Text mining is one technique used to uncover this intelligence. But where is this newly found knowledge stored, updated and queried? GraphDB™ (formerly OWLIM) is the world’s leading RDF Triplestore built on OWL (Ontology Web Language) standards.
It stores your data in the form of atomic facts expressed as subjects, predicates and objects which have relationships to other facts. GraphDB is the only triplestore that can perform semantic inferencing at scale allowing users to create new semantic facts from existing facts. It handles massive loads, queries and inferencing in real time.
This allows very easily merge of information from multiple sources including Linked Open Data or proprietary sources. The ability to recognize entities across multiple sources holds great promise helping to manage your data more effectively and pinpointing connections in your data that may be masked by slightly different entity references. Merging information from various sources produces more accurate results, a clearer picture of how entities are related to one another and the ability to improve the speed with which your organization operates.
GraphDB works with globally unique identifiers, the most popular variant of which are the widely used URLs. All data elements (objects, entities, concepts, relationships, attributes) are identified with URIs, allowing data from different sources to merge without collisions. By doing this, you can align the same real-world entity used in different data sources. The most standard and powerful predicate used to establish mappings between multiple URIs of a single object is owl:sameAs.
Tracking where data came from has so many important use cases. GraphDB maintains this level of detail. Users not only know the semantic facts but they know data sources, dates, levels of trust and other Metadata about the data.
It is based on materialised connections between free flowing, unstructured text, and data facts stored as database entities. These connections are extremely valuable as they link entities from the database to the documents that mention them denoting relationships from which they were extracted.When these connections exist, organizations can keep all of their data synchronized. The interconnected information space can be accessed through hybrid queries, combining the richness of the full-text search with the selectivity and precision of the databases.
Hundreds of billions of World knowledge are available for free in the Linked Open Data world about music, places, subjects of interest and products. When applied correctly, semantic facts can enhance your knowledge management and data discovery applications.
Graph databases are often referred as schema-less, where there is no schema that defines how database is organized. GraphDB supports all forms of metadata classification of data, express as ontologies, where ontologies are equated but limited to thesauri, taxonomic hierarchies of classes, class definitions and relations.
GraphDB can infer new knowledge from existing facts. This is called inferencing. Why is this important? You can create new facts from existing facts. Your queries run faster. Your results are more accurate. Not all graph databases support this capability and some apply different techniques to infer new semantic facts. The applications of inferencing span industries and use cases. Knowing that two people are connected through a series of other factual relationships can be helpful in identifying networks for a variety of purposes – for example social networks, fraud networks or terrorist networks. In physician referrals and clinical trials research, the ability to infer a doctor’s specialty based on the drugs prescribed can help you find doctors that you require. When analyzing economic markets, the ability to infer trading price points for commodities using weather and regional data may provide you with a competitive advantage.
For graph databases such as Resource Description Framework (RDF) and SPARQL query language. Unlike other proprietary NoSQL specifications, these standards are solid and have as much industry support as the basic specifications that make WWW work: HTTP and HTML. GraphDB stores semantic facts in the form of subject – predicate – object using the Resource Description Framework. RDF is a standard model for data publishing and interchange on the Web. RDF has features that facilitate data merging even if the underlying schemas differ. RDFS and OWL are its schema languages; SPARQL is the query language, similar to SQL. This ecosystem of standards is defined by the W3C consortium within community processes that involve all major data management vendors (IBM, ORACLE, HP, Microsoft, to name just few.
Probably the highest profile application of semantic technology to date, BBC’s 2010 World Cup web site is delivered using the OWLIM Enterprise semantic repository. A famous “call to action” by John O’Donovan (Chief Technical Architect, BBC) has spurred a flurry of semantic technology activity in world-known media companies, particularly in the UK and US.
KT Corporation (formerly Korea Telecom), is the leading South Korean integrated telecommunication service provider. KT has an information and communications business, and has the largest portion of the South Korean local telephone and high-speed Internet business. Megapass is a broadband telecommunication service offered by KT, featuring Interactive TV, TV-VOD (Video on Demand) and including movies and popular television programs, remote domestic video monitoring, SMS messaging, and local service ‘directory’ information.
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