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Information quality has become the number one issue a knowledge worker faces in today’s environment, due to the high noise‐to‐signal ratio of non‐essential content in knowledge repositories and the lack of quality measurement for the information. This paper discusses information organization and retrieval technologies for improving the quality of knowledge by reducing the noise‐to‐signal ratio, and focusing on techniques to abstract data from search results into comprehensible knowledge for users. A case study concerning a customer management system is used to illustrate the ideas.
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Stored only when both papers are in the corpus — 28 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.