Articles from this series
- Information science - Introduction
- Information science – Uniqueness and essential questions
- Information science – Philosophical approaches
- Information science – Paradigms
- Information science – Epistemologies
- Information science – What is information?
- Information science - Terminology (Knowledge, Document)
Main source
Introduction to Information Science - DAVID BAWDEN and LYN ROBINSON
Terminology
Those will be the final terms. In previous articles I’ve described information, knowledge and document.
Collections/Databases
Collections are organized places, where physical documents are being stored. Usually they are focused on a specific designated “field of knowledge”. Collections are either storing documents that represent some kind of ideas (libraries), or documents that are supposed to stimulate those that perceive the documents and transmit information to them through that (museums, archives). For long centuries, those were the only “temples of knowledge”. In digital age though, everything changed.
Database is a digital representation of one, or several collections. As I explained in previous article, every document has its identifying number that is being used to distinguish between different documents in those databases. Because of the databases, the reachability of all documents suddenly skyrocketed. Moreover the borders between the databases are ever weakening. The same documents are now being stored in several databases and the conception of where one database starts and where one ends is ever dwindling over time. This wasn’t possible prior to digitalization of the documents. Digital databases are also very “easy” to build (it can be finished rather quickly, compared to the collections to say the least) and the physical document is backed up in the there.
Relevance
It could seem that relevance doesn’t even need a definition. Everyone knows, more, or less, what it means. It basically is information that in any imaginable way contributes to the receiver. When we though want to be effective while searching for information, the matters of the term are not as easy as they seem. Since “effective searching for information” can be perceived as global problem of the mankind, it’s one of the most vital terms in Information science. Unfortunately information systems are not perfect and they tend to throw out a lot of completely irrelevant texts, even when our knowledge about “searching commands” is almost perfect. It can’t be perfect without the complete understanding of what is relevant anyway. And I will immediately explain why that is not possible.
Objective (or systemic) relevance is one thing. Imagine that we want to search for documents about “migraine cures”. Every document that contains term “migraine” and “cure” will be found and objectively (systemically), they are relevant. They DO speak about migraine and its cures. They possess some information connected to the topic.
But then subjective relevance kicks in. What if the system found a document that is a biography about a guy who spent his whole life searching for migraine cure. That is not really relevant. Also what if you have already chewed trough couple of relevant documents. The information and knowledge you have gathered has changed your overall understanding of the topic. What would be relevant few documents ago is suddenly not relevant now, because you already know the information. That is why it’s impossible to find a “truth” about relevance. Not prior to understanding how our brains work and somehow be able to find the humans "goals" and way how to transfer that information into the search engine.
