad Operations - 2025 DATA.docx asdaddaewewew
byumaid74
 
DOCX
Knowledge Management Information Sheet(58f671121fa4e5fd56cf1025ded949ee).docx
byjesusjeager
 
PPTX
Human-relations (1).pptxajajjajjajajjajaj
bykellyjimalipio
 
PDF
Google Search Console: The SEO power tool you're probably misusing
byMartin Hayman
 
PPTX
How to Leverage a PIM for a Successful Data Migration 10 2025.pptx
byPaula Levy
 
DOCX
researchmethodologyreport-140226083638-phpapp02.docx
byomgorde230
 
PDF
gis based document for apparture radar analysis
byBedassaDessalegn3
 
PPTX
MongoDB Developers Best Practices12.pptx
bysivasanniboina1
 
PDF
Short note for sampling theory and research method
byTigistG2
 
PPTX
Agentic Systems and Compliance - A brief intro [1.2]
byrobertowilliams
 
PDF
Step-by-Step Process for Buy Verified Apple Pay Accounts.pdf
bymarketing
 
PDF
Data Mining Core Concept & Introduction.
bySyed Ahsan Raza Shah
 
PDF
bcg-the-economic-impact-of-ford-and-the-f-series-sept-2020-v2.pdf
byfertiharki
 
PDF
Paper: The World Game (s) Great Redesign.pdf
bySteven McGee
 
PPTX
Result Analysis - Mechanical engineering.pptx
bySolomon Raj
 
PPTX
Bəktiyev Əli-1.pptxyyyyyyyyyyyyyyyyyyyyyyyyy
bynecesen996
 
Buying, Verified Coinbase Accounts A Complete Guide .pdf
byTinVejos
 
Morgan (Kumpulan Tabel).pptx Morgan (Kumpulan Tabel).pptx
byDhika Adhi Pratama
 
WORLD BANK - Keys to Energy-Efficient Shipping
byEl Estrecho Digital
 
Epicentrum Tourism Data August 2025 Bali
bysaravian2
 
Ad Operations - 2025 DATA.docx asdaddaewewew
byumaid74
 
Knowledge Management Information Sheet(58f671121fa4e5fd56cf1025ded949ee).docx
byjesusjeager
 
Human-relations (1).pptxajajjajjajajjajaj
bykellyjimalipio
 
Google Search Console: The SEO power tool you're probably misusing
byMartin Hayman
 
How to Leverage a PIM for a Successful Data Migration 10 2025.pptx
byPaula Levy
 
researchmethodologyreport-140226083638-phpapp02.docx
byomgorde230
 
gis based document for apparture radar analysis
byBedassaDessalegn3
 
MongoDB Developers Best Practices12.pptx
bysivasanniboina1
 
Short note for sampling theory and research method
byTigistG2
 
Agentic Systems and Compliance - A brief intro [1.2]
byrobertowilliams
 
Step-by-Step Process for Buy Verified Apple Pay Accounts.pdf
bymarketing
 
Data Mining Core Concept & Introduction.
bySyed Ahsan Raza Shah
 
bcg-the-economic-impact-of-ford-and-the-f-series-sept-2020-v2.pdf
byfertiharki
 
Paper: The World Game (s) Great Redesign.pdf
bySteven McGee
 
Result Analysis - Mechanical engineering.pptx
bySolomon Raj
 
Bəktiyev Əli-1.pptxyyyyyyyyyyyyyyyyyyyyyyyyy
bynecesen996
 

The Fast Track to Knowledge Engineering

  • 1.
    Andreas Blumauer CEO &Managing Partner Semantic Web Company / PoolParty Semantic Suite The Fast Track to Knowledge Engineering
  • 2.
    Introduction 2 Semantic Web Company founder & CEOof Andreas Blumauer developer and vendor of 2004 founded 7.0 current Version active at based on Vienna located part of Enterprise Knowledge Graphs manages standard for part of enriches >200serves customers editor of Taxonomies is about Ontologies standard for graduates
  • 3.
    PoolParty Academy Three Training Programs 3 ▸ InitialLaunch: September 2016 ▸ E-learning tracks: 4 (incl. Partner Track) ▸ Learners enrolled: >700 ▸ Certifications: 330 Product relatedTechnology oriented
  • 4.
  • 5.
  • 6.
    GET STARTED 6 Get yourtest account at www.poolparty.biz
  • 7.
    Semantics in anutshell Knowledge graphs Linked Data Management Text Analytics Use Cases 7
  • 8.
    Different shades ofmetadata > Read more!
  • 9.
    Core Principle The Semantic Layercompletes the Four-layered Data & Content Architecture 9 > Read more!
  • 10.
    The Semantic Web A standards-based graphof knowledge graphs 10 Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/
  • 11.
    Google Knowledge Graph Knowledge Graphs(KG) can cover general knowledge (often also called cross-domain or encyclopedic knowledge), or provide knowledge about special domains such as biomedicine. In most cases KGs are based on Semantic Web standards, and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work. Examples: ▸ DBpedia ▸ Google Knowledge Graph ▸ YAGO ▸ OpenCyc ▸ Wikidata 11 Who is the inventor of the World Wide Web?
  • 12.
    DBpedia, YAGO & Co. Useweakly structured sources like Wikipedia as they were a structured data source DBpedia is a frequently used Knowledge Graph covering general knowledge (often also called cross-domain or encyclopedic knowledge), also used as a central hub for entity linking to other graphs in the LOD cloud. Like YAGO, it is harvested from Wikipedia and structured by its own ontology. Just recently, new large-scale knowledge graphs like Diffbot or Unigraph were introduced. 12 Show me all the countries using Euro on a map Link to Online Demo
  • 13.
    Semantic Search & Assistants Basedon Knowledge Graphs & Knowledge Extraction 13 Perth Australia Perth is one of the most isolated major cities in the world, with a population of 2,022,044 living in Greater Perth. Australia is a member of the OECD, United Nations, G20, ANZUS, and the World Trade Organisation. Country City is a is a is located in Support complex Q&A: distance between Which cities located in the Commonwealth of Nations have a population of more than 2 mio. people? Commonwealth of Nations International Organisation is part of is a Avoid illogical answers:
  • 14.
    Implicit Semantics ▸ Natural languages ▸Ambiguity versus Universality ▸ Context information and background knowledge needed 14 Susan observes Mike on a tower with a telescope.
  • 15.
    Context is King ▸Natural languages ▸ Ambiguity versus Universality ▸ Context information and background knowledge needed 15 - Susan and Mike are persons. - Yesterday Michael bought a Celestron. - If one buys something, (s)he owns it and can use it. - Mike and Michael is the same person. - A Celestron is a telescope. Susan observes Mike on a tower with a telescope.
  • 16.
    Taxonomies Introducing some explicit semantics ▸ Taxonomies ▸SKOS taxonomies are concept- and resource-based knowledge models ▸ SKOS stands for Simple Knowledge Organization System and is part of the Semantic Web Standard16 skos: Concept Celestron skos:prefLabel skos: Concept skos:related Mike skos:prefLabel Michael skos:altLabel skos: Concept Susan skos:prefLabel skos:related skos: Concept Scheme skos:inScheme skos:inScheme Person skos:prefLabel skos: Concept Tower of Babel skos:prefLabel skos: Concept skos:broader Telescope skos:prefLabel skos:related
  • 17.
    Ontologies Some more explicit semantics ▸ Ontologies ▸Ontologies classify things and define more specific relations and attributes ▸ Locally and globally recognised ontologies can be combined ▸ Ontologies can have various levels of expressivity (RDFS, OWL)17 schema: Product Telescope schema:name foaf: Person schema:owns Mike foaf:nick Michael foaf:givenName foaf: Person Susan foaf:givenName myOnt:observes geo: Spatial Thing Tower of Babel skos:prefLabel schema: Brand schema:brand Celestron schema:name myOnt:visits > Read more!
  • 18.
    Reasoning 18 If someonebuys a Celestron, (s)he can use it as a telescope. buys uses is ais subproperty of
  • 19.
    Reasoning over taxonomies 19 Celestron Telescope Optical device NEXSTAR SLT Takeyour explorations to new heights with Celestron's NexStarSLT. Available with a variety of optical tubes up to 127 mm in aperture, the NexStar SLT has something for everyone. Beginners will appreciate the intuive SkyAlign technology, which makes aligning your device's computer to the night sky as easy as centering three bright objects in the eyepiece. The NexStar SLT is a precision instrument that can grow with you in the hobby of amateur astronomy for years to come. I’m looking for documents about Optical Devices skos:broader skos:broader is a owl:TransitiveProperty
  • 20.
  • 21.
    Resolving Language Problems 21 “While most peoplecan deal with linguistic features as synonyms, homographs, polyhierarchies, and even with far more peculiar characteristics of natural languages, machines often struggle with automatic sense-making because of the lack of a semantic knowledge model that can be used programmatically.” > Read more!
  • 22.
    ‘Things’ but notStrings: Using a ‘Semantic Knowledge Graph’ http://www.my.com/ taxonomy/62346723 prefLabel Retina image http://www.my.com/ images/90546089 http://www.my.com/ taxonomy/ 97345854 prefLabel Funduscope altLabel Ophthalmoscope http://www.mycom.com /taxonomy/4543567 prefLabel Diagnostic Equipment has broader
  • 23.
    The power ofknowledge graphs: Agility, extensibility, precision doc doc doc Retinoscope Endoscope Flowmeter Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Show me all documents about Diagnostic Equipment Traditional approach Graph-based approach doc doc doc
  • 24.
    The power ofknowledge graphs: Agility, extensibility, precision doc doc doc Diagnostic Equipment, Retinoscope Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Show me all documents about Diagnostic Equipment Diagnostic Equipment Traditional approach Graph-based approach doc doc doc
  • 25.
    The power ofknowledge graphs: Agility, extensibility, precision doc doc doc Diagnostic Equipment, Retinoscope Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Funduscopes doc doc doc Show me all documents about Diagnostic Equipment
  • 26.
    The power ofknowledge graphs: Agility, extensibility, precision doc doc doc Funduscope, Diagnostic Equipment, Retinoscope Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Funduscopes doc doc doc Show me all documents about Diagnostic Equipment Funduscope
  • 27.
    The power ofknowledge graphs: Agility, extensibility, precision doc doc doc Ophthalmoscope, Funduscope, Diagnostic Equipment, Retinoscope Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Ophthalmoscopes doc doc doc Show me all documents about Diagnostic Equipment Funduscope
  • 28.
    The power ofknowledge graphs: Agility, extensibility, precision doc doc doc Optical Instruments, Ophthalmoscope Funduscope, Diagnostic Equipment, Retinoscope Optical Instruments, Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Funduscopes doc doc doc Show me all documents about Diagnostic Equipment Funduscope Optical Instruments Optical instruments?
  • 29.
    The power ofknowledge graphs: Agility, extensibility, precision doc doc doc Optical Instruments, Ophthalmoscope Funduscope, Diagnostic Equipment, Retinoscope Optical Instruments, Diagnostic Equipment, Endoscope Diagnostic Equipment, Flowmeter Surgical Equipment, Pessary doc Retinoscope Endoscope Flowmeter Pessary doc Diagnostic Equipment Traditional approach Graph-based approach Show me all documents about Funduscopes doc doc doc Show me all documents about Diagnostic Equipment Funduscope Optical Instruments Optical instruments? Metadata per document 1. No or little network effects 2. No reuse of metadata 3. Metadata resides in silos 4. Data quality hard to measure 5. Not machine-readable Knowledge about metadata 1. Explicit knowledge models 2. Reusable and measurable 3. Metadata is machine-processable 4. Standards-based metadata 5. Linkable metadata opens silos
  • 30.
    How to builda Knowledge Graph? Anatomy of an Enterprise Knowledge Graph 30
  • 31.
    Things and URIs Venice St.Mark’s Square Peggy Guggenheim Museum http://my.com/1 http://my.com/2 http://my.com/3
  • 32.
    Labels and basicrelations: Taxonomies and Thesauri prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco Peggy Guggenheim Museum prefLabel Piazza altLabel Town Square related related prefLabel broader > Read more!
  • 33.
    Classes, specific relations,restrictions: Ontologies and Custom Schemas prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace broader
  • 34.
    Metadata and Graphannotations prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace CC BY-SA 3.0 broader
  • 35.
    Entity linking andschema mappings: Links to other graphs prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace CC BY-SA 3.0 broader
  • 36.
    Linking to dataand documents stored in other systems prefLabel Venice prefLabel St. Mark’s Square altLabel Piazza San Marco http://schema.org/City http://schema.org/TouristAttraction http://schema.org/ArtGallery Monday through Sunday, all day opening Hours image http://schema.org/containedInPlace prefLabel Piazza altLabel Town Square broader Peggy Guggenheim Museum prefLabel containedInPlace containedInPlace CC BY-SA 3.0 The Peggy Guggenheim Collection is a modern art museum on the Grand Canal in the Dorsoduro sestiere of Venice, Italy.
  • 37.
    Linked Data Life Cycle Howto build Enterprise Knowledge Graphs? 37 > Read more!
  • 38.
    Use Case: Integrated Views on BusinessData > Try it out! 38 Learn more about it: Structured and Unstructured Data Linked
  • 39.
    Knowledge Graphs as input forMachine Learning 39 Unstructured Data Structured Data Knowledge Graphs Machine Learning Semantic Layer Cognitive Applications > Read more!
  • 40.
    Core Principle The Semantic Layercompletes the Four-layered Data & Content Architecture 40 > Read more!
  • 41.
  • 42.
    Fact sheet: PoolParty PoolParty SemanticSuite ▸ Most complete Semantic Middleware on the Global Market ▸ Semantic AI: Fusion of Knowledge Graphs, NLP, and Machine Learning ▸ Linked Data Management along the whole Data Life Cycle ▸ W3C standards compliant ▸ First release in 2009 ▸ Current version 7.0 ▸ Over 200 installations world-wide ▸ On-premises or cloud-based ▸ KMWorld listed PoolParty as Trend-Setting Product 2015, 2016 and 2017 ▸ www.poolparty.biz 42 PoolParty supports the management of semantic knowledge graphs and linked data governance along the entire data lifecycle.
  • 43.
    Solutions based on PoolParty 43Process Automation ▹ Enhanced Machine Learning ▹ Text Mining & NLP ▹ Document Classification Enhanced Customer Experience ▹ Recommender Systems ▹ SEO ▹ Smart Helpdesk Solutions ▹ Chatbots and Q&A engines Knowledge Management ▹ Semantic Search ▹ Personalization ▹ Knowledge Discovery Portals Information Management ▹ Semantic Content Management ▹ Metadata Management ▹ Masterdata Management Knowledge Engineering ▹ Taxonomy Management ▹ Ontology Management ▹ Knowledge Graph Management ▹ Data Visualization Agile Data Integration ▹ Linked Data ▹ Integrating heterogeneous data ▹ Entity Linking
  • 44.
  • 45.
    Selected Customer References and Partners SWC head- quarters 45Selected Customer References ● Credit Suisse ● Boehringer Ingelheim ● Roche ● adidas ● The Pokémon Company ● Fluor ● Harvard Business School ● Wolters Kluwer ● Philips ● Nestlé ● Electronic Arts ● Springer Nature ● Pearson - Always Learning ● Healthdirect Australia ● World Bank Group ● Canadian Broadcasting Corporation ● Oxford University Press ● International Atomic Energy Agency ● Siemens ● Singapore Academy of Law ● Inter-American Development Bank ● Council of the E.U. ● AT&T Selected Partners ● Enterprise Knowledge ● Mekon Intelligent Content Solutions ● Soitron ● Accenture ● EPAM Systems ● BAON Enterprises ● Findwise ● Tellura Semantics ● HPC ● Minerva Intelligence ● Make it a Triple US East US West AUS/ NZL UK We work with Global Fortune Companies, and with some of the largest GOs and NGOs from over 20 countries.
  • 46.
    How PoolParty’s ontology and customschema management plays together with taxonomies 46 Taxonomy Ontology Ontology 1 from library Ontology 2 (imported) Ontology 3 (custom-made) Custom Schema
  • 47.
    Corpus analysis results ina network of concepts and terms 47 I need support to continuously extend our taxonomy / controlled vocabulary! skos: Concept Reference Corpus - Websites - PDF, Word, … - Abstracts from DBpedia - RSS Feeds skos: Concept skos: Concept Term 1 Term 3 Term 7 Term 8 Term 6 Term 4 Term 2 Term 5 - Relevant terms and phrases - Relevancy of concepts - co-occurence between concepts and terms - co-occurence between terms and terms
  • 48.
    Autotagging & Consistent Tagging basedon controlled vocabularies 48
  • 49.
    PoolParty Semantic Integrator - at aglance Watch Tutorial 49 Deep Data Analytics Semantic Search Semantic Integrator Unstructured Data Structured Data ETL / Monitoring / Scheduling
  • 50.
  • 51.
    Place your screenshothere 51Maintaining Vocabularies Taxonomies and controlled vocabularies are maintained by using the SKOS standard of W3C. The intuitive user interface provides comfortable control elements like drag & drop or autocomplete. A tree view on the taxonomy plays a central part in navigation and orientation.
  • 52.
    Place your screenshothere 52SKOS Editor The SKOS View on a concept allows the management of labels (e.g. synonyms), hierarchies and non-hierarchical relations, and mappings to other vocabularies. Also more complex actions like merging of concepts, moving of subtrees or the creation of poly-hierarchies are supported. PoolParty fully covers the SKOS standard of W3C incl. SKOS-XL and SKOS Collections.
  • 53.
    Place your screenshothere 53History & Audit Trails Every change being made on a concept of a thesaurus is stored and can be tracked. A full history containing the author, timestamp and action being taken can be displayed for each concept and for the whole project. Recovery and rollback can be managed by PoolParty’s snapshot mechanism.
  • 54.
    Place your screenshothere 54Linking & Mapping The same concept can occur in several taxonomies and can be put in different contexts. PoolParty provides a comfortable dialogue for the semi-automatic linking between concepts from several thesauri. Additionally, concepts can also be mapped to linked data sources like DBpedia or Geonames, or even to non-RDF sources provided by you.
  • 55.
    Place your screenshothere 55User Management & Roles User Management is based on user accounts, roles, and groups. User authentication can be integrated with LDAP. PoolParty’s security layer is based on Spring Security. PoolParty’s API is fully integrated with the security layer.
  • 56.
    Place your screenshothere 56Workflows Approval (or rejection) of changes on a thesaurus can be governed by workflows. Several roles in the PoolParty system have different rights to apply changes, reject or approve those. A clearly structured dashboard helps taxonomists not to loose track of all the tasks that need to be performed.
  • 57.
    57 Taxonomy Linking SKOS based TaxonomyManagement Workflows Import Excel SELECTED VIDEOS > PoolParty on YouTube
  • 58.
    ADVANCED FUNCTIONALITIES Efficient taxonomy managementand text mining based on PoolParty 58
  • 59.
    Place your screenshothere 59Entity Extraction PoolParty’s API provides a rich set of methods for text mining and entity extraction. This ultra-fast service makes use of your controlled vocabularies, therefore it is highly accurate for your specific domain. The service will improve over time and learns from reference text corpora. It supports over 40 languages and comes with a powerful disambiguation algorithm.
  • 60.
    Place your screenshothere 60Semantic Classifier Text Classification based on Machine Learning and Semantic Knowledge Models. PoolParty Semantic Classifier combines machine learning algorithms (SVM, Deep Learning, Naive Bayes, etc.) with Semantic Knowledge Graphs. The combined approach improves the classification results by up to 3% as compared to traditional term-based approaches.
  • 61.
    Place your screenshothere 61Corpus Analysis PoolParty can automatically analyze reference text corpora. The calculation of a statistical model of a ‘typical vocabulary’ of a specific domain helps to suggest candidate concepts for the expansion of a taxonomy. By this means, the quality of term extraction improves over time and potential relations between concepts and terms can be suggested by the system.
  • 62.
    Place your screenshothere 62Custom Schemes & Ontologies SKOS is based on a simple schema. This can be expanded by additional custom schemes. Custom schemes can be created with help of PoolParty’s ontology & schema editor. For an increased interoperability, PoolParty provides a rich set of preconfigured ontologies like schema.org or FOAF.
  • 63.
    Place your screenshothere 63Quality Management & Import Validation Data quality and especially the quality of metadata is key to a more efficient information management. PoolParty Server provides several built-in quality checks (e.g. to avoid circularities). Checks can be executed when imports are made, at run-time or at any time to generate a quality report.
  • 64.
    Place your screenshothere 64Linked Data The use of Linked Data standards increases interoperability of your knowledge graphs & metadata. With PoolParty, each thesaurus and ontology can be provided as a Linked Data graph. In return, every linked data source can potentially be used to enrich a thesaurus. PoolParty supports scenarios like ‘Enterprise Linked Data’ as well as ‘Linked Open Data’.
  • 65.
    Place your screenshothere 65Linked Data Orchestration With UnifiedViews, data processing tasks can be modelled as pipelines: Make use of the intuitively usable graphical interface. Versatile data integration platform: Link data from internal and external data sources in a central NoSQL linked data warehouse. Custom plugins: Your pipelines are highly customizable by creating your own data processing units (DPUs).
  • 66.
    Place your screenshothere 66GraphSearch Semantic search at the highest level: PoolParty Graph Search Server combines the power of graph databases and SPARQL engines with features of ‘traditional’ search engines. Document search and visual analytics: Benefit from additional insights through interactive visualizations of reports and search results derived from your data lake by executing sophisticated SPARQL queries.
  • 67.
    67 Corpus Analysis Custom Schemes& Ontologies Entity Extraction UnifiedViews SELECTED VIDEOS > PoolParty on YouTube
  • 68.
    INTEGRATION WITH A CMS Benefitingfrom a Semantic Layer 68
  • 69.
  • 70.
    SharePoint and PoolParty at aGlance > Learn more 70
  • 71.
    Autotagging & Consistent Tagging basedon controlled vocabularies 71
  • 72.
  • 73.
  • 74.
    TWO INTEGRATION SCENARIOS 74 DAM/CMS Option 1: Concepts arederived from taxonomy and tagging is stored together with the asset in the DAM/CMS http://apple.com/macmini.jpg http://apple.com/graph/1234 PoolParty API Option 2: Concepts are derived from taxonomy, and tagging event is stored in a Linked Data Store by tying together assets with concepts from graph. DAM/CMS http://apple.com/macmini.jpg http://apple.com/graph/1234 PoolParty API http://apple.com/macmini.jpg http://apple.com/macmini.jpg http://apple.com/graph/1234 LD Store Wed 3 May, 2017User4711 DAM/CMS API Pool Party Pool Party
  • 75.
  • 76.
    YOUR BENEFIT 76 Semantic asa Service Standards-based technology Precise document classification Semantic Middleware for Enrichment and Linking + = FULL SEMANTICS STACK Fast Time to Results Ask Anything Universal Index Trusted Data and Transactions Enterprise-Grade Security Scale-Out Commodity Hardware Lightning Fast and Real-Time Operational and Transactional Enterprise NoSQL Database Data Integration Intelligent Search Deep Analytics Data Enrichment Data Governance Graph-based metadata management Superior user friendliness Beyond search
  • 77.
    MarkLogic / PoolParty Demo Application > Tryit out! 77 Learn more about MarkLogic and PoolParty as a bundle
  • 78.
    Some Use Cases thatmake use of PoolParty 78
  • 79.
  • 80.
    CONNECT Andreas Blumauer CEO, SemanticWeb Company ▸ andreas.blumauer@semantic-web.com ▸ https://www.linkedin.com/in/andreasblumauer ▸ https://twitter.com/semwebcompany ▸ https://ablvienna.wordpress.com/ 80 © Semantic Web Company - http://www.semantic-web.com and http://www.poolparty.biz/