In the 1950s and 1960s, information retrieval (IR) theorists drew a distinction between“document retrieval systems” and “fact retrieval systems.” The former, were intendedto retrieve, in response to a user’s query, all documents that might contain informationpertinent to answering that query, while the latter were to lead the user directly tospecific pieces of information – facts – embedded within the documents being searchedthat would answer his or her question. The idea of information analysis clearlyprovided the theoretical impetus for fact retrieval (aka question-answering) systems

the emergence of the idea that documents could bedecomposed not only into smaller bibliographical units (as, for example, aperiodical into articles or a book into chapters), but also into yet smallerinformation units (such as, for example, the concepts or facts discussed indiscrete passages within a text) and that, once identified, these informationunits could be reconfigured in new arrangements that would facilitate theirretrieval [1, p. 223; 2, pp. 221–222].

The ongoing “placing”of the notes is then another work process that takes time; but also an activity thatgoes beyond the sheer monotony of reading and, as it were, incidentally trains thememory.

Interesting. I suspect it depends on how you use it. Students with a high level of metacognitive capacity could use this to their advantage. Teaching (particularly the Whole-Part-Whole Reteaching technique) is a very useful technique for active recall (don't forget expanding gap spacing and interleaving); it forces you to use all aspects of your cognitive schemas to provide a clear and understandable explanation of what you know to have others understand it. When you struggle to explain it to others or they ask questions and you cannot answer it (or explain it in different ways) you have identified knowledge gaps.These recall techniques serve not only to strengthen the neural connections between concepts in the cognitive schemata (Hebbian plasticity; re-encoding benefits) but, perhaps more importantly, also to identify knowledge gaps making you know what to focus on when improving your knowledge mastery (maybe even what information to drill, depending on the information type).

Dr. Miho Ohsaki re-examined workshe and her group had previously published and confirmed that the results are indeed meaningless in the sensedescribed in this work (Ohsaki et al., 2002). She has subsequently been able to redefine the clustering subroutine inher work to allow more meaningful pattern discovery (Ohsaki et al., 2003)

Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

After reviewing more than 100 years of research on learning, authors of a new paper say combining two strategies -- spacing and retrieval practice -- is key to success.

Das gängige Paradigma des Information Retrievals setzt unter anderem voraus, dass der Informationssuchende tatsächlich genau weiss, was er sucht.

And the well-known jurist Jacques Cujas stated that ‘hee is a Learned Man non qui multa legit sed qui can fitly turne to Authors et use them according to his occasions. Non qui multa memoria teneat sed qui optima in libris optimis posset inve-nire’ (he is a learned man not the one who reads a number of books but the one who can fitly turn to authors and use them according to his occasion. [He is a learned man] not the one who keeps in mind a number of things but the one who can find the best passages in the best books).21

Johannes Sturm, for example, admitted that it was not important after all to remember; it was far more important to know how to retrieve what in the meantime had been forgotten.20

At the same time, use of the web is now ubiquitous,and ”Google” is a verb. With the advent of search en-gines, users have learned to find data by describing whatthey want (e.g., various characteristics of a photo) insteadof where it lives (i.e., the full pathname of the photo inthe filesystem). This can be seen in the popularity ofsearch as a modern desktop paradigm in such products asWindows Desktop Search (WDS) [26]; MacOS X Spot-light [21], which fully integrates search with the Mac-intosh journaled HFS+ file system [7]; and the variousdesktop search engines for Linux [4, 27]. Indeed, MacOS X in particular goes one step further and exports APIsto developers allowing applications to directly access themeta-data store and content index.

The real heart of the matter of selection, however, goes deeper than a lag in the adoption of mechanisms by libraries, or a lack of development of devices for their use. Our ineptitude in getting at the record is largely caused by the artificiality of systems of indexing. When data of any sort are placed in storage, they are filed alphabetically or numerically, and information is found (when it is) by tracing it down from subclass to subclass. It can be in only one place, unless duplicates are used; one has to have rules as to which path will locate it, and the rules are cumbersome. Having found one item, moreover, one has to emerge from the system and re-enter on a new path.

So much for the manipulation of ideas and their insertion into the record. Thus far we seem to be worse off than before—for we can enormously extend the record; yet even in its present bulk we can hardly consult it. This is a much larger matter than merely the extraction of data for the purposes of scientific research; it involves the entire process by which man profits by his inheritance of acquired knowledge. The prime action of use is selection, and here we are halting indeed. There may be millions of fine thoughts, and the account of the experience on which they are based, all encased within stone walls of acceptable architectural form; but if the scholar can get at only one a week by diligent search, his syntheses are not likely to keep up with the current scene.

A record if it is to be useful to science, must be continuously extended, it must be stored, and above all it must be consulted.

Retrieval practice boosts learning by pulling information out of students’ heads (by responding to a brief writing prompt, for example), rather than cramming information into their heads (by lecturing at students, for example). In the classroom, retrieval practice can take many forms, including a quick no-stakes quiz. When students are asked to retrieve new information, they don’t just show what they know, they solidify and expand it. Feedback boosts learning by revealing to students what they know and what they don’t know. At the same time, this increases students’ metacognition — their understanding about their own learning progress. Spaced practice boosts learning by spreading lessons and retrieval opportunities out over time so that new knowledge and skills are not crammed in all at once. By returning to content every so often, students’ knowledge has time to be consolidated and then refreshed. Interleaving — or practicing a mix of skills (such as doing addition, subtraction, multiplication, and division problems all in one sitting) — boosts learning by encouraging connections between and discrimination among closely related topics. Interleaving sometimes slows students’ initial learning of a concept, but it leads to greater retention and learning over time.

In 1996 and 1998, a pair of workshops at the University of Glasgow on information retrieval and human–computer interaction sought to address the overlap between these two fields. Marchionini notes the impact of the World Wide Web and the sudden increase in information literacy – changes that were only embryonic in the late 1990s.

Warner's view is related to what might be termed a hermeneutical approach to searching (cf. Boell & Cecez-Kecmanovic, 2010) as opposed to a positivist approach. The positivist view implies that searching can be done in a formal way (algorithmic) that retrieves relevant knowledge without bias in the search (and this is the assumption behind evidence-based practice). The hermeneutic approach is based on the assumption that there is a constant reinterpretation of the relevant literature, implying the need for great flexibility in search criteria and a great level of iteration in search processes—and, most important, an understanding of what is going on during the search.

A fourth level of KO in Boolean systems is generated by the searcher

A third level of KO in classical databases consists of the information retrieval thesaurus,19 ontologies, and other kinds of controlled vocabulary constructed by information specialists.

Another level of KO is the bibliographical record and its organization into fields (and the corresponding organization of data in linear and inverted files). Such records vary from database to database and from host to host.

The selection of material to the bibliography constitutes the first and most basic level of KO. Because the meaning of terms is implicitly understood in this disciplinary context and to the extent that classification and indexing is based on the principle of “literary warrant,” this selection influences the developments of thesauri and ontologies, which may thus be understood as a higher level of KO.

Given the Boolean model, the goal for KO can be understood as improving bibliographical records in ways which improve searchers' selection power.

In the Boolean model, a great range of strategies are available to increase “recall” and “precision” (sometimes termed “recall devices” and “precision devices”). To utilize such devices in optimal ways, the user has to know about the databases, search facilities, documents, genres, languages, paradigms, and so on, in which he or she is searching. This should be part of what is often termed information literacy.

The problem is not that best-match systems are being developed, but that an ideological tendency to make things “user friendly” (and the market bigger) tends to hurt the development of systems aimed at increasing the selection power of users and search experts.

But much of the popularity of contemporary search engines may also be attributed to the easy pickings afforded by the first generation of Internet full-text based systems (owing to the cheap cost of digital storage capacity after 1990): no doubt it is good to have all text on the web indexed and made searchable—and often with free access. However, when the easy pickings have been utilized, more complex strategies (and more humanistic approaches) may be needed to make further progress.

Again, though, if maximum recall is required, it is impossible in ranked retrieval to know what is omitted by new queries, whereas Boolean queries allow the user to control and modify the search until a satisfactory result has been achieved and they therefore also seem better suited to iterative searches.

For a researcher conducting human studies, writing a dissertation, finding information pertinent to patient care, or conducting an in-depth literature review, Google Scholar does not appear to be a replacement for PubMed, though it may serve effectively as an adjunct resource to complement databases with more fully developed searching features.

To understand the possibilities of Boolean search when used in its most advanced ways, it is necessary to know about bibliographical records in online databases,

As previously mentioned, the medical domain is an exception to the general trend that the study of the optimization of document searching strategies has suffered in information science.