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HomeArtificial Intelligence and Machine LearningReimagine knowledge discovery using Amazon Kendra’s Web Crawler

Reimagine knowledge discovery using Amazon Kendra’s Web Crawler

When you deploy intelligent search in your organization, two important factors to consider are access to the latest and most comprehensive information, and a contextual discovery mechanism. Many companies are still struggling to make their internal documents searchable in a way that allows employees to get relevant information knowledge in a scalable, cost-effective manner. A 2018 International Data Corporation (IDC) study found that data professionals are losing 50% of their time every week—30% searching for, governing, and preparing data, plus 20% duplicating work. Amazon Kendra is purpose-built for addressing these challenges. Amazon Kendra is an intelligent search service that uses deep learning and reading comprehension to deliver more accurate search results.

The intelligent search capabilities of Amazon Kendra improve the search and discovery experience, but enterprises are still faced with the challenge of connecting troves of unstructured data and making that data accessible to search. Content is often unstructured and scattered across intranets and Wikis, making critical information hard to find and costing employees time and effort to track down the right answer.

Enterprises spend a lot of time and effort building complex extract, transform, and load (ETL) jobs that aggregate data sources. Amazon Kendra connectors allow you to quickly aggregate content as part of a single unified searchable index, without needing to copy or move data from an existing location to a new one. This reduces the time and effort typically associated with creating a new search solution.

With the recently launched Amazon Kendra web crawler, it’s now easier than ever to discover information stored within the vast amount of content spread across different websites and internal web portals. You can use the Amazon Kendra web crawler to quickly ingest and search content from your websites.

Sample use case

A common need is to reduce the complexity of searching across multiple data sources present in an organization. Most organizations have multiple departments, each having their own knowledge management and search systems. For example, the HR department may maintain a WordPress-based blog containing news and employee benefits-related articles, a Confluence site could contain internal knowledge bases maintained by engineering, sales may have sales plays stored on a custom content management system (CMS), and corporate office information could be stored in a Microsoft SharePoint Online site.

You can index all these types of webpages for search by using the Amazon web crawler. Specific connectors are also available to index documents directly from individual content data sources.

In this post, you learn how to ingest documents from a WordPress site using its sitemap with the Amazon Kendra web crawler.

Ingest documents with Amazon Kendra web crawler

For this post, we set up a WordPress site with information about AWS AI language services. In order to be able to search the contents of my website, we create a web crawler data source.

On the Amazon Kendra console, choose Data sources in the navigation pane.

Under WebCrawler, choose Add connector.

For Data source name, enter a name for the data source.
Add an optional description.

Choose Next.

The web crawler allows you to define a series of source URLs or source sitemaps. WordPress generates a sitemap, which I use for this post.

For Source, select Source sitemaps.
For Source sitemaps, enter the sitemap URL.

Add a web proxy or authentication if your host requires that.
Create a new AWS Identity and Access Management (IAM) role.
Choose Next.

For this post, I set up the web crawler to crawl one page per second, so I modify the Maximum throttling value to 60.

The maximum value that’s allowed is 300.

For this post, I remove a blog entry that contains 2021/06/28/this-post-is-to-be-skipped/ in the URL, and also all the contents that have the term /feed/ in the URL. Keep in mind that the excluded content won’t be ingested into your Amazon Kendra index, so your users won’t be able to search across these documents.

In the Additional configuration section, add these patterns on the Exclude patterns

For Sync run schedule, choose Run on demand.
Choose Next.

Review the settings and choose Create.
When the data source creation process is complete, choose Sync now.

When the sync job is complete, I can search on my website.

Conclusion

In this post, you saw how to set up the Amazon Kendra web crawler and how easy is to ingest your websites into your Amazon Kendra index. If you’re just getting started with Amazon Kendra, you can build an index, ingest your website, and take advantage of intelligent search to provide better results to your users. To learn more about Amazon Kendra, refer to the Amazon Kendra Essentials workshop and deep dive into the Amazon Kendra blog.

About the Authors

Tapodipta Ghosh is a Senior Architect. He leads the Content And Knowledge Engineering Machine Learning team that focuses on building models related to AWS Technical Content. He also helps our customers with AI/ML strategy and implementation using our AI Language services like Amazon Kendra.

 

 

Vijai Gandikota is a Senior Product Manager at Amazon Web Services for Amazon Kendra.

 

 

 

 

Juan Bustos is an AI Services Specialist Solutions Architect at Amazon Web Services, based in Dallas, TX. Outside of work, he loves spending time writing and playing music as well as trying random restaurants with his family.

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