requirements aren’t directly expressed by MeSH but these terms manage the
indexing terms. The standard TREC format provides the topic statements and
relevant document files are described below which simulate human judgement and
contain information for 0 or 1 for every MeSH term expressed in the filtration
of any given topic.
relevance judgments (files: qrels.ohsu.*)
searchers replicate each query. Out of these four, two are physicians who are experienced
in searching and the other two are medical librarians. A completely different
set of physicians estimate the results for relevance. This judgement is
performed on a three point scale. The pointers are: definitely, possibly, or
not relevant. Consideration for relevance is done for all documents that are
checked to be either definitely relevant or possibly relevant.
relevance judgments (files: qrels.mesh.*)
The document is considered to be relevant to a
MeSH topic if its concept is included in the list of MeSH term fields.
C. WHOOSH: Python Library
created by Matt Chaput.
? Whoosh uses only pure python hence
runs anywhere python can, and so is fast. It runs without requiring a compiler.
? Whoosh uses the Okapi BM25F ranking
function by default, but can be easily modified.
? Fairly small indexes are created by
Whoosh as compared to numerous other search libraries.
? All indexed text in Whoosh must be
permits you index free structured text for quickly searching matching documents
with respect to either simple or complex search guidelines.
predefined field types are provided by whoosh:
is used for indexing the text and storing locations for the terms. These
positions or locations further allow phrase searching.
entire value of the field is indexed into a single unit using the ID field,
rather than breaking it up into separate terms.
is neither an indexed type nor a searchable one. This is useful for displaying
the information to the user in the search results.
indexed and searchable type, this is created for comma and space separated
is capable of storing int, long, or floating point numbers in a format that is
sortable and compact
of boolean values is done by this field and this type allows users to search
for results like: true, false, 1, 0, t, f, yes, no.
objects are stored in this field in a compact and extremely sortable format.
A Format object is
made to define the type of information is recorded by a field about each term.
It also describes how it has to be stored on the disk. For example, this is how
the postings are stored by the Existence format:
While on the
other hand, this is how the Positions format would do the same:
string is passed to the field’s format object for a field by the indexing code.
An analyser is called by the format object which breaks the string into tokens.
Further, encoding of the information is done about each of them.
index performs mapping of the terms to the documents in which they appear. Also,
sometimes it is useful to store a term vector that maps all the terms that arise
in the documents to the original document sources.
inverted index of a field is:For the image above, the respective
forward index is:
D. Creating An Index
opening an existing index in a directory,
Open the index directory
import whoosh index index
creating an index in a directory,
Create a new index
Import os and os path
if os path doesn’t exist, make index directory
and create an index with schema as parameter.
schema using which the index is created is stored with the index itself. Indexes
can be kept in the same directory using the index-name keyword.
use the functions for convinience
Create index with schema and index name usage as parameters.
Open this index
use Storage object
Call storage.create with schema and index name usage as parameters
Open the storage
The relevance of
the documents using Hidden Markov Model is compared with the tf.idf approach.
Tf.idf is an approach based on numerical statistic based vector model. It reflects
necessity of a word to a document in a collection of documents. Often, it is used
in IR and data mining as a weighting factor.
The tf-idf value
is proportional to the frequency of appearance of a word given in the document.
Although, it is offset by the frequency of the word in the collection. This
helps to relate to the fact that in general some words have more frequency of
appearance than others.
implementation, the first step is to design the schema and then indexing is
performed 5. Then tf.idf values are calculated using Whoosh Library in Python.
For HMM calculation the data observed is assumed to be the query Q, and an
unknown key is assumed to be a relevant document D that is desired. The mind of
the user is a noisy channel, who is having either some precise or rough notion of
the documents he requires. This channel transforms that expressed notion into
the query text Q. Hence, we compute the probability for each document D that it
was the relevant one in the user mind, provided that Q was the query which was
expressed or produced, i.e. P (D is RjQ). We further rank the documents with
respect to this measure 6. This can be incorporated in the form of graphs. These graphical structures represent information
about a domain that is uncertain. Particularly, nodes denote random variables
with the edges denoting the probabilistic dependencies transitioning between all
the random variables 8.
is the term represents that an observer cannot realise the transition of states
and the underlying sequences by which the output is generated. But he view the output
states only 9.
P (qjD) is the
output distribution of any document D. It is set to be the sample distribution
for the words that appear in that document. For any document Dk, we can
has the maximum probability of producing Dk by repeatedly sampling the state
“General English”. It is estimated by
here is taken for all documents present in the collection. Using the parameters
estimated above, the formula for P (QjDk is R) is stated as under:
1. Hidden Markov
model is a formal substructure used for creating probabilistic models for problems
of linear sequence ‘labelling’. Just by drawing an intuitive image, a
conceptual toolkit is provided. This is very useful for building complex models.
They are at the hub of a set of miscellaneous programs. These programs include
gene finding, multiple alignments of sequence, profile searches and identification
of regulatory site.
2. HMM is a complete
probabilistic model. The overall ‘scores’ generated for sequences and the
parameters calculated are all probabilities 6, 9. Hence, Bayesian probability
theory can be incorporated for the manipulation of these numbers in more
powerful ways. This includes optimization of parameters and interpretation of
the significance of scores 5.
3. HMMs can be proved
useful for modelling of processes which contain different stages that occur in
definite orders 9.
If, for example,
you want to model the behaviour of a technical system that first boots, then
operates, then enters sleep mode, and iteratively changes between sleep and
operation later on, you might need three states (boot, operate, sleep) and can
use this process model to find out what’s going on in the system at any one
time. Similar is the case with a human biological system where the observations
can be the sequence of symptoms of a human being. Human genome project also
requires the assimilation of HMM for DNA sequencing and RNA structuring 10.
like scalability and frequencies of paging update are familiar IR issues. Ranking
algorithms are implemented with the usage of methods that elucidate
relationships amongst the given query and the accumulated documents. All the
feedback provided by the IR system has to be evaluated, which is another issue
with IR. The way the system behaves, may or may not converge with the
suppositions of the user. All the documents that are extracted from the
procedure may not be able to give relevance to a given query.
The way a user
interacts with the IR system is termed as Information needs. Retrieval of a lot
of information might be disruptive in a number of systems. On the other hand,
in another number of systems, not returning a complete set of relevant data may
handling a set of voluminous information from the internet might be extremely difficult
because of the extremely large size of documents the server manages.
A thousand of
documents can be returned by a simple retrieval query. Many of those documents
are loosely related to the original criteria of retrieval. To deal with this,
an IR system is required to have a query management that is efficient enough as
well as contains a good level of ability in order to give weight as priority to
documents that are closer for relevance to the user query.