First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
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This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
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First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
- 2. Announcement: New training course Designing a Semantic Technology Proof of Concept with GraphDB™ 13 December 2016 | 10am CET | 9am GMT | 11am EET Course contents: • 3 hours worth of tailored video materials on Semantic Technologies • 2 hours worth of SPARQL exercises and sample solutions • 4 hours live interactive session designing a sample Proof of Concept with GraphDB • 1 hour 1-on-1 consulting follow-up session Topics Covered #2 • Modeling data using the Resource Description Framework • Applying flexible schemas on schema-less data • Using simple ontologies for automated reasoning on data • Effectively using and configuring RDF databases & repositories • Transforming, cleaning up and linking heterogeneous data with OntoRefine • Loading distributed data in one unified data layer • Querying and updating RDF data with SPARQL • Linked Open Data: how to link data and useful LOD resources • Data exploration and data visualization with GraphDB™ • Domain-specific use cases of adopting semantic technologies
- 3. Presentation Outline • Modeling data using RDF • Applying flexible schema on schema-less data • Ontologies for automated reasoning on data • SPARQL query types and modifiers • Graph databases and triplestores • Choosing an appropriate database solution • Niche-specific reference projects • S4 for on-demand low-cost smart data management • S4 REST services • S4 Knowledge graph #3
- 4. MODELING DATA USING RDF #4
- 5. Example #5 Information can be described through relationships between things, e.g. • The relationship between the movie Thor and Kenneth Branagh is that Kenneth directed the movie. • The relationship between the movie Thor and the date May 6, 2011 is that the movie was released (in the US) on that date. Such descriptions are formalized using the Resource Description Framework.
- 6. Resource Description Framework (RDF) is a graph data model that • Formally describes the semantics, or meaning, of information • Represents metadata, i.e., data about data RDF data model consists of triples • That represent links (or edges) in an RDF graph • Where the structure of each triple is Subject, Predicate, Object Example triples: ‘mdb:’ refers to the namespace ‘http://example.org/movieDB/’ so that ‘mdb:Thor’ expands to <http://example.org/movieDB/Thor> a Universal Resource Identifier (URI). What is RDF? Subject Predicate Object mdb:Thor mdb:directedBy mdb:KennethBranagh . mdb:Thor mdb:releaseDate 2011-05-06 . 6 #6
- 7. An Example of an RDF Model 7 #7
- 8. An Example of an RDF Model 8 #8
- 9. An Example of an RDF Model 9 #9
- 10. An Example of an RDF Model 10 #10 But RDF is more than just a tool for representing information that we already know!
- 11. FLEXIBLE SCHEMA & AUTOMATED REASONING #11
- 12. RDF Schema (RDFS) • Adds – Concepts such as Resource, Literal, Class, and Datatype – Relationships such as subClassOf, subPropertyOf, domain, and range • Provides the means to define – Classes and properties – Hierarchies of classes and properties • Includes “entailment rules”, i.e., axioms to infer new triples from existing ones What is RDFS? 12 #12
- 13. Applying RDFS To Infer New Triples mdb:directedBy rdfs:domain mdb:Movie ; rdfs:range mdb:Director . mdb:Thor mdb:directedBy mdb:KennethBranagh . mdb:Director rdfs:subClassOf mdb:Human . mdb:Thor a mdb:Movie . mdb:KennethBranagh a mdb:Director . mdb:KennethBranagh a mdb:Human . 13 #13
- 14. An ontology is a formal specification that provides sharable and reusable knowledge representation. Other examples of such formal specifications include: • Taxonomies • Vocabularies • Thesauri • Topic Maps • Logical Models #14 What is in an Ontology?
- 15. What is in an Ontology? An ontology specification includes descriptions of • Concepts and properties in a domain • Relationships between concepts • Constraints on how the relationships can be used • Individuals as members of concepts 15 #15
- 16. The Benefits of an Ontology Ontologies provide: • A common understanding of information • Explicit domain assumptions These provisions are valuable because ontologies: • Support data integration for analytics • Apply domain knowledge to data • Support interoperation of applications • Enable model-driven applications • Reduce the time and cost of application development • Improve data quality, i.e., metadata and provenance 16 #16
- 17. OWL Overview The Web Ontology Language (OWL) adds more powerful ontology modelling means to RDF/RDFS • Providing – Consistency checks: Are there logical inconsistencies? – Satisfiability checks: Are there classes that cannot have instances? – Classification: What is the type of an instance? • Adding identity equivalence and identity difference – Such as, sameAs, differentFrom, equivalentClass, equivalentProperty • Offering more expressive class definitions, such as – Class intersection, union, complement, disjointness – Cardinality restrictions • Offering more expressive property definitions such as, – Object and datatype properties – Transitive, functional, symmetric, inverse properties – Value restrictions 17 #17
- 18. SPARQL #18
- 19. What is SPARQL? SPARQL is a SQL-like query language for RDF graph data with the following query types: • SELECT which returns tabular results • CONSTRUCT creates a new RDF graph based on query results • ASK which returns ‘yes’ if the query has a solution, otherwise ‘no’ • DESCRIBE which returns RDF graph data about a resource; useful when the query client does not know the structure of the RDF data in the data source • INSERT which inserts triples into a graph • DELETE which deletes triples from a graph. Ontotext, AD and Keen Analytics, LLC. All Rights Reserved 19 19
- 20. Using SPARQL to Insert Triples To create an RDF graph, perform these steps: • Define prefixes to URIs with the PREFIX keyword • Use INSERT DATA to signify you want to insert statements. Write the subject-predicate-object statements (triples). • Execute this query. PREFIX mdb: <http://example.org/movieDB/> INSERT DATA { mdb:Thor mdb:starring mdb:ChrisHemsworth; mdb:starring mdb:NataliePortman, mdb:AnthonyHopkins. } #20
- 21. Using SPARQL to Select Triples To access the RDF graph you just created, perform these steps: • Define prefixes to URIs with the PREFIX keyword. • Use SELECT to signify you want to select certain information, and WHERE to signify your conditions, restrictions and filters. • Execute this query. PREFIX : <http://example.org/movieDB> SELECT ?subject ?predicate ?object WHERE {?subject ?predicate ?object } Subject Predicate Object mdb:Thor mdb:directedBy mdb:KennethBranagh mdb:Thor mdb:releaseDate 2011-05-06 mdb:Thor mdb:starring mdb:ChrisHemsworth mdb:Thor mdb:starring mdb:NataliePortman mdb:Thor mdb:starring mdb:AnthonyHopkins #21
- 22. Using SPARQL to Find Prolific Actors To find actors who stars in multiple movies, first find out if such an actor exists: • Define prefixes to URIs with the PREFIX keyword • Use ASK to discover whether an actor is starring in two (or more) different movies • Use WHERE to signify those conditions. YES PREFIX mdb: <http://example.org/movieDB/> PREFIX owl: <http://www.w3.org/2002/07/owl#> ASK WHERE { ?movie1 a mdb:Movie; mdb:starring ?actor . ?movie2 a :Movie; mdb:starring ?actor . FILTER NOT EXISTS {?movie1 owl:sameAs ?movie2} } Ontotext, AD and Keen Analytics, LLC. All Rights Reserved #22
- 23. Using SPARQL to Find Prolific Actors Now that we know at least one such actor exists, perform these steps to find each actor and pair of movies: • Define prefixes to URIs with the PREFIX keyword • Use SELECT to signify you want to select an actor and 2 movies, and WHERE to signify your conditions. ?actor ?movie1 ?movie2 mdb:AnthonyHopkins mdb:Noah mdb:Thor #23 PREFIX mdb: <http://example.org/movieDB/> PREFIX owl: <http://www.w3.org/2002/07/owl#> SELECT ?actor ?movie1 ?movie2 WHERE { ?movie1 a mdb:Movie; mdb:starring ?actor . ?movie2 a :Movie; mdb:starring ?actor . FILTER NOT EXISTS {?movie1 owl:sameAs ?movie2} }
- 25. Graph databases Graph databases store data in terms of entities and the relationships between entities. They are particularly suited for interconnected data, as they cater for: • Integration of heterogeneous data sources • Hierarchical or interconnected datasets • Dynamic data models / schema evolution • Relationship centric analytics / discovery • Path traversal / navigation, sub-graph pattern matching #25
- 26. Semantic graph databases A variant on graph databases are RDF databases (triplestores, semantic graph databases) which store data in triples of the format subject-predicate-object. Advantages of semantic graph databases include: • Simple, graph based data model • Exploratory queries against unknown schema • Agile schema / schema-less • Rich, semantic data models (schemas) • Easily map between data models (schemas) • Global identifiers of nodes & relations • Inference of implicit facts, based on rules • Compliance to standards (RDF, SPARQL), no vendor lock-in • Easy to publish / consume open Knowledge Graphs (Linked Data) #26
- 27. GraphDB by Ontotext • High performance semantic graph database, 10s of billions of triples • Full compliance to W3C standards (RDF, SPARQL, OWL, …) • Various inference profiles, including custom rules • Extensions – Geo-spatial, RDF Rank, full-text search, Blueprints/Gremlin, 3rd party plugins • Tooling for DBAs #27
- 28. GraphDB™ Editions • GraphDB™ Free • GraphDB™ Standard • GraphDB™ Cloud • GraphDB™ as-a-Service (S4) • GraphDB™ Enterprise #28
- 29. Fully Managed Database-as-a-Service • Low-cost DBaaS for Ontotext GraphDB • Ideal for small to moderate data & query volumes – database options: 10M (free), 50M, 250M & 1B triples • Instantly deploy new databases when needed – Easily scale up / down as data volume changes • Zero administration – automated operations, maintenance & upgrades • Faster experimentation & prototyping, reduced TCO #29
- 31. Choosing an appropriate database solution From experimentation to production • Priorities: cost, ease of deployment, performance, availability • GraphDB options: Free, Standard, Enterprise • Deployment: on premise, AWS cloud, database-as-a-service • Seamless upgrade paths – all options based on the same engine #31 Learning Prototype Pilot Production
- 32. Choosing an appropriate database solution Learning • Priorities – Free – Easy & quick to set up, “sandbox” environment • Recommended – Database-as-a-Service (free 10M triples) – GraphDB Free #32 Learning Prototype Pilot Production
- 33. Choosing an appropriate database solution Prototype • Priorities – Free / low-cost – Easy & quick to set up, “sandbox” environment • Recommended – GraphDB Free – Database-as-a-Service (10M – 50M triples) #33 Learning Prototype Pilot Production
- 34. Choosing an appropriate database solution Pilot • Priorities – Low-cost – Performance and scalability • Recommended – GraphDB Standard • Also consider – Database-as-a-Service (250M – 1B triples) – GraphDB Free #34 Learning Prototype Pilot Production
- 35. Choosing an appropriate database solution Production • Priorities – Performance and scalability – High availability • Recommended – GraphDB Enterprise • Recommended – GraphDB Standard #35 Learning Prototype Pilot Production
- 37. Profile • Mass media broadcaster founded in 1922 • 23,000 employees and over 5 billion pounds in annual revenue. Goals • Create a dynamic semantic publishing platform that assembled web pages on-the-fly using a variety of data sources • Deliver highly relevant data to web site visitors with sub- second response Challenges • BBC journalists author and publish content which is then statistically rendered. The costs and time to do this were high. • Diverse content was difficult to navigate, content re-use was not flexible • User experience needed to be improved with relevant content "The goal is to be able to more easily and accurately aggregate content, find it and share it across many sources. From these simple relationships and building blocks you can dynamically build up incredibly rich sites and navigation on any platform." John O’Donovan Chief Technical Architect BBC #37
- 38. Future Media BBC MMXII 10 000+ Dynamic Aggregations
- 39. Profile • Top 3 business media • Focused both on B2C publishing and B2B services Goals • Create a horizontal platform for both data and content based on semantics and serve all functionality through it Challenges • Critical part of the entire workflow • Multiple development projects in parallel with up to 2 months time between inception and go live • GraphDB used not only for data, but for content storage as well • Horizontal platform with focus on organizations, people, GPEs and relations between them • Automatic extraction of all these concepts and relationships • Separate stream of work for a user behavior based recommendation of relevant content and data across the entire media Financial Times #39
- 40. Profile • Established in 1961 to enable federal agencies • Specializes in logistics, financial, infrastructure & information management Goals • Unlock large collections of complex documents • Improve analyst productivity • Create an application they can sell to US Federal agencies Challenges • Analysts taking hours to find, download and search documents, using inaccurate keyword searches • Needed a knowledge base to search quickly and guide the analysts – highly relevant searches • Extracts knowledge from collection of documents • Uses GraphDB to intuitively search and filter • Knowledge base used to suggest searches • Hyper speed performance • Huge savings in analyst time • Accurate results LMI #40
- 41. Profile • Global, Bio-pharma company • $28 billion in sales in 2012 • $4 billion in R&D across three continents Goals • Efficient design of new clinical studies • Quick access to all of the data • Improved evidence based decision-making • Strengthen the knowledge feedback loop • Enable predictive science Challenges • Over 7,000 studies and 23,000 documents are difficult to obtain • Searches returning 1,000 – 10,000 results • Document repositories not designed for reuse • Tedious process to arrive at evidence based decisions AstraZeneca #41
- 42. Profile • Euromoney Institutional Investor PLC, the international online information and events group Goals • Create a horizontal platform to serve 100 different publications • create a new publishing and information platform which would include the latest authoring, storing, and display technologies including, semantic annotation, search and a triple store repository Challenges • Different domains covered • Sophisticated content analytics incl. Relation, template and scenario extraction • Analytics of reports and news of various domains • Extraction of sophisticated macro economic views on markets and market conditions; trades, condition and trade horizons, assets, asset allocations, etc. • Multi-faceted search • Completely new content and data infrastructure Euromoney #42
- 43. S4 - SELF-SERVICE SEMANTIC SUITE #43
- 44. • Capabilities for Smart Data management and analytics – Text analytics for news, life sciences and social media – RDF graph database as-a-service – Access to large open knowledge graphs • Available on-demand, anytime, anywhere – Simple RESTful services • Simple pay-per-use pricing – No upfront commitments Self-service semantic suite (S4) #44
- 45. • Enables quick prototyping – Instantly available, no provisioning & operations required – Focus on building applications, don’t worry about software + infrastructure • Free tier! • Easy to start, shorter learning curve – Detailed documentation, various add-ons, SDKs and demo code • Based on enterprise technology by Ontotext S4 Benefits #45
- 46. Support and FAQ’s support@ontotext.com Additional resources: Ontotext: Community Forum and Evaluation Support: http://stackoverflow.com/questions/tagged/graphdb GraphDB Website and Documentation: http://graphdb.ontotext.com Whitepapers, Fundamentals: http://ontotext.com/knowledge-hub/fundamentals/ SPARQL, OWL, and RDF: RDF: http://www.w3.org/TR/rdf11-concepts/ RDFS: http://www.w3.org/TR/rdf-schema/ SPARQL Overview: http://www.w3.org/TR/sparql11-overview/ SPARQL Query: http://www.w3.org/TR/sparql11-query/ SPARQL Update: http://www.w3.org/TR/sparql11-update #46
- 47. For Further Information • Georgi Georgiev, Head of Global Alliances Development – georgiev@ontotext.com – 359.882.885.636 • Ilian Uzunov, Europe Sales and Business Development – Ilian.uzunov@ontotext.com – 359.888.772.248 • Peio Popov, North America Sales and Business Development – peio.popov@ontotext.com – 1.929.239.0659 #47
- 48. Announcement: New training course Designing a Semantic Technology Proof of Concept with GraphDB™ 13 December 2016 | 10am CET | 9am GMT | 11am EET Course contents: • 3 hours worth of tailored video materials on Semantic Technologies • 2 hours worth of SPARQL exercises and sample solutions • 4 hours live interactive session designing a sample Proof of Concept with GraphDB • 1 hour 1-on-1 consulting follow-up session Topics Covered #48 • Modeling data using the Resource Description Framework • Applying flexible schemas on schema-less data • Using simple ontologies for automated reasoning on data • Effectively using and configuring RDF databases & repositories • Transforming, cleaning up and linking heterogeneous data with OntoRefine • Loading distributed data in one unified data layer • Querying and updating RDF data with SPARQL • Linked Open Data: how to link data and useful LOD resources • Data exploration and data visualization with GraphDB™ • Domain-specific use cases of adopting semantic technologies
- 49. The End
Editor's Notes
- Welcome everyone to the Ontotext GraphDB Fundamentals Webinar [Click]
- What is RDF? <click> Resource Description Framework, more commonly known as RDF, is a graph data model <click> that formally describes the semantics, or meaning of information. <click> It also represents metadata, that is, data about data. <click> RDF consists of triples. <click> These triples are based on an Entity Attribute Value, or EAV, model <click> in which the subject is the entity, the predicate is the attribute, and the object is the value. <click> Each triple has a unique identifier known as the Uniform Resource Identifier, or URI. URI’s look like web page addresses. <click> The parts of a triple, the subject, predicate, and object, represent links in a graph. <click> For example, <click> <click> “Fred hasSpouse Wilma” is an example of a triple. Fred is the subject, hasSpouse is the predicate and Wilma is the object. Also, in the next triple, “Fred hasAge 25,” Fred is the subject hasAge is the predicate and 25 is the object, or value.
- <click> In this example, we demonstrate how multiple triples link together to form an RDF model. As you can see, we are describing the characters and relationships from the Flintstones television cartoon series. Here we see the triple “WilmaFlintstone livesin Bedrock.” “Wilma Flintstone” is the subject, “lives in” is the predicate, and “Bedrock” is the object. “FredFlintstone livesin Bedrock” is another triple. So, we know the Flintstones live in the town of Bedrock, <click> which is part of Cobblestone County, <click> in Prehistoric America. <click> Fred Flintstone is married to Wilma and <click> they have a child Pebbles. <click> Fred works for the Rock Quarry company and <click> Wilma’s mother is Pearl Slaghoople. <click> Pebbles Flintstone is married to Bamm-Bamm Rubble <click> who is the child of Barney and Betty Rubble. Thus, as you can see, many triples form an RDF model.
- <click> In this example, we demonstrate how multiple triples link together to form an RDF model. As you can see, we are describing the characters and relationships from the Flintstones television cartoon series. Here we see the triple “WilmaFlintstone livesin Bedrock.” “Wilma Flintstone” is the subject, “lives in” is the predicate, and “Bedrock” is the object. “FredFlintstone livesin Bedrock” is another triple. So, we know the Flintstones live in the town of Bedrock, <click> which is part of Cobblestone County, <click> in Prehistoric America. <click> Fred Flintstone is married to Wilma and <click> they have a child Pebbles. <click> Fred works for the Rock Quarry company and <click> Wilma’s mother is Pearl Slaghoople. <click> Pebbles Flintstone is married to Bamm-Bamm Rubble <click> who is the child of Barney and Betty Rubble. Thus, as you can see, many triples form an RDF model.
- <click> In this example, we demonstrate how multiple triples link together to form an RDF model. As you can see, we are describing the characters and relationships from the Flintstones television cartoon series. Here we see the triple “WilmaFlintstone livesin Bedrock.” “Wilma Flintstone” is the subject, “lives in” is the predicate, and “Bedrock” is the object. “FredFlintstone livesin Bedrock” is another triple. So, we know the Flintstones live in the town of Bedrock, <click> which is part of Cobblestone County, <click> in Prehistoric America. <click> Fred Flintstone is married to Wilma and <click> they have a child Pebbles. <click> Fred works for the Rock Quarry company and <click> Wilma’s mother is Pearl Slaghoople. <click> Pebbles Flintstone is married to Bamm-Bamm Rubble <click> who is the child of Barney and Betty Rubble. Thus, as you can see, many triples form an RDF model.
- <click> In this example, we demonstrate how multiple triples link together to form an RDF model. As you can see, we are describing the characters and relationships from the Flintstones television cartoon series. Here we see the triple “WilmaFlintstone livesin Bedrock.” “Wilma Flintstone” is the subject, “lives in” is the predicate, and “Bedrock” is the object. “FredFlintstone livesin Bedrock” is another triple. So, we know the Flintstones live in the town of Bedrock, <click> which is part of Cobblestone County, <click> in Prehistoric America. <click> Fred Flintstone is married to Wilma and <click> they have a child Pebbles. <click> Fred works for the Rock Quarry company and <click> Wilma’s mother is Pearl Slaghoople. <click> Pebbles Flintstone is married to Bamm-Bamm Rubble <click> who is the child of Barney and Betty Rubble. Thus, as you can see, many triples form an RDF model.
- What is RDFS? <click> RDF Schema, more commonly known as RDFS, adds schema to the RDF. <click> It defines a metamodel of concepts like Resource, Literal, Class, and Datatype and relationships such as subClassOf and subPropertyOf, domain, and range. <click> RDFS provides a means for defining the classes, properties, and relationships in an RDF model and organizing these concepts and relationships into hierarchies. <click> RDFS specifies entailment rules or axioms for the concepts and relationships. These rules can be used to infer new triples, as we show on the next slide.
- Looking at this example, we see how new triples can be inferred by applying RDFS rules to a small RDF/RDFS model. In this model, we use RDFS to define that the hasSpouse relationship is restricted to humans. <click> And as you can see, human is a subclass of mammal. <click> If we assert that Wilma is Fred’s spouse using the ‘hasSpouse’ relationship, then we can infer that <click> Fred and Wilma are human because, in RDFS, the hasSpouse relationship is defined to be between humans. And, <click> because we also know humans are mammals, we can further infer that Fred and Wilma are mammals.
- Let’s go a little further and talk about what is in an ontology. <click> An ontology specification includes descriptions of <click> Concepts and properties in a domain <click> Relationships between concepts <click> Constraints on how the relationships can be used <click> and, Individuals as members of concepts <click> In the example below, we can classify the two individuals, Fred and Wilma, in a class of type Person, and we also know that a Person is a Mammal. Fred works for the Slate Rock Company and the Slate Rock Company is of type Company, so we also know that Person worksFor Company.
- <click> So, why develop an ontology? Well, let’s talk about the benefits. <click> First, ontologies are very useful in gaining a common understanding of information <click> and making assumptions explicit in ways that can be used to support a number of activities. <click> These provisions, a common understanding of information and explicit domain assumptions, are valuable because ontologies <click> support data integration for analytics, <click> apply domain knowledge to data, <click> support application interoperability, <click> enable model driven applications, <click> reduce time and cost of application development, <click> and improve data quality by improving meta data and provenance.
- The Web Ontology Language, or OWL, adds more powerful ontology modeling means to RDF and RDFS. <click> Thus, when used with OWL reasoners, like in GraphDB, it provides consistency checks, such as, are there any logical inconsistencies? It also provides satisfiability checks, such as are there classes that cannot have instances? And OWL provides classification such as the type of instance. <click> OWL also adds identity equivalence and identity difference, such as, sameAs, differentFrom, equivalentClass, and equivalentProperty. <click> In addition, OWL offers more expressive class definitions, such as, class intersection, union, complement, disjointness and cardinality restrictions. <click> Finally, OWL offers more expressive property definitions, such as, object and datatype properties, transitive, functional, symmetric, inverse properties, and value restrictions.
- What is SPARQL? <click> SPARQL is a SQL-like query language for RDF data. SPARQL queries can produce result sets that are tabular or RDF graphs depending on the kind of query used. <click> The SPARQL SELECT is similar to the SQL SELECT in that it produces Tabular result sets. <click> The SPARQL CONSTRUCT creates a new RDF graph based on query results. <click> The ASK query returns Yes or No depending on whether the query has a solution. <click> DESCRIBE returns the RDF graph data about a resource. This is, of course, useful when the query client doesn’t know the structure of the RDF data in the data source. <click> INSERT adds triples to a graph, <click> and, DELETE removes triples from a graph.
- Let’s use SPARQL, the query language for RDF graphs, to create a graph. <click> To write the SPARQL query which creates an RDF graph, perform these steps: <click> First, define prefixes to URIs with the PREFIX keyword. In the example below, we set bedrock as the default namespace for the query. <click> Next, use INSERT DATA to signify you want to insert statements. Write the subject predicate object statements. <click> Finally, execute this query. As you can see in the example shown in the gray box, we wrote a query which included PREFIX, INSERT DATA, and several subject predicate object state
- <click> Now, let’s write a SPARQL query to access the RDF graph you just created. <click> First, define prefixes to URIs with the PREFIX keyword. <click> As in the earlier example, we set bedrock as the default namespace for the query. <click> Next, use SELECT to signify you want to select certain information, and WHERE to signify your conditions, restrictions, and filters. <click> Finally, execute this query. As you can see in this example shown in the gray box, we wrote a SPARQL query which included PREFIX, SELECT, and WHERE. <click> The orange box displays the information which is returned in response to the written query. We can see the familial relationships between Fred, Pebbles, Wilma, Roxy, and Chip. SPARQL is quite similar to SQL, however, unlike SQL which requires SQL schema and data in SQL tables, SPARQL can be used on graphs and does not need a schema to be defined initially.
- <click> In the following example, we will use SPARQL to find Fred’s grandchildren. First, we need to establish whether Fred has any grandchildren. <click> First, define prefixes to URIs with the PREFIX keyword. <click> Next, we use ASK to discover whether Fred has a grandchild, and WHERE to signify the conditions. As you can see in the query in the green box, Fred’s children’s children are his grandchildren. Thus the query is easily written in SPARQL by matching Fred’s children and then matching his children’s children. <click> The ASK query returns “Yes” so we know Fred has grandchildren.
- <click> Now that we know Fred has at least one grandchild, how do we find who are Fred’s grandchildren? That is, what are their names? <click> This question is easily answered by using the same query from the previous slide and replacing the ASK statement with a SELECT statement. <click> First, define prefixes to URIs with the PREFIX keyword. <click> Then, we use SELECT to signify that we want to select a grandchild, and WHERE to signify our conditions. The query results, reflected in the red box, tell us that Fred’s grandchildren are Roxy and Chip.
- The only restriction of Free is that it supports only 2 query client connections. Other than that, it is identical to Standard.
- LMI was founded in 1961 to provide solutions to enable federal government agencies. Specializes in logistics, acquisition and financial management, infrastructure management, information management and policy and program support.
- AstraZeneca is a world leader in bio-pharmaceuticals with over 28 billion in revenue and 4 billion invested in R&D. Innovation and creating great medicines are core values. To continue on their path to success, they needed to design new clinical studies efficiently through instant access to all of their data. By doing this, they could improve evidence based decision making and create a knowledge feedback loop through accessing historical clinical studies and related documents. But with over 7,000 studies and 23000 documents, the volume of unstructured data was overwhelming them. They would search for what they thought were relevant results and instead get anywhere between 1,000 and 10,000 results that needed review. The fact is their document repository was not designed for reuse. They had not extracted the meaning from their documents they needed. They had not created reusable meta data allowing for the search results to be highly targeted. The tedious process of document review was slowing down the innovation engine. They needed to reduce the onerous manual effort, gain complete visibility, decease the time to locate knowledge and arrive at instant analytical results. [click to build the slide] Ontotext was able to extract meaning from the unstructured documents, optimize their knowledge repository for flexible semantic searches – searches that leveraged newly created metadata. The data was stored in a way where it was optimized for navigation and retrieval. To do this, we used vocabularies for drugs and biomarkers while also creating a master databases linking all the information together in OWL. We indexed all of the disambiguated data allowing users to find targeted results from context-based search. In the end this created less patient & regulatory risk, used fewer resources to locate and analyze clinical data and enhanced the innovation process allowing AZ to create new drugs faster. All of their goals were met and they are using this system in production today.