Search and Recommendation
Get relevant results in less time with sophisticated search and recommendations.
Graphwise Search and Recommendation Components comprise a range of search capabilities built on Graph AI. These applications are highly configurable, allowing the user to display information according to their specific needs. With Graphwise Search and Recommendation, users can benefit from knowledge retrieval that understands the user’s natural language, where faceted search helps to narrow down results, and recommendations are based on intent and context.
Go beyond simple search.
Graph Search is built on on enriched metadata to return results based on the diverse range of attributes related to a document. It fetches results that match the meaning of a user query instead of focusing exclusively on the exact words and phrases – behavior that is typically found in less sophisticated engines.
Experience advanced semantic search in a user-friendly graphical interface
Combine the power of graph databases and SPARQL engines
Use the “Concept Display” to see how the tagged concepts relate to each other and let you jump to other topics relevant to your search
Transform and harmonize documents/data into RDF using our Graph Automation so that it can be ready for the Graph Search application
Filter and narrow down search queries with facets that are based on a taxonomy
Automatically index linked RSS feeds with Solr or Elasticsearch
Get sophisticated content recommendations from a tailor-made recommender system.
Graphwise recommender systems are driven by domain, context, and intent, which means that they are highly customizable to an organization’s specific use case. With a Graphwise Recommender, content that is not explicitly related (but still relevant) to the search query can be surfaced; a user can find exactly what they are looking for PLUS benefit from additional helpful information.
Use semantic footprinting and query expansion to get recommendations that are not only based on similarity but implicit connections
Benefit from traceable recommendations to make sound decisions
Incorporate linked data such as Wikidata and/or DBpedia to easily broaden the knowledge model
Factor in multilingualism with bundled concepts
Easily set up your configuration in the Workbench.
The Graphwise Workbench makes it easy to configure your search or recommendation space in the backend. A system administrator or user with editing permissions can turn features on/off with a simple click and decide which factors they want the applications to draw from. These configurations can easily be pushed to a Graphwise ADF (Application Development Framework) frontend.
Choose the Taxonomy and the graph repositories you want to run the search space on – you can draw from multiple projects at once
Determine which facets you want to display in the search frontend and how deep in the hierarchy you want the facets to go
Enter the minimum score a concept has to reach to be extracted. The score ranges from 1 to 100 where a higher score means that the concept is more relevant for the processed text
Resolve language ambiguities by drawing upon the connected thesaurus and local context – what would normally take sophisticated coding work, only takes seconds by toggling a lever
Use a corpus and shadow concepts to broaden the scope of the recommendations
Set the languages for multilingual search and recommendations
While it is typically a data scientist or machine learning expert who handles the configuration of such systems, the user-friendly interface of Graphwise Workbench can even be used by a subject matter expert, who is typically a non-technical user.
Take a deep dive with an expert.
Please fill out the form to get connected with a Graphwise Team Member. Get ready for your guided walkthrough and full-access account!