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Changing the Rules of Search Through Ontology

When it comes to definition and the description of Ontology we have referenced content from Wikipedia (Ontology).

Ontology applies the power, simplicity and speed of semantic search to gain insight into all enterprise application data replacing traditional data integration. This means the ability to search and link applications, databases, files, spreadsheets etc. anywhere, without the cost and risk of integration.

In the same way that Google made it possible to find any "string" in the Internet via text search, Ontology makes it possible to find any "thing" across enterprise data and applications via just-enough semantic modeling and graph-search. This means the ability to search and link core applications, databases, big data sources, files, spread sheets, documents, emails etc. anywhere, without the cost and risk of integration.

ONTOLOGIES ARE BECOMING INCREASINGLY ESSENTIAL FOR NEARLY ALL DATA RICH APPLICATIONS

Ontology systems are revolutionizing how companies use their applications and data. The Internet and the Internet of Things continue to create a strong demand for sharing the semantics of data. Ontologies are becoming increasingly essential for nearly all data-rich applications. Companies are looking toward them as vital machine-processable semantic resources for many application areas. The reason they are important to understand and grasp is they can all but eliminate the difficult task of integrating all of your systems. By sharing an ontology, autonomous and distributed applications can meaningfully communicate to exchange data and thus make transactions interoperate independently of their internal technologies.

However, some confusion on how to reuse these techniques is evident. For example, many have confused ontologies with data schemes, knowledge bases or even logic programs. Unlike a conceptual data schema or a "classical" knowledge base that captures semantics for a given enterprise application, the main and fundamental advantage of an ontology is that it captures domain knowledge highly independently of any particular application or task.

Eliminating the need to Integrate

insight over 80per failWe have discussed how traditional data integration is difficult. To reemphasize that point, let's reflect. Corporations worldwide spend vast sums of money and time trying to get usable knowledge by integrating the often-conflicting, data spread across their many applications.

Unfortunately, dataintegration has been necessary whenever initiatives like customer care dashboards, compliance or financial reporting, IT consolidation, data migration and business intelligence are contemplated. The high cost, difficulty and risk of data integration are uncontroversial, as numerous research firms have noted that over 80% of data migrations fail.

Before Internet search engines, such as Google and Bing, you needed to be told the address of each website. The ability to search the unstructured web for strings and phrases ("string") revolutionized the Internet. Nobody integrated the Internet.

The Impact of Search

Before enterprise search engines and wikis, one needed to know the name and full location (the server, folder, filename) of the document being searched. This took time. Enterprise-wide stringsearch of unstructured documents and emails revolutionized IT document management by making it easy for anybody, even customers, to get results. No one needs integration to find enterprise documents.

So — what about all of the structured data that exists across enterprise applications? You simply cannot search applications using strings. Applications are not documents; they contain data that represents "things."

So until now, one needed data integration to figure out, for example, which ten applications reference the same customer, and which five systems define the services and infrastructure that they depend upon. This meant never-ending master data management projects, CRM consolidations and Customer 360 integrations. All expensive, and often delivered late.

Ontology solves this problem through one simple insight: As Google has indexed every website on the Internet via "strings" to deliver the simplicity of search, so can we index every enterprise application via semantics or "things" to deliver far simpler integration.

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