Getting informed of what is registered in the Web space on time, can greatly
help the psychologists, marketers and political analysts to familiarize,
analyse, make decision and act correctly based on the society`s different
needs. The great volume of information in the Web space hinders us to
continuously online investigate the whole space of the Web. Focusing on the
considered blogs limits our working domain and makes the online crawling in the
Web space possible.

On the internet, web surfers, in the search of information, always strive for
recommendations. The solutions for generating recommendations become more
difficult because of exponential increase in information domain day by day. In
this paper, we have calculated entropy based similarity between users to
achieve solution for scalability problem. Using this concept, we have
implemented an online user based collaborative web recommender system. In this
model based collaborative system, the user session is divided into two levels.
Entropy is calculated at both the levels.

MultiDendrograms is a Java-written application that computes agglomerative
hierarchical clusterings of data. Starting from a distances (or weights)
matrix, MultiDendrograms is able to calculate its dendrograms using the most
common agglomerative hierarchical clustering methods. The application
implements a variable-group algorithm that solves the non-uniqueness problem
found in the standard pair-group algorithm.

In 2010, Web users ordered, only in Amazon, 73 items per second and massively
contribute reviews about their consuming experience. As the Web matures and
becomes social and participatory, collaborative filters are the basic
complement in searching online information about people, events and products.
In Web 2.0, what connected consumers create is not simply content (e.g.
reviews) but context. This new contextual framework of consumption emerges
through the aggregation and collaborative filtering of personal preferences
about goods in the Web in massive scale.

As the amount of online document increases, the demand for document
classification to aid the analysis and management of document is increasing.
Text is cheap, but information, in the form of knowing what classes a document
belongs to, is expensive. The main purpose of this paper is to explain the
expectation maximization technique of data mining to classify the document and
to learn how to improve the accuracy while using semi-supervised approach.
Expectation maximization algorithm is applied with both supervised and
semi-supervised approach.

With the explosion of information stored world-wide,data intensive computing
has become a central area of research.Efficient management and processing of
this massively exponential amount of data from diverse sources,such as
telecommunication call data records,online transaction records,etc.,has become
a necessity.Removing redundancy from such huge(multi-billion records) datasets
resulting in resource and compute efficiency for downstream processing
constitutes an important area of study.

Many algorithms have been proposed for predicting missing edges in networks,
but they do not usually take account of which edges are missing. We focus on
networks which have missing edges of the form that is likely to occur in real
networks, and compare algorithms that find these missing edges. We also
investigate the effect of this kind of missing data on community detection
algorithms.

The use of community detection algorithms is explored within the framework of
cover song identification, i.e. the automatic detection of different audio
renditions of the same underlying musical piece. Until now, this task has been
posed as a typical query-by-example task, where one submits a query song and
the system retrieves a list of possible matches ranked by their similarity to
the query. In this work, we propose a new approach which uses song communities
to provide more relevant answers to a given query.

Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides.

Whereas today's information systems are well-equipped for efficient query
handling, their strict mathematical foundations hamper their use for everyday
tasks. In daily life, people expect information to be offered in a personalized
and focused way. But currently, personalization in digital systems still only
takes explicit knowledge into account and does not yet process conceptual
information often naturally implied by users. We discuss how to bridge the gap
between users and today's systems, building on results from cognitive
psychology.

Current "data deluge" has flooded the Web of Data with very large RDF
datasets. They are hosted and queried through SPARQL endpoints which act as
nodes of a semantic net built on the principles of the Linked Data project.
Although this is a realistic philosophy for global data publishing, its query
performance is diminished when the RDF engines (behind the endpoints) manage
these huge datasets. Their indexes cannot be fully loaded in main memory, hence
these systems need to perform slow disk accesses to solve SPARQL queries.

Due to the emergence of the semantic Web and the increasing need to formalize
human knowledge, ontologie engineering is now an important activity. But is
this activity very different from other ones like software engineering, for
example ? In this paper, we investigate analogies between ontologies on one
hand, types, objects and data bases on the other one, taking into account the
notion of evolution of an ontology. We represent a unique ontology using
different paradigms, and observe that the distance between these different
concepts is small.

Semantic web is the next generation web, which concerns the meaning of web
documents It has the immense power to pull out the most relevant information
from the web pages, which is also meaningful to any user, using software
agents. In today's world, agent communication is not possible if concerned
ontology is changed a little. We have pointed out this very problem and
developed an Ontology Purification System to help agent communication. In our
system you can send queries and view the search results. If it can't meet the
criteria then it finds out the mismatched elements.

This paper is about a better understanding on the structure and dynamics of
science and the usage of these insights for compensating the typical problems
that arises in metadata-driven Digital Libraries. Three science model driven
retrieval services are presented: co-word analysis based query expansion,
re-ranking via Bradfordizing and author centrality.

Ranking problem of web-based rating system has attracted many attentions. A
good ranking algorithm should be robust against spammer attack. Here we
proposed a correlation based reputation algorithm to solve the ranking problem
of such rating systems where user votes some objects with ratings. In this
algorithm, reputation of user is iteratively determined by the correlation
coefficient between his/her rating vector and the corresponding objects'
weighted average rating vector.

SPARQL query composition is difficult for the lay-person or even the
experienced bioinformatician in cases where the data model is unfamiliar.
Established best-practices and internationalization concerns dictate that
semantic web ontologies should use terms with opaque identifiers, further
complicating the task. We present SPARQL Assist: a web application that
addresses these issues by providing context-sensitive type-ahead completion to
existing web forms.

Because of the increasing number of electronic data, designing efficient
tools to retrieve and exploit documents is a major challenge. Current search
engines suffer from two main drawbacks: there is limited interaction with the
list of retrieved documents and no explanation for their adequacy to the query.
Users may thus be confused by the selection and have no idea how to adapt their
query so that the results match their expectations. This paper describes a
request method and an environment based on aggregating models to assess the
relevance of documents annotated by concepts of ontology.

The use of domain knowledge is generally found to improve query efficiency in
content filtering applications. In particular, tangible benefits have been
achieved when using knowledge-based approaches within more specialized fields,
such as medical free texts or legal documents. However, the problem is that
sources of domain knowledge are time-consuming to build and equally costly to
maintain.

Mercury is a federated metadata harvesting, search and retrieval tool based
on both open source and software developed at Oak Ridge National Laboratory. It
was originally developed for NASA, and the Mercury development consortium now
includes funding from NASA, USGS, and DOE. A major new version of Mercury was
developed during 2007. This new version provides orders of magnitude
improvements in search speed, support for additional metadata formats,
integration with Google Maps for spatial queries, support for RSS delivery of
search results, among other features.

This paper is a short description of an information retrieval system enhanced
by three model driven retrieval services: (1) co-word analysis based query
expansion, re-ranking via (2) Bradfordizing and (3) author centrality. The
different services each favor quite other - but still relevant - documents than
pure term-frequency based rankings. Each service can be interactively combined
with each other to allow an iterative retrieval refinement.

Text classification is the process of classifying documents into predefined
categories based on their content. It is the automated assignment of natural
language texts to predefined categories. Text classification is the primary
requirement of text retrieval systems, which retrieve texts in response to a
user query, and text understanding systems, which transform text in some way
such as producing summaries, answering questions or extracting data. Existing
supervised learning algorithms to automatically classify text need sufficient
documents to learn accurately.

Text classification is the process of classifying documents into predefined
categories based on their content. It is the automated assignment of natural
language texts to predefined categories. Text classification is the primary
requirement of text retrieval systems, which retrieve texts in response to a
user query, and text understanding systems, which transform text in some way
such as producing summaries, answering questions or extracting data. Existing
supervised learning algorithms for classifying text need sufficient documents
to learn accurately.

In this paper we present a method for reformulating the Recommender Systems
problem in an Information Retrieval one. In our tests we have a dataset of
users who give ratings for some movies; we hide some values from the dataset,
and we try to predict them again using its remaining portion (the so-called
"leave-n-out approach").

This paper describes an effective unsupervised method for query-by-example
speaker retrieval. We suppose that only one speaker is in each audio file or in
audio segment. The audio data are modeled using a common universal codebook.
The codebook is based on bag-of-frames (BOF). The features corresponding to the
audio frames are extracted from all audio files. These features are grouped
into clusters using the K-means algorithm. The individual audio files are
modeled by the normalized distribution of the numbers of cluster bins
corresponding to this file.

We engineer an algorithm to solve the approximate dictionary matching
problem. Given a list of words $\mathcal{W}$, maximum distance $d$ fixed at
preprocessing time and a query word $q$, we would like to retrieve all words
from $\mathcal{W}$ that can be transformed into $q$ with $d$ or less edit
operations. We present data structures that support fault tolerant queries by
generating an index. On top of that, we present a generalization of the method
that eases memory consumption and preprocessing time significantly. At the same
time, running times of queries are virtually unaffected.

Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences.

With the advancement of technology and reduced storage costs, individuals and
organizations are tending towards the usage of electronic media for storing
textual information and documents. It is time consuming for readers to retrieve
relevant information from unstructured document collection. It is easier and
less time consuming to find documents from a large collection when the
collection is ordered or classified by group or category. The problem of
finding best such grouping is still there.

Recommender systems apply data mining techniques and prediction algorithms to
predict users' interest on information, products and services among the
tremendous amount of available items. The vast growth of information on the
Internet as well as number of visitors to websites add some key challenges to
recommender systems. These are: producing accurate recommendation, handling
many recommendations efficiently and coping with the vast growth of number of
participants in the system.

Wiktionary is a unique, peculiar, valuable and original resource for natural
language processing (NLP). The paper describes an open-source Wiktionary
parser: its architecture and requirements followed by a description of
Wiktionary features to be taken into account, some open problems of Wiktionary
and the parser. The current implementation of the parser extracts the
definitions, semantic relations, and translations from English and Russian
Wiktionaries.

The Library of Babel, described by Jorge Luis Borges, stores an enormous
amount of information. The Library exists {\it ab aeterno}. Wikipedia, a free
online encyclopaedia, becomes a modern analogue of such a Library. Information
retrieval and ranking of Wikipedia articles become the challenge of modern
society. We analyze the properties of two-dimensional ranking of all Wikipedia
English articles and show that it gives their reliable classification with rich
and nontrivial features.

In the practical work of websites popularization, analysis of their
efficiency and downloading it is of key importance to take into account
web-ratings data. The main indicators of website traffic include the number of
unique hosts from which the analyzed website was addressed and the number of
granted web pages (hits) per unit time (for example, day, month or year). Of
certain interest is the ratio between the number of hits (S) and hosts (H). In
practice there is even used such a concept as "average number of viewed pages"
(S/H), which on default supposes a linear dependence of S on H.

Objectives: Text categorization has been used in biomedical informatics for
identifying documents containing relevant topics of interest. We developed a
simple method that uses a chi-square-based scoring function to determine the
likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our
procedure requires construction of a genetic and a nongenetic domain document
corpus. We used MeSH descriptors assigned to MEDLINE citations for this
categorization task. We compared frequencies of MeSH descriptors between two
corpora applying chi-square test.

This paper proposes some extensions to the work on kernels dedicated to
string alignment (biological sequence alignment) based on the summing up of
scores obtained by local alignments with gaps. The extensions we propose allow
to construct, from classical time warp distances, what we called summative time
warp kernels that are positive definite if some simple sufficient conditions
are satisfied.

Mining Time Series data has a tremendous growth of interest in today's world.
To provide an indication various implementations are studied and summarized to
identify the different problems in existing applications. Clustering time
series is a trouble that has applications in an extensive assortment of fields
and has recently attracted a large amount of research. Time series data are
frequently large and may contain outliers. In addition, time series are a
special type of data set where elements have a temporal ordering.

We describe a clustering method for labeled link network (semantic graph)
that can be used to group important nodes (highly connected nodes) with their
relevant link's labels by using PARAFAC tensor decomposition. In this kind of
network, the adjacency matrix can not be used to fully describe all information
about the network structure. We have to expand the matrix into 3-way adjacency
tensor, so that not only the information about to which nodes a node connects
to but by which link's labels is also included.

We propose to use MapReduce to quickly test new retrieval approaches on a
cluster of machines by sequentially scanning all documents. We present a small
case study in which we use a cluster of 15 low cost ma- chines to search a web
crawl of 0.5 billion pages showing that sequential scanning is a viable
approach to running large-scale information retrieval experiments with little
effort. The code is available to other researchers at:
this http URL

This paper addresses the design and implementation of BiLingual Information
Retrieval system on the domain, Festivals. A generic platform is built for
BiLingual Information retrieval which can be extended to any foreign or Indian
language working with the same efficiency. Search for the solution of the query
is not done in a specific predefined set of standard languages but is chosen
dynamically on processing the user's query. This paper deals with Indian
language Tamil apart from English.

In this paper, based on the user-tag-object tripartite graphs, we propose a
recommendation algorithm, which considers social tags as an important role for
information retrieval. Besides its low cost of computational time, the
experiment results of two real-world data sets, \emph{Del.icio.us} and
\emph{MovieLens}, show it can enhance the algorithmic accuracy and diversity.
Especially, it can obtain more personalized recommendation results when users
have diverse topics of tags.

This paper proposes an incremental method that can be used by an intelligent
system to learn better descriptions of a thematic context. The method starts
with a small number of terms selected from a simple description of the topic
under analysis and uses this description as the initial search context. Using
these terms, a set of queries are built and submitted to a search engine. New
documents and terms are used to refine the learned vocabulary.

Missing web pages, URIs that return the 404 "Page Not Found" error or the
HTTP response code 200 but dereference unexpected content, are ubiquitous in
today's browsing experience. We use Internet search engines to relocate such
missing pages and provide means that help automate the rediscovery process. We
propose querying web pages' titles against search engines. We investigate the
retrieval performance of titles and compare them to lexical signatures which
are derived from the pages' content. Since titles naturally represent the
content of a document they intuitively change over time.

World Wide Web is a huge repository of web pages and links. It provides
abundance of information for the Internet users. The growth of web is
tremendous as approximately one million pages are added daily. Users' accesses
are recorded in web logs. Because of the tremendous usage of web, the web log
files are growing at a faster rate and the size is becoming huge. Web data
mining is the application of data mining techniques in web data.

Consider a family of sets and a single set, called the query set. How can one
quickly find a member of the family which has a maximal intersection with the
query set? Time constraints on the query and on a possible preprocessing of the
set family make this problem challenging. Such maximal intersection queries
arise in a wide range of applications, including web search, recommendation
systems, and distributing on-line advertisements. In general, maximal
intersection queries are computationally expensive.

The United States Code (Code) is a document containing over 22 million words
that represents a large and important source of Federal statutory law. Scholars
and policy advocates often discuss the direction and magnitude of changes in
various aspects of the Code. However, few have mathematically formalized the
notions behind these discussions or directly measured the resulting
representations. This paper addresses the current state of the literature in
two ways.

The shift from an information society to a knowledge society require rapid
information harvesting, reliable search and instantaneous on demand delivery.
Information extraction agents are used to explore and collect data available
from Web, in order to effectively exploit such data for business purposes, such
as automatic news filtering, advertisement or product searching and price
comparing. In this paper, we develop a real-time automatic harvesting agent for
adverts posted on Servihoo web portal and an SMS-based notification system.

In this paper we describe a mechanism to improve Information Retrieval (IR)
on the web. The method is based on Formal Concepts Analysis (FCA) that it is
makes semantical relations during the queries, and allows a reorganizing, in
the shape of a lattice of concepts, the answers provided by a search engine. We
proposed for the IR an incremental algorithm based on Galois lattice. This
algorithm allows a formal clustering of the data sources, and the results which
it turns over are classified by order of relevance.

Text segmentation is an inherent part of an OCR system irrespective of the
domain of application of it. The OCR system contains a segmentation module
where the text lines, words and ultimately the characters must be segmented
properly for its successful recognition. The present work implements a Hough
transform based technique for line and word segmentation from digitized images.
The proposed technique is applied not only on the document image dataset but
also on dataset for business card reader system and license plate recognition
system.

In Information Retrieval (IR), whether implicitly or explicitly, queries and
documents are often represented as vectors. However, it may be more beneficial
to consider documents and/or queries as multidimensional objects. Our belief is
this would allow building "truly" interactive IR systems, i.e., where
interaction is fully incorporated in the IR framework.

Within the documentary system domain, the integration of thesauri for
indexing and retrieval information steps is usual. In libraries, documents own
rich descriptive information made by librarians, under descriptive notice based
on Rameau thesaurus. We exploit two kinds of information in order to create a
first semantic structure. A step of conceptualization allows us to define the
various modules used to automatically build the semantic structure of the
indexation work. Our current work focuses on an approach that aims to define an
ontology based on a thesaurus.

Emergence of various vertical search engines highlights the fact that a
single ranking technology cannot deal with the complexity and scale of search
problems. For example, technology behind video and image search is very
different from general web search. Their ranking functions share few features.
Question answering websites (e.g., Yahoo! Answer) can make use of text matching
and click features developed for general web, but they have unique page
structures and rich user feedback, e.g., thumbs up and thumbs down ratings in
Yahoo! answer, which greatly benefit their own ranking.

When users rate objects, a sophisticated algorithm that takes into account
ability or reputation may produce a fairer or more accurate aggregation of
ratings than the straightforward arithmetic average. Recently a number of
authors have proposed different co-determination algorithms where estimates of
user and object reputation are refined iteratively together, permitting
accurate measures of both to be derived directly from the rating data.

How to rank web pages, scientists and online resources has recently attracted
increasing attention from both physicists and computer scientists. In this
paper, we study the ranking problem of rating systems where users vote objects
by discrete ratings. We propose an algorithm that can simultaneously evaluate
the user reputation and object quality in an iterative refinement way.
According to both the artificially generated data and the real data from
MovieLens and Amazon, our algorithm can considerably enhance the ranking
accuracy.

Mutual information among three or more dimensions (mu-star = - Q) has been
considered as interaction information. However, Krippendorff (2009a, 2009b) has
shown that this measure cannot be interpreted as a unique property of the
interactions and has proposed an alternative measure of interaction information
based on iterative approximation of maximum entropies. Q can then be considered
as a measure of the difference between interaction information and redundancy
generated in a model entertained by an observer.

We introduce K-tree in an information retrieval context. It is an efficient
approximation of the k-means clustering algorithm. Unlike k-means it forms a
hierarchy of clusters. It has been extended to address issues with sparse
representations. We compare performance and quality to CLUTO using document
collections. The K-tree has a low time complexity that is suitable for large
document collections. This tree structure allows for efficient disk based
implementations where space requirements exceed that of main memory.

Recent advances in hardware sophistication related to graphics display, audio
and video devices made available a large number of multimedia and hypermedia
applications. These multimedia applications need to store and retrieve the
different forms of media like text, hypertext, graphics, still images,
animations, audio and video. Dance is one of the important cultural forms of a
nation and dance video is one such multimedia types. Archiving and retrieving
the required semantics from these dance media collections is a crucial and
demanding multimedia application.

Now a day's, search engines are been most widely used for extracting
information's from various resources throughout the world. Where, majority of
searches lies in the field of biomedical for retrieving related documents from
various biomedical databases. Currently search engines lacks in document
clustering and representing relativeness level of documents extracted from the
databases. In order to overcome these pitfalls a text based search engine have
been developed for retrieving documents from Medline and PubMed biomedical
databases.

Document indexation is an essential task achieved by archivists or automatic
indexing tools. To retrieve relevant documents to a query, keywords describing
this document have to be carefully chosen. Archivists have to find out the
right topic of a document before starting to extract the keywords. For an
archivist indexing specialized documents, experience plays an important role.
But indexing documents on different topics is much harder. This article
proposes an innovative method for an indexing support system.

In this paper, we explain social information retrieval (SIR) and
collaborative information retrieval (CIR). We see SIR as a way of knowing who
to collaborate with in resolving an information problem while CIR entails the
process of mutual understanding and solving of an information problem among
collaborators. We are interested in the transition from SIR to CIR hence we
developed a communication model to facilitate knowledge sharing during CIR.

The dynamic environment in the real world calls for the adaptive techniques
for information filtering, namely to provide real-time responses to the changes
of system data. Where many incremental algorithms are designed for this
purpose, they are usually challenged by the worse and worse performance
resulted from the cumulative errors over time. In this Letter, we propose two
incremental diffusion-based algorithms for the personalized recommendations,
which integrate some pieces of local and fast updatings to achieve the
approximate results.

Search engines are nowadays one of the most important entry points for
Internet users and a central tool to solve most of their information needs.
Still, there exist a substantial amount of users' searches which obtain
unsatisfactory results. Needless to say, several lines of research aim to
increase the relevancy of the results users retrieve. In this paper the authors
frame this problem within the much broader (and older) one of information
overload.

Co-occurrence matrices, such as co-citation, co-word, and co-link matrices,
have been used widely in the information sciences. However, confusion and
controversy have hindered the proper statistical analysis of this data. The
underlying problem, in our opinion, involved understanding the nature of
various types of matrices. This paper discusses the difference between a
symmetrical co-citation matrix and an asymmetrical citation matrix as well as
the appropriate statistical techniques that can be applied to each of these
matrices, respectively.

Compression of inverted lists with methods that support fast intersection
operations is an active research topic. Most compression schemes rely on
encoding differences between consecutive positions with techniques that favor
small numbers. In this paper we explore a completely different alternative: We
use Re-Pair compression of those differences. While Re-Pair by itself offers
fast decompression at arbitrary positions in main and secondary memory, we
introduce variants that in addition speed up the operations required for
inverted list intersection.

The WWW is the most important source of information. But, there is no
guarantee for information correctness and lots of conflicting information is
retrieved by the search engines and the quality of provided information also
varies from low quality to high quality. We provide enhanced trustworthiness in
both specific (entity) and broad (content) queries in web searching. The
filtering of trustworthiness is based on 5 factors: Provenance, Authority, Age,
Popularity, and Related Links.

Collaborative tags are playing more and more important role for the
organization of information systems. In this paper, we study a personalized
recommendation model making use of the ternary relations among users, objects
and tags. We propose a measure of user similarity based on his preference and
tagging information.

XML has become the de-facto standard for data representation and exchange,
resulting in large scale repositories and warehouses of XML data. In order for
users to understand and explore these large collections, a summarized, bird's
eye view of the available data is a necessity. In this paper, we are interested
in semantic XML document summaries which present the "important" information
available in an XML document to the user. In the best case, such a summary is a
concise replacement for the original document itself.

A content-based network representation of web logs (blogs) using a basic
word-overlap similarity measure is presented. Due to a strong signal in blog
data the approach is sufficient for accurately classifying blogs. Using Swedish
blog data we demonstrate that blogs that treat similar subjects are organized
in clusters that, in turn, are hierarchically organized in higher-order
clusters. The simplicity of the representation renders it both computationally
tractable and transparent.

We provide a comprehensive and critical review of the h-index and its most
important modifications proposed in the literature, as well as of other similar
indicators measuring research output and impact. Extensions of some of these
indices are presented and illustrated.

Nowadays people realize that it is difficult to find information simply and
quickly on the bulletin boards. In order to solve this problem, people propose
the concept of bulletin board search engine. This paper describes the
priscrawler system, a subsystem of the bulletin board search engine, which can
automatically crawl and add the relevance to the classified attachments of the
bulletin board.

We present a new method to accelerate the HITS algorithm by exploiting
hyperlink structure of the web graph. The proposed algorithm extends the idea
of authority and hub scores from HITS by introducing two diagonal matrices
which contain constants that act as weights to make authority pages more
authoritative and hub pages more hubby. This method works because in the web
graph good authorities are pointed to by good hubs and good hubs point to good
authorities. Consequently, these pages will collect their scores faster under
the proposed algorithm than under the standard HITS.