A university library digitizes fifty years of scanned theses and research papers that have inconsistent file names and no catalog. Staff want researchers to type a topic into a search box and instantly retrieve relevant documents, with the system automatically pulling out metadata such as author names and publication years from the scanned text so results can be filtered. Which AI capability class fits this need?
Select an answer to reveal the explanation.
Short Explanation
Fifty years of scanned papers with no catalog is basically a library where every book got dumped on the floor in random order. What the staff want is a way to type in a topic and have the right documents surface immediately, plus have useful details like who wrote each one and when pulled out automatically so people can filter results. That's a very different job from sorting each paper into one of a fixed set of buckets, and it has nothing to do with predicting a number or spotting weird outliers in the data. This is about turning a pile of unorganized scanned text into something searchable and enriched with facts pulled straight from the content, so a researcher's search box actually works the way a modern search engine does, instead of staff manually flipping through boxes of paper.
Full Explanation
The correct answer is B. Knowledge mining combines content extraction, such as pulling text and metadata out of scanned or unstructured documents, with an indexed search experience, so users can type a query and retrieve relevant content along with structured facts like author or date pulled from the source material. That combination of full-text search plus automatic metadata extraction over a large, disorganized document collection is precisely what the library needs. Option A is incorrect because a custom classification model sorts each document into one of a predefined set of categories, which does not by itself build a searchable index or extract open-ended metadata like names and dates. Option C is incorrect because regression forecasting predicts a continuous numeric value, such as next year's checkout volume, and has no role in indexing documents or extracting text-based metadata. Option D is incorrect because anomaly detection flags data points that deviate from an expected pattern, which is unrelated to helping researchers search a document collection or surfacing metadata from scanned pages. The library's need to search across decades of unindexed scanned material and enrich it with extracted facts points specifically to a knowledge mining solution.