Get fewer, more relevant results with semantic search for the enterprise

Stop wasting time sifting through irrelevant results. Get the right answers, faster, no matter the structure or location of the underlying data.

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Arming employees with the right information lets experts do their job better by reducing blind spots and preventing duplicate work. For products with built-in search, providing the right information to customers increases user satisfaction and engagement.

The key problem with traditional search is that relevant results will be left out. If you tune the search engine to reduce the likelihood that results will be left out, you’ll wind up with a lot of results, which you then have to sift through. In contrast, Stardog works by searching based on the meaning of the query, which provides more intuitive results.

Stop searching, start solving

With traditional search solutions, balancing precision and recall means either missing out on results or getting many irrelevant results. Consequently, time and energy is wasted reviewing irrelevant answers or testing different search terms in hopes of getting better results. Stardog’s semantic search is different – it scans the Knowledge Graph to uncover all layers of connections across the search terms, ensuring that no results are left out just because of syntax.

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IDB's Findability Project makes research more available, increasing global profile of the bank

Search across all relevant data sources

Employees and users alike require up-to-date information they can trust; but search applications often work with old data. Stardog’s virtualization capability unlocks access to data and ensures that search is current and comprehensive. Use Stardog’s database Connectors to search across SQL, NoSQL and full-text documents – so no critical results are left out.

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SpringerMaterials helps materials scientists conduct more effective research

More intelligent results

Humans think associatively but normal search applications index information hierarchically or rank results based on term frequency. In contrast, Stardog’s Knowledge Graph-powered solution captures the real-world context of data so it can determine all the various links between concepts. The Knowledge Graph understand concepts, entities and the relationships between them. The result: user intent is more easily understood, providing more intelligent results. For AIs like chatbots or recommendation engines, smart search is the ideal solution.

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NASA aggregates and links existing employee data to find expertise within their organization

  • Quote

    They have the ability to follow the chain of custody and talk about the "why" of the data, which is really important

    Conrad Leonard, Senior Bioinformatician, QIMR Berghofer
  • See Semantic Search in action!

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Stardog easily incorporates new data sources, allowing for easy iterative development of smart search enabled applications


Where some AIs cannot provide explanations for results, Stardog offers proofs for all query results for easy interpretation


BITES translates full-text documents into searchable data, capturing concepts and their relationships

Built-in ML

Use built in predictive analytics and similarity search to develop models to improve recommendations in search results