Download Semantics - MarkLogic

D ata s h e e t
Semantics
Semantics describes MarkLogic’s ability to store graph data as RDF Triples—a new approach to modeling data that focuses on
relationships. Storing RDF enhances MarkLogic’s document model by providing a smart way to connect and enhance the JSON
and XML documents that MarkLogic stores. This is important for data integration and better querying. Semantics also provides
context for data by providing a smart way to store metadata. For example, consider a database that has information about
parts, and one part is listed with a size of “42.” But, where is the contextual information: What are the units of “42”? What is the
tolerance? Who measured it? When was it measured? Who can see this data? That contextual information is the semantics of
your data, and is easily stored in MarkLogic as RDF triples.
DATA is stored in Triples, expressed as:
Subject
: Predicate
QUERY with SPARQL, gives us a simple look up... and more! Find people who
live in (a place that’s in) England
: Object
John Smith
livesIn
London
London
isIn
England
“John Smith”
livesIn
“London”
isIn
“England”
Based on W3C standards,
triples are written using RDF
(Resource Description
Framework), and queried
using SPARQL.
livesIn
RDF and SPARQL
The standard way to represent semantic data is with RDF Triples (Resource Description Framework), and the standard query
language is SPARQL. Triples are derived from subject-predicate-object constructions based on entities (people, places, or things)
and their relationships. One example is, “John lives in London.” The database can also infer new facts, such as “John lives in
England.” These simple facts can all be linked together to form a graph of hundreds of billions of facts and relationships. This
simple model is ideal for describing the nature and meaning of relationships.
people
Model: Person
addresses
Model: Address
first_name
string
street_number
string
last_name
string
street_name1
string
address1_id
integer
street_name2
string
address2_id
integer
city_id
integer
states
JSON
Model: State
cities
Model: City
name
string
name
string
state_id
integer
city_id
integer
Multi-Model Database: Documents, Data, and Triples
• Documents + Data + Triples – MarkLogic natively stores documents, data, and triples together
• Fully Composable – MarkLogic is truly multi-model—you can write fully composable queries across all data types
• Enterprise Ready – Full set of enterprise features features, including ACID transactions, security, disaster recovery, etc.
• Sophisticated Indexing – Specialized triple index to improve query power and performance
• Enormous Scale – MarkLogic stores hundreds of billions of triples
Semantics
M a r k l o g i c D ata s h e e t
Supported Features
• Store and manage hundreds of billions of RDF triples
• Query across documents, data, and triples
• Semantic inference using rule sets at query time
-- Supplied rule sets for RDFS, RDFS+, and OWL Horst
--
• Triple index for sub-second search results
Support for user-defined rule sets
• Triple cache for high performance across large clusters
• Full support for SPARQL 1.1
• Bulk-load triples via MarkLogic Content Pump (mlcp)
• SPARQL endpoint and graph store protocol support
• Provenance and reification by adding metadata
• SPARQL from server-side JavaScript, Node.js
• XQuery helper modules for serializations and transitive closures
• Support for Jena and Sesame APIs
• Updates, aggregates via MarkLogic APIs
• Full integration with semantics technology partners (Smartlogic,
Pool Party, Cambridge Semantics)
• Graph traversal with property paths and transitive closures
• MarkLogic enterprise features: ACID transactions, certified
security (at document/triple level), high availability and disaster
recovery, scalability and elasticity
DOCUMENTS
TRIPLES
(Entities: “Customers,” “Orders”)
(Relationships: “Customer placed Order 8001”)
"Customer_ID": 1001,
"Order_ID": 8001,
Customer
placed
Order
contains
bought
Product
How Semantics Helps With Data Integration
In a relational database, relationships are weak. They are defined only by primary and foreign key relationships or obfuscated in
application code. Relational databases also make it hard to distinguish the real relationships from the spurious ones that exist
only to allow reconstruction of the entities that were broken apart across tables in the first place.
With triples, you can define the meaning of relationships. Consider the example above, which shows documents for “customers”
and “orders.” With triples, you can define what it means to “place” an order, what it means for an order to “contain” something,
and new inferences to say that a customer “bought” a certain product. Semantics makes such relationships all have meaning,
and that meaning will always stay intact, and can be leveraged, as you integrate data from different systems.
About MarkLogic
MarkLogic is the world’s best database for integrating data from silos, providing an operational and transactional Enterprise
NoSQL database platform that integrates data better, faster, with less cost. Visit www.marklogic.com for more information.
© 2016 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. This technology is protected by U.S. Patent No. 7,127,469B2, U.S. Patent No. 7,171,404B2, U.S.
Patent No. 7,756,858 B2, and U.S. Patent No 7,962,474 B2. MarkLogic is a trademark or registered trademark of MarkLogic Corporation in the United States and/or other
countries. All other trademarks mentioned are the property of their respective owners.
MARKLOGIC CORPORATION
999 Skyway Road, Suite 200 San Carlos, CA 94070
+1 650 655 2300 | +1 877 992 8885 | www.marklogic.com | [email protected]