Abstract :- :-
Automatic
Discovery of Association Orders between Name and Aliases from the Web :- :-
Many
celebrities and experts from various fields may have been referred by not only
their personal names but also by their aliases on web. Aliases are very
important in information retrieval to retrieve complete information about a
personal name from the web, as some of the web pages of the person may also be
referred by his aliases. The aliases for a personal name are extracted by
previously proposed alias extraction method. In information retrieval, the web
search engine automatically expands the search query on a person name by
tagging his aliases for complete information retrieval thereby improving recall
in relation detection task and achieving a significant mean reciprocal rank
(MRR) of search engine.
For
the further substantial improvement on recall and MRR from the previously
proposed methods, our proposed method will order the aliases based on their
associations with the name using the definition of anchor texts-based
co-occurrences between name and aliases in order to help the search engine tag
the aliases according to the order of associations. The association orders will
automatically be discovered by creating an anchor texts-based co-occurrence
graph between name and aliases.
Ranking
support vector machine (SVM) will be used to create connections between name
and aliases in the graph by performing ranking on anchor texts-based
co-occurrence measures. The hop distances between nodes in the graph will lead
to have the associations between name and aliases. The hop distances will be
found by mining the graph. The proposed method will outperform previously
proposed methods, achieving substantial growth on recall and MRR.
Existing System
The
existing namesake disambiguation algorithm assumes the real name of a person to
be given and does not attempt to disambiguate people who are referred only by
aliases.
Disadvantage
1) To low MRR and AP scores on all data sets.
2) To complex hub discounting measure.
Proposed System
The
proposed method will work on the aliases and get the association orders between
name and aliases to help search engine tag those aliases according to the
orders such as first order associations, second order associations etc so as to
substantially increase the recall and MRR of the search engine while searching
made on person names. The term recall is defined as the percentage of relevant
documents that were in fact retrieved for a search query on search engine.
The
mean reciprocal rank of the search engine for a given sample of queries is that
the average of the reciprocal ranks for each query. The term word co-occurrence
refers to the temporal property of the two words occurring at the same web page
or same document on the web. The anchor text is the clickable text on web
pages, which points to a particular web document. Moreover the anchor texts are
used by search engine algorithms to provide relevant documents for search
results because they point to the web pages that are relevant to the user
queries.
So
the anchor texts will be helpful to find the strength of association between two
words on the web. The anchor texts-based co-occurrence means that the two
anchor texts from the different web pages point to the same the URL on the web.
The anchor texts which point to the same URL are called as inbound anchor
texts.
The
proposed method will find the anchor texts-based co-occurrences between name
and aliases using co-occurrence statistics and will rank the name and aliases
by support vector machine according to the co-occurrence measures in order to
get connections among name and aliases for drawing the word co-occurrence
graph.
Then
a word co-occurrence graph will be created and mined by graph mining algorithm
so as to get the hop distance between name and aliases that will lead to the
association orders of aliases with the name. The search engine can now expand
the search query on a name by tagging the aliases according to their
association orders to retrieve all relevant pages which in turn will increase
the recall and achieve a substantial MRR.
Modules Description
1. Co-occurrences in Anchor Texts
The
proposed method will first retrieve all corresponding URLs from search engine
for all anchor texts in which name and aliases appear. Most of the search
engines provide search operators to search in anchor texts on the web. For
example, Google provides In anchor or Allinanchor search operator to retrieve
URLs that are pointed by the anchor text given as a query. For example, query
on “Allinanchor:Hideki Matsui” to the Google will provide all URLs pointed by
Hideki Matsui anchor text on the web.
2. Role of Anchor Texts
The
main objective of search engine is to provide the most relevant documents for a
user’s query. Anchor texts play a vital role in search engine algorithm because
it is clickable text which points to a particular relevant page on the web.
Hence search engine considers anchor text as a main factor to retrieve relevant
documents to the user’s query. Anchor texts are used in synonym extraction,
ranking and classification of web pages and query translation in cross language
information retrieval system.
3. Anchor Texts Co-Occurrence Frequency
The
two anchor texts appearing in different web pages are called as inbound anchor
texts if they point to the same URL. Anchor texts co-occurrence frequency between anchor texts refers to the number of
different URLs on which they co-occur. For example, if p and x that are two
anchor texts are co-occurring, then p and x point to the same URL. If the
co-occurrence frequency between p and x is that say an example k, and then p
and x co-occur in k number of different URLs. For example, the picture of
Arnold Schwarzenegger is shown in Fig 2 which is being liked by four different
anchor texts.
5. Ranking Anchor Texts
Ranking
SVM will be used for ranking the aliases. The ranking SVM will be trained by
training samples of name and aliases. All the co-occurrence measures for the
anchor texts of the training samples will be found and will be normalized into
the range of [0-1]. The normalized values termed as feature vectors will be
used to train the SVM to get the ranking function to test the given anchor
texts of name and aliases. Then for each anchor text, the trained SVM using the
ranking function will rank the other anchor texts with respect to their
co-occurrence measures with it. The highest ranking anchor text will be elected
to make a first–order association with its corresponding anchor text for which
ranking was performed. Next the word co-occurrence graph will be drawn for name
and aliases according to the first order associations between them.
6. Discovery Of Association Orders
Using
the graph mining algorithm, the word co-occurrence graph will be mined to find
the hop distances between nodes in graph. The hop distances between two nodes
will be measured by counting the number of edges in-between the corresponding
two nodes. The number of edges will yield the association orders between two
nodes. According to the definition, a node that lies n hops away from p has an
n-order co-occurrence with p. Hence the first, second and higher order
associations between name and aliases will be identified by finding the hop
distances between them. The search engine can now expand the query on person
names by tagging the aliases according to the association orders with the name.
Thereby the recall will be substantially improved by 40% in relation detection
task. Moreover the search engine will get a substantial MRR for a sample of
queries by giving relevant search results.
For More Details About
This Project Please Contact
Logic
Systems
Hyderabad
- 500038
Email
id: logicsystems24x7@gmail.com
Website:
- www.logicsystems.org.in
System Configuration
Hardware
Configuration
1. Processor
:- Pentium –III
2. Speed
:- 1.1 GHz
3. RAM
:- 256 MB (min)
4. Hard
Disk :- 20 GB
5. Floppy
Drive :- 1.44 MB
6. Key
Board :- Standard Windows Keyboard
7. Mouse
:- Two or Three Button Mouse
8. Monitor
:- SVGA
Software
Configuration
1. Operating
System :- Windows95/98/2000/XP
2. Application Server :- Tomcat5.0/6.X
3. Front
End :- HTML, Java, Jsp
4. Scripts
:- JavaScript.
5. Server
side Script :- Java Server Pages.
6. Database
:- Mysql
7. Database
Connectivity :- JDBC.
