What is your search missing?
The built-in search experience in SharePoint 2013 has greatly improved from previous versions, and companies adopting it enjoy a bag of new features (such as the visual refiners, the social search, the hover-panel with previews, to name a few). However, is your implementation of the search in SharePoint 2013 matching all your business and information needs?! Is your search solution reaching the target search KPIs? Are you wondering how you can cut down on the task of the editor, improve the search experience for your users, or reduce the time spent by your information workers finding the relevant content?
Entity Extraction in SharePoint 2013 Search
To make your search good you need good metadata. They can be then used as a filters, boosted fields etc. Usually that means that documents need to be tagged, which may take a long time if done manually by content owners.
However, it is possible to extract some metadata from document content during index time. In SharePoint 2013 there are two ways of doing it: “Custom entity extraction” or with use of “Custom Content processing”. In this post you can learn the first way.
Custom entity extraction
SharePoint 2013 introduced a new way for entity extraction. It allows to extract entities from document based on dictionary.
The first step is, of course, preparing the dictionary. It needs to be in following format:
Then you need to register that dictionary file in SharePoint – using Powershell scripts: https://technet.microsoft.com/library/jj219614.aspx
Last thing left to do is enabling entity extraction on the Managed Property it should be applied to. To get them from content of a document just edit “body” Managed Property
and select “Word Extraction – Custom 1” checkbox:
We choose that one because our dictionary was registered with – DictionaryName “Microsoft.UserDictionaries.EntityExtraction.Custom.Word.1” parameter, if you need more dictionaries then you register them using different dictionary name values and selecting the right option in Managed Property settings.
After that run “Full Crawl” for your sources.
This way is really good if you are able to generate a static dictionary. Eg. you can use a list of all countries or cities you can find on the internet for location extraction. You can also extract at dictionary from your customers database or your employees list and then update it on regular basis.
However usually it’s not possible to get a full list of all entities, and they must be extracted using one of NLP algorithms, that will be described in next part.