dat in Murtaugh 2016
)
[Michael Murtaugh](/wiki/index.php?title=Michael_Murtaugh "Michael Murtaugh")
In text indexing and other machine reading applications the term "bag of
words" is frequently used to underscore how processing algorithms often
represent text using a data structure (word histograms or weighted vectors)
where the original order of the words in sentence form is stripped away. While
"bag of words" might well serve as a cautionary reminder to programmers of the
essential violence perpetrated to a text an
way. The resulting representation is then
a collection of each unique word used in the text, typically weighted by the
number of times the word occurs.
Bag of words, also known as word histograms or weighted term vectors, are a
standard part of the data engineer's toolkit. But why such a drastic
transformation? The utility of "bag of words" is in how it makes text amenable
to code, first in that it's very straightforward to implement the translation
from a text document to a bag of words representa
cation for reasons of safety, commercial
telegraphy extended this network of communication to include those parties
coordinating the "raw materials" being mined, grown, or otherwise extracted
from overseas sources and shipped back for sale.
## "Raw data now!"
From [La ville intelligente - Ville de la connaissance](/wiki/index.php?title
=La_ville_intelligente_-_Ville_de_la_connaissance "La ville intelligente -
Ville de la connaissance"):
Étant donné que les nouvelles formes modernistes et l'util
ndex.php?title
=The_Smart_City_-_City_of_Knowledge "The Smart City - City of Knowledge"):
As new modernist forms and use of materials propagated the abundance of
decorative elements, Otlet believed in the possibility of language as a model
of '[raw data](/wiki/index.php?title=Bag_of_words "Bag of words")', reducing
it to essential information and unambiguous facts, while removing all
inefficient assets of ambiguity or subjectivity.
> Tim Berners-Lee: [...] Make a beautiful website, but first give us the
unadulterated data, we want the data. We want unadulterated data. OK, we have
to ask for raw data now. And I'm going to ask you to practice that, OK? Can
you say "raw"?
>
> Audience: Raw.
>
> Tim Berners-Lee: Can you say "data"?
>
> Audience: Data.
>
> TBL: Can you say "now"?
>
> Audience: Now!
>
> TBL: Alright, "raw data now"!
>
> [...]
>
> So, we're at the stage now where we have to do this -- the people who think
it's a great idea. And all the people -- and I think there's a lot of people
at TED who do things because -- even though there's not an immediate return on
the investment because it will only really pay off when everybody else has
done it -- they'll do it because they're the sort of person who just does
things which would be good if everybody else did them. OK, so it's called
linked data. I want you to make it. I want you to demand it. [6]
## Un/Structured
As graduate students at Stanford, Sergey Brin and Lawrence (Larry) Page had an
early interest in producing "structured data" from the "unstructured" web. [7]
> The World Wide Web provides a vast source of information of almost all
types, ranging from DNA databases to resumes to lists of favorite restaurants.
However, this information is often scattered among many web servers and hosts,
using many different formats. If these chunks of information could be
extracted from the World Wide Web and integrated i
rectory of people, the largest and most diverse
databases of products, the greatest bibliography of academic works, and many
other useful resources. [...]
>
> **2.1 The Problem**
> Here we define our problem more formally:
> Let D be a large database of unstructured information such as the World
Wide Web [...] [8]
In a paper titled _Dynamic Data Mining_ Brin and Page situate their research
looking for _rules_ (statistical correlations) between words used in web
pages. The "baskets" they mention stem from the origins of "market basket"
techniques developed to find correlations between the it
ackle the scale of the web and still perform using
contemporary computing power completing its task in a reasonably short amount
of time.
> A traditional algorithm could not compute the large itemsets in the lifetime
of the universe. [...] Yet many data sets are difficult to mine because they
have many frequently occurring items, complex relationships between the items,
and a large number of items per basket. In this paper we experiment with word
usage in documents on the World Wide Web (see Section 4.2 for details about
this data set). This data set is fundamentally different from a supermarket
data set. Each document has roughly 150 distinct words on average, as compared
to roughly 10 items for cash register transactions. We restrict ourselves to a
subset of about 24 million documents from
hat's quite
symptomatic. It goes something like this: you (the programmer) have managed to
cobble out a lovely "content management system" (either from scratch, or using
any number of helpful frameworks) where your user can enter some "items" into
a database, for instance to store bookmarks. After this ordered items are
automatically presented in list form (say on a web page). The author: It's
great, except... could this bookmark come before that one? The problem stems
from the fact that the database ordering (a core functionality provided by any
database) somehow applies a sorting logic that's almost but not quite right. A
typical example is the sorting of names where details (where to place a name
that starts with a Norwegian "Ø" for inst
ge-specific, and
when a mixture of languages occurs, no single ordering is necessarily
"correct". The (often) exascerbated programmer might hastily add an additional
database field so that each item can also have an "order" (perhaps in the form
of a date or some other kind of (alpha)numerical "sorting" value) to be used
to correctly order the resulting list. Now the author has a means, awkward and
indirect but workable, to control the order of the presented data on the start
page. But one might well ask, why not just edit the resulting listing as a
document? Not possible! Contemporary content management systems are based on a
data flow from a "pure" source of a database, through controlling code and
templates to produce a document as a result. The document isn't the data, it's
the end result of an irreversible process. This problem, in this and many
variants, is widespread and reveals an essential backwardness that a
particular "computer scientist" mindset relating to what constitutes "data"
and in particular it's r
n, still followed by modern web browsers, the only difference
between the two visually is that UL items are preceded by a bullet symbol,
while OL items are numbered.
The idea of ordering runs deep in programming practice where essentially
different data structures are employed depending on whether order is to be
maintained. The indexes of a "hash" table, for instance (also known as an
associative array), are ordered in an unpredictable way governed by a
representation's particular implementation. This data structure, extremely
prevalent in contemporary programming practice sacrifices order to offer other
kinds of efficiency (fast text-based retrieval for instance).
## Data mining
In announcing Google's impending data center in Mons, Belgian prime minister
Di Rupo invoked the link between the history of the mining industry in the
region and the present and future interest in "data mining" as practiced by IT
companies such as Google.
Whether speaking of bales of
orithm, and in the process (voluntarily) blind themselves to the work
practices which have produced and maintain these "resources".
Berners-Lee, in chastising his audience of web publishers to not only publish
online, but to release "unadulterated" data belies a lack of imagination in
considering how language is itself structured and a blindness to the need for
more than additional technical standards to connect to existing publishing
practices.
Last Revision: 2*08*2016
1. ↑ Benjamin Franklin
Stanford webpage](http://infolab.stanford.edu/~sergey/) Display 200
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characters around the word.
8. ↑ Extracting Patterns and Relations from the World Wide Web, Sergey Brin, Proceedings of the WebDB Workshop at EDBT 1998,
9. ↑ Dynamic Data Mining: Exploring Large Rule Spaces by Sampling; Sergey Brin and Lawrence Page, 1998; p. 2
10. ↑ Hypertext Markup Language (HTML): "Internet Draft", Tim Berners-Lee and Daniel Connolly, June 1993,