The technical case for a semantic web
1 like349 views
The document discusses the evolution of the web from Web 1.0 to Web 3.0, focusing on the shift towards a semantic web where data is interconnected and can be meaningfully analyzed. It highlights the limitations of traditional relational databases in managing complex and dynamic data, suggesting the advantages of graph databases and semantic integration for enhanced data utility. Key components of the semantic web, such as RDF, ontologies, and inference capabilities, are detailed, underscoring their role in facilitating improved data accessibility and understanding.
More Related Content
Similar to The technical case for a semantic web (20)
![From Curiosity to ROI — Cost-Benefit Analysis of Agentic Automation [3/6]](https://cdn.slidesharecdn.com/ss_thumbnails/session-3-250903141006-a3917e1a-thumbnail.jpg?width=560&fit=bounds)
![Strategic Picks — Prioritising the Right Agentic Use Cases [2/6]](https://cdn.slidesharecdn.com/ss_thumbnails/session-2-250902135931-4c97717a-thumbnail.jpg?width=560&fit=bounds)
The technical case for a semantic web
- 1. A Semantic Web Primer ™ In partnership with
- 2. History & Perspective “We might hope to see the finances of the Union as clear and intelligible as a merchant's books, so that every member of Congress and every man of any mind in the Union should be able to comprehend them, to investigate abuses, and consequently to control them.” Thomas Jefferson, 1802
- 3. History & Perspective Web 1.0 Read Only Visual Human Presentation Web 2.0 Interactive Social Applications Web 3.0 “Web of Data”
- 4. Graph Theory Problem: Traverse each bridge once and only once in touring the city Solution: Zero or two nodes of odd degree Konigsburg, Prussia, 1735
- 5. Web 3.0 – The GGG RelFinder SpaceTimeFacebook Graph Search Diseasome Linked Life Data iDrove.itEdamam TBL 2009
- 6. Google Gets It Google Knowledge Graph Geosearch Demo The Knowledge Graph
- 7. The Resource Description Framework (RDF) http://www.example.org/~joe/contact.rdf#joesmith http://www.example.org/~joe/ “Object” resource (joe's home page) “Subject” resource (joesmith in RDF) Can be a literal... http://xmlns.com/foaf/0.1/homepage “Predicate” resource (FOAF relationship) URL URN URI
- 8. Story in RDBMS ID Name Type Legs Fur 001 Harrison Human 2 No 002 Layla Dog 4 Yes 003 MyBall Ball N/A N/A Concept: Harrison has a dog friend named Layla whose favorite activity is chasing a ball ObjectTable ID1 ID2 001 002 002 001 FriendshipTable Type1 Type2 Human Mammal Dog Mammal Ball Toy ID1 ID2 002 003 SubclassTable FavActivityTable
- 10. Story in OODBMS Concept: Harrison has a dog friend named Layla whose favorite activity is chasing a ball DogDog HumanHuman BallBall ToyToy 001001 002002 HarrisonHarrison LaylaLayla22 44 TrueTrueFalseFalse 003003 MammalMammal MyBallMyBall foaf:friend isA isA ofType ofType hasName numLegs hasFur hasName numLegs hasFur hasName ofType isA chases Class (Concept) Sub-Class Object (Individual) Attribute (Property)
- 11. Atomic (axiomatic) Concepts ID Name Type Legs Fur 001 Harrison Human 2 No 002 Layla Dog 4 Yes 003 MyBall Ball N/A N/A ID1 ID2 002 003 Concept (dog) Instance (Layla) Favorite Activity Name RDBMS OODBMS
- 12. Reuse Time to Implementation Diminishes with each Phase & Domain
- 13. WWW Contrast
- 14. Quantitative B-Tree Index: M x O(log2 N) Graph: 1 x O(log2 N) – plus pointer traversalRef: Michel Domenjoud, blog.octo.com
- 15. Qualitative Horizontal Scale (Complexity) VerticalScale(Size) Oliver, Andrew C. "Which Freaking Database Should I Use?" InfoWorld. InfoWorld, 02 Aug. 2012. Web. 07 Oct. 2013. Gonzalez, Rob. "Two Kinds of Big Data." Semanticweb.com. Semanticweb.com, 06 Sept. 2011. Web. 07 Oct. 2013. GGG
- 16. The “Ontology” (Vocabulary) TaxonomyTaxonomy: Organizational hierarchy based upon similarities in properties OntologyOntology: Set of representational primitives (classes, attributes & relationships) with which to model a domain of knowledge or discourse* HumanHuman MammalMammal Novel Stringtitle Human Authorprofession Novel Authorcreator NameName NovelNovel DocDoc AuthorAuthor NameName Human Stringname Key pointKey point: The triple-store's construction enables the extraction of meaning through inference (description logic), ie, “Smart Data” *Encyclopedia of Database Systems, L. Liu et al, 2009
- 17. Summary Aspect Web 2.0 Web 3.0 Unification Join Built-in Retrieval Keyword indexing Pointer traversal Query Keyword matching Inference Citizens Documents Data Polysemy & Synonymy Yes No Construct Table Triple (how) Descriptor URL - where URI = URL + URN (what) Data Aggregated Connected Flexibility Low High Dynamic Relationships Meaning
- 18. Ian Robinson, Neo4J Ian Robinson
- 19. Basic (very) Architecture Unstructured Data Structure-er (ETL) Triple-store (OOBDMS) Inference and rendering engine SPARQL Web Server
- 20. Open Government Use Case Database Search ORM DCAT Data Portal .NET (wrapper) PHPPylons Framework Publish Aggregate Project 2011 2014 Consume
- 21. Not a new idea... LOD Cloud, October 2007
- 22. But It Is Growing Ref: lod-cloud.net
- 24. Testimonials This inference capability makes both the journalist tagging and the triple store powered SPARQL queries simpler and indeed quicker than a traditional SQL approach. Dynamic aggregations based on inferred statements increase the quality and breadth of content across the site. The RDF triple approach also facilitates agile modeling, whereas traditional relational schema modeling is less flexible and also increases query complexity. -BBC Because there haven't been graph models in the market-leading relational databases, the graph approach has been mostly overlooked by corporate users. But it shouldn't be ignored anymore, he added: Graphs provide "a good representation of the data structure with which you're dealing." -Robin Bloor, the Bloor Group, July 2013 We believe that Graph Search is part of a trend that is much bigger than Facebook, and more widespread than search. Facebook is tapping into a fundamentally new way to exploit the information that exists in all the world’s databases. In this post, we will look at the Facebook announcement from a different angle, that of connected data: a growing trend that is on the verge of changing how companies large and small understand their data. -Andreas Kollegger, Jan 2013 The Web as we know it today will not be the Web as we know it tomorrow. The Web of today is oriented towards the universal accessibility of files (e.g. web pages, images). The Web of today can be thought of as a large-scale, distributed file system. The Web of tomorrow will encode any datum (e.g. strings, integers, dates). The Web of tomorrow can be thought of as a large-scale, distributed database. -Marko Rodriguez, Los Alamos, 2009 The construction and structure of graphs or networks is the key to understanding the complex world around us. -Albert Laszlo, “Linked: The New Science of Networks” Today, most data is being managed in traditional relational databases. These databases have trouble scaling to accommodate the increase in data volume, variety, and complexity. It is also difficult and expensive for these relational databases to provide answers to clinical questions in a timely manner. These relational databases are not a viable solution for solving this new category of data challenges. KnowMED web site. While traditional, relational data warehousing and federation approaches can scale well and are effective for many core data storage and access requirements, such approaches often fail when facing the dynamic changes and the inherent complexity of data integration requirements for Healthcare / Life Sciences (HCLS) research. Semantic integration methods assure coherence, harmonize synonyms and different terminologies, and provide an extensible, flexible data integration platform and interactive knowledge base for relevant network analysis. -IO Informatics
- 25. Drivers for a Paradigm Shift 80% of data being generated is unstructured. * *Dennis McCatherty, Baseline magazine **CFO.com, http://www3.cfo.com/article/2012/2/analytics_big-data-business-intelligence ● Hobbled analytics ● Poor collaboration ● Opacity ● TCO ● Performance 11:1** Silo IoT $$$$
- 27. Enablers ● Necessity - W3C Use Cases ● Tools ● Accelerators & Incubators ● Awareness ● Money
- 28. Community & Regulatory ● DTC – Data Transparency Coalition (Data Transparency 2013, Sep 11-12) ● UDI – Unique Device Identification (FDA) ● ACA – Affordable Care Act (administrative simplification clause) ● FITA – Foundation for Information Technology Accessibility ● LEI – Legal Entity Identifier (mandate) ● FIBO – Financial Industry Business Ontology (Dodd-Frank) ● ACT – Advisory Committee on Transparency (Sunlight Foundation) ● Schema.org ● DATA – Digital Accountability and Transparency Act ● CMS – Centers for Medicaid and Medicare Services ● W3C eGovernment ● Executive Order Specifically, this Memorandum requires agencies to collect or create information in a way that supports downstream information processing and dissemination activities. This includes using machine readable and open formats, data standards, and common core and extensible metadata for all new information creation and collection efforts. OMB Memorandum, M-13-13, May 9, 2013
- 29. Pew Study, May 2010
- 30. LOD vs Graphs Open Data Certificate Raw Pilot Standard Expert
- 31. Unintended consequences (of the good kind) ● Other domains and business use cases – Supply chain – Philanthropy – Social – Investor relations – Word search – Real Estate – Arts & humanities – E-Commerce (“perfect capitalism”¹) – Education ²Google Semantic Search by David Amerland ¹The Physics of the Future by Michio Kaku ● Other benefits – SEO - “The Knowledge Graph does away with the inherent ambiguity of conventional search at Google's search box”² – Accessible community driven content – Unleash the full power of “big data” – Unanticipated reuse
- 32. The Way Forward Get the word out Facilitate ontology development Build POC's Nurture community and industry involvement Innovate! LOD Goes Global
- 33. Further Reading ● Use Cases ● Linked Data e-book ● Semantic Community ● Web Science Trust ● STI International ● Schema.org ● semanticweb.com ● World Wide Web Consortium ● Depository Trust and Clearing Corporation ● Freebase ● Jena
- 34. Terminology Term Definition RDF Resource Description Framework, a way of representing information on the web (ie, a set of statements), successor to XML, provides the ontology syntax (framework) for describing resources OWL Web Ontology Language - Standard framework for describing ontologies, based upon XML for the low-level syntax and RDF/RDFS for the mid-level syntax RDFS RDF Schema, recommended structure for RDF resources R2RML Relational to RDF Mapping Language - W3C sanctioned language for expressing customized mappings from relational databases to RDF data sets, D2RQ is the same thing in open source (not as capable) Open/Closed World Open: Any statement not explicitly known to be true or false is considered unknown (OWL). Closed: any statement that is not known to be true is false (SQL). Concept Description logic synonym for “Class” Jena Apache foundation PPMC (Podling Project Management Committee) to develop a semantic app framework (Java API) RIF Rule Interchange Format – W3C recommendation SPARQL SPARQL Protocol and RDF Query Language – query language for RDF data stores, an “endpoint” being a resource that can accept such queries and return results SWRL Semantic Web Rule Language – combines OWL & RML FIBO Financial Industry Business Ontology SBVR Semantics of Business Vocabulary and Rules - OMG developed human readable business modeling language OMG Object Management Group - Nonprofit industry consortium dedicated to data interoperability (ex. UML, MDA, CORBA) OASIS Organization for the Advancement of Structured Information Standards - Standards body for web services, e-commerce, supply-chain and many other domains (including
- 35. FacetApp SmartData Platform OntoApp Toolkit FacetApp Platform Client AppClient App OntologyOntology Client AppClient App OntologyOntology Enterprise Applications OntoApp ServerServer OntoApp ServerServer InferenceInference ServerServer InferenceInference ServerServer Inferen ce Engine Inferen ce Engine Harness Reusable Objects for Fast, Scalable Model-driven Enterprise Apps Configure enterprise applications in a model vs. programming low level source code. Copyright 2013 FacetApp LLC, Confidential and Proprietary
- 36. SmartData Breakthrough Maintained by One Model Application DataApplication Data Application LogicApplication Logic Technical Programmers Database Admins Data Scientist Focus is where it should be – on maximizing ROI
- 38. FacetApp API
- 39. Thank You! Tony Dobaj (doe-bye) [email protected] 720-277-2052