“conceptClassifier for SharePoint was selected due to its transparent integration with SharePoint and the ability to automatically generate and classify conceptual metadata and easily develop and manage organizational taxonomies through Concept Searching’s conceptTaxonomyManager.”
- Provide relevant content
- Enable existing content to be reused and repurposed
- Normalize complex terms and suggest appropriate terminology
Associated with a major university, the medical center employs over 18,000 and is ranked as one of the top medical facilities in the US.
In addition to providing outstanding primary and specialty medical care the medical center intranet also specializes in pioneering scientific research as well as undergraduate, professional, and post-graduate education.
The medical center intranet site provided a free text search to allow users to find information. As with most free text search solutions, the end user would get too much irrelevant information with little ability to filter out unwanted results leading to a longer search process and a less than satisfactory user visit.
End users had many diverse objectives when searching for information, depending on their role at the medical center. For example, the information that a researcher may be looking for will differ from the data an administrator or nurse or doctor is looking up, yet the current environment had no way to assist the user in finding exactly what they needed.
End users needed to identify content in the context of what they were seeking. The fundamental problem with most search solutions is that they are based on an index of single words.
Yet most searches are expressed in short patterns of words and not single words in isolation which are highly ambiguous.
Keyword search was insufficient and end users did not always know exactly what they were looking for and were not adept at performing complex keyword searches.
The system didn’t acknowledge ambiguity in words – one word can have many meanings, two or more words can share the same meaning.
The requirements of the solution included:
- Provide easier navigation to end users
- Enable end users to retrieve relevant information based on their search query
- Aggregation of the multiple sources of information
- Enhance the search interface with ‘findability’ features to make it easier
A key driver for the solution was the underlying compound term technology of conceptClassifier for SharePoint.
Instead of identifying single keywords, compound term processing identifies multi-word terms that form a complex entity (concepts). By forming these compound terms and placing them in the search engine’s index the search can be performed with a higher degree of accuracy because the ambiguity inherent in single words is no longer a problem.
As a result, a search for “triple heart bypass” will locate documents about this topic even if this precise phrase is not contained in any document. A concept search using compound term processing can extract the key concepts, in this case “triple heart bypass” and use these concepts to select the most relevant documents.
Additional findability features included in the solution are the ability to automatically suggest the appropriate terms for end users, eliminating the need for them to type in complex medical terms and the associated syntax.
The result is the ability to deliver relevant and precise information to a variety of users based on their individual needs, improving the search experience and increasing productivity.
conceptClassifier for SharePoint provided the ability to automatically generate conceptual metadata and provide the rich multi-term metadata to the search engine index, expediting the search process.
The solution delivered the ability to:
- Identify relevant content to support end users, regardless of their organizational role to find the information they were searching for.
- Improve productivity through advanced ‘findability’ in search
- Enable existing content to be discoverable for reuse and repurposing.
- Ability to normalize complex medical terms, simplifying the process for end users to find what they need and automatically suggest the appropriate terminology for end users.