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What Might The AI-Powered Corporate Library Of The Future Look Like? Posted on : May 15 - 2019

What might the corporate library of the future look like? As the Web becomes increasingly personalized and intelligent, with algorithms that understand our interests and deep learning systems that can make sense of everything from text to movies, how might all of these tools come together to reimagine the corporate reference library of tomorrow?

Half a century after the debut of the modern keyword search engine, we still rely on carefully selected textual keywords and phrases to search our vast archives of human knowledge. Boolean queries and complex search operators can all help, but at the end of the day the nearly unimaginable wealth of human information is still accessed through the lowly keyword.

The rise of deep learning algorithms may finally help us move beyond the limitations of trying to guess the exact wording an author of a document may have used.

Today even the most powerful enterprise search engines are frequently stymied by synonyms and different ways of saying the same thing. In contrast, library patrons are ever more accustomed to “intelligent” search engines that can search across minor and even major linguistic differences.

Search on Google for a specific phrase and documents that mention related terms are likely to show up even without containing the keyword itself. To the end user, it doesn’t matter that this may merely be because some other page on the Web used the keyword in linking to the page, rather than the result of some intelligent algorithm. All that matters is that they ran a search and relevant content was returned.

Moreover, since most searches return more than a single result, the ranking of the returned material is absolutely critical. Users have become accustomed to Web search engines that can bring to bear hundreds upon hundreds of metrics and the structure of the entire Web in real-time in deciding what to surface on the first page of results. In contrast, enterprise search engines often rely primarily on half-century-old simple textual relevance.

Web search engines increasingly personalize our results, using our past history of what kinds of search results we click on to learn the kinds of pages and sources we prefer. Thus, ten people all running the same search at the same moment might get ten very different first pages of results. In contrast, few enterprise search systems support much in the way of individual-level personalization across an entire company.

Global companies must work across languages, yet when it comes to their library, typically only a small number of languages are supported. An engineering patron of an American company's library looking for past designs of a particular component might be assisted by library personnel searching in English and perhaps a few European languages. A Chinese technical report that is a perfect match would likely be entirely missed, if the library subscribed to it at all. View More