Posts Tagged ‘Findability’
AI Still Needs a Map
Artificial intelligence (AI) has changed how we search for information. We can ask questions conversationally, explore unfamiliar topics and receive answers without knowing the exact keywords. But that convenience does not eliminate the need to organize information. It makes a strong foundation even more valuable. This important topic came to us from LinkedIn inn their…
Read MoreFindability in the Age of AI Starts with a Strong Taxonomy
Artificial intelligence (AI) can search vast collections of information in seconds, interpret natural language and surface connections people might otherwise miss. But even the smartest AI tools can struggle when an organization’s information is inconsistent, poorly described or difficult to access. This interesting topic came from Hedden Information Management in their article, “Generative AI and…
Read MoreAI Didn’t Replace Taxonomies – It Made Them More Useful
Every few years, a new technology arrives and someone declares an older discipline dead. With artificial intelligence (AI), taxonomies have become a frequent target. Why spend time organizing information into agreed-upon categories when a model can read a document, identify its subject and answer a question in seconds? It is a reasonable question. AI can…
Read MoreWhen Information Is Everywhere, What Do Employees Remember?
A workplace used to reward the person who knew where everything was. Now, a quick search can surface a policy, project history or technical answer in seconds. That access is valuable, but it may also change what employees commit to memory. This interesting subject came to us from Harvard Business Review in their article, “The…
Read MoreSemantic Search Is Only as Smart as Its Governance
Semantic search promised more than traditional keyword search: an understanding of meaning, context and intent. Instead of matching exact words, it can connect a question with relevant information even when the language differs. But that intelligence depends on more than a fancy new model. It depends on well-governed data. MarTech brought this topic to our…
Read MoreData Quality Is Still the Foundation of Information Science
For information science professionals in higher education, data quality is hardly a new concern. Long before generative artificial intelligence (GenAI), machine learning and advanced analytics entered the conversation, the principle was simple: information systems can only be as trustworthy as the data they contain. This important topic came to us from IT Brief and their…
Read MoreSemantic Search and the Preservation of Scholarly Intent
For decades, academic publishing has focused on creating authoritative, peer-reviewed content designed to advance knowledge. Yet, as discovery increasingly depends on artificial intelligence (AI) and semantic search systems, a new challenge has emerged: ensuring that scholarly intent survives machine interpretation. Semantic search differs from traditional keyword search by attempting to understand meaning rather than simply…
Read MoreOntology: The Invisible Architecture Powering Findability in the Age of AI
In information science, ontology is one of those terms that sounds intimidating but quietly does some of the most important work behind the scenes. At its core, an ontology is a structured framework that defines the relationships between concepts within a domain. It goes beyond simple lists of terms or categories by mapping how ideas…
Read MoreRecords Retention in the Age of AI: Why Taxonomy Still Reigns Supreme
In a world increasingly shaped by artificial intelligence (AI), records retention has moved from back-office obligation to strategic necessity. AI systems thrive on data, but not just any data—relevant, accurate, well-structured data. Without intentional retention policies, organizations risk feeding their AI outdated, redundant or even harmful information, leading to flawed outputs and questionable decisions. This…
Read MoreEvolution in Search
Early search engines relied almost entirely on keywords, treating every query as a string of terms to be matched against web pages. Results were based on how often those words appeared and where they were placed. This approach worked for a time, but it assumed that users always knew the exact words to use and…
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