Introduction

In the context of an internal enterprise search engine, relevancy refers to the degree to which the search results match the user’s needs and expectations. A search engine is relevant if it returns results that are accurate, useful, and appropriate for the user’s query. Relevancy is an important factor in the effectiveness of a search system, as users are more likely to find what they are looking for and be satisfied with their search experience if the results are relevant. Relevancy can be influenced by several factors, including the quality of the search index, the ranking algorithms used by the search engine, and the design of the search interface. Improving relevancy can help to increase the usefulness and efficiency of the search system, and ultimately improve the productivity of users.

Strategies to Improve Search Relevancy

There are several strategies that you can use to improve the relevancy of your internal enterprise search engine

Use synonyms and related terms: Make sure your search engine can recognize synonyms and related terms for the query. This will help return more relevant results. Let’s say a user searches for the term “computer.” You could have your search engine return results that include synonyms such as “PC” or “desktop,” as well as related terms like “laptop” or “hardware.”

Implement keyword stemming: Keyword stemming involves returning results that match the root form of a word. If a user searches for the term “running,” your search engine could return results that include the root form of the word, “run,” such as “I go for a run every morning” or “She is running a marathon this weekend.”

Use metadata: By adding metadata to your documents, such as titles and descriptions, you can give your search engine more context about the content of the documents. This can help improve the relevancy of the search results. For example, if you have a document about “best practices for training dogs,” you could include the title “Training Your Dog: Tips and Tricks” and a description that says, “Learn how to effectively train your dog with these proven techniques.” This can help the search engine understand the content of the document and return it as a relevant result when a user searches for “dog training.”

Implement filters: Allow users to filter search results by specific criteria, such as document type or date, to help narrow down the results and make them more relevant. You could allow users to filter search results by document type (such as PDF or Word documents) or by date (such as documents created in the last month or year). This can help narrow down the search results and make them more relevant to the user’s needs.

Utilize user feedback: Collect feedback from users about the relevancy of the search results they are receiving. This can help you identify any issues with your search algorithm and make necessary improvements. You could set up a system for users to rate the relevancy of the search results they receive. If many users rate a result as not relevant, you can use that feedback to improve your search algorithm and make it more effective. You could also allow users to provide feedback about why they think a result is not relevant, which can help you understand any issues with your search algorithm.

Entity Extraction: Entity extraction, which involves identifying and extracting important entities or concepts from a document, can potentially improve the relevancy of your search engine. Here is how it can help,

• Improved query understanding: By extracting entities from a document, you can give your search engine a better understanding of the content and context of the document
• Enhanced search result organization: You can use the extracted entities to organize search results in a more meaningful way. For example, if you extract the entities “dog” and “training” from a document, you could group that document with other documents that also mention those entities. This can help users find related content more easily.
• More accurate search result ranking: You can also use the extracted entities to rank search results based on their relevance to the query. For example, a document that mentions the query entity multiple times could be ranked higher than a document that only mentions the entity once.

Overall, entity extraction can be a useful tool for improving the relevancy of your search engine, as it can help your search algorithm better understand the content of your documents and more accurately rank and organize search results.

Conclusion

In summary, there are several key steps that can be taken to improve relevancy in an internal enterprise search engine. But the most important of all is to Understand your users and their search needs. It is important to identify the different types of users and their goals when searching and gather user feedback and analyze search logs to inform search design. By following the steps mentioned in the article, organizations can take a proactive and data-driven approach to improving the relevancy of their internal enterprise search engines, ultimately leading to a more effective and useful search experience for their users.