All enterprises deal with information on a daily basis, and someone needs to filter and sort this information in order to decide what is relevant to a given task and what is not. This course aims to help the student in developing such skills by introducing the theory behind situational relevance. Information always has a sender with a certain intention of informing, and the person in need of the information likewise has an intention of receiving it. Thus, delegating the right information to the right person means understanding the intentions of both parties and bridging the gap by asking the information seeker questions like a librarian trying to find the right book to cover the reader’s needs. Today, the job can in principle be done automatically by an advanced information tool - an “electronic librarian”.

Language plays an increasingly crucial role in technology, yet computers have never been very good at understanding human language; human communication is simply too different from computer logics. The internet provides vast amounts of information for anyone able to interpret it, but extracting this knowledge programmatically is extremely difficult if it is written in a human language. Even so, computers are more and more often expected to be able to interpret natural language, not least because we want to communicate our needs through everyday language. Using a search engine is such a kind of linguistic communication, even if it traditionally involves very little linguistic substance, but only a few words. How do people express their needs, and how can we make them provide the extra information necessary in order to truly understand what information would solve their problems?

Traditional search engines see the search string as nothing more than a string of characters, but later years have seen an encouraging development towards semantic search engines that are able to interpret the string as words with meanings. The next step is for the system to be able to “imagine” what the user really meant, and what prompted that particular formulation of the query. Language is often both vague and ambiguous, and we consequently need to take the context into account - firstly, that of the search situation, and secondly, that of the situation which caused the user to approach the search engine in the first place. We accordingly need to teach computers to construct a cognitive interpretation of the linguistic input in order to arrive at the kind of personalized information tool that will make information retrieval truly effective.

Context is a very complex concept, potentially containing vast amounts of circumstantial information. How then do we know what is relevant? The central concept in this question is relevance. The concept of relevance is central to any form of communication, yet it is an incredibly slippery notion, which researchers have found it very hard to define. Everything we say is open to interpretation, yet our listeners are able to ignore all irrelevant construals, and to detect the slight traces of relevance even in cases where we seem to be talking of something completely unrelated.

This course gives a fundamental introduction to the concepts of relevance and context with the purpose of looking at how a theory of relevance can be utilized in information retrieval technology and examining whether an information tool can be enabled to decide whether a given resource will be relevant to a user at a certain time. The theories are equally useful in connection with human communication, however. The course will set out by introducing the theories on relevance in human discourse, which was the original application. It will then move on to investigating subjectivity in connection with information needs. Finally, we shall be looking at some attempts at producing personalized information tools from a theoretical point of view.

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