This document discusses Aardvark, a large-scale social search engine. It begins with an abstract that introduces social search engines and notes how Aardvark differs from traditional hypertextual search engines by using a user's social network as an information source rather than documents. The document then covers Aardvark's architecture, how it works by socially crawling profiles, indexing people, analyzing questions, ranking results, and its user interface. Examples are provided to illustrate how Aardvark routes questions to experts in a user's network to provide answers, unlike traditional search engines that return documents.

1. Aardvark : Large - Scale Social Search Engine
A seminar report submitted in partial fulfillment of the requirements
for the award of the degree of
Bachelor of Technology
in
Computer Science & Engineering
Eighth Semester 2011 Admission

2. ABSTRACT
A program that searches for and identifies items in a database that correspond to
keywords or characters specified by the user, used especially for finding particular
web sites on the World Wide Web is a simple definition for a Search Engine. Cur-
rently there are two kind of Search Engines available in the Internet which are Hyper
Textual Based Search Engine and Social Search Engine.
This report is mainly concentrating on the concepts of Social Search Engine. The
Social Search Engine is also like an ordinary web search engine but have a vast differ-
ence on comparison with the traditional Hyper Text Based Search Engine. In Social
Search Engine the Information source is not a Document but a Living Human Being.
In traditional search engine when a query is provided as an input the search engine
algorithm searches for the best document containing the requested keyword or the
keyword related documents. But in a social search engine when a query is provided
as input , it’s meaning . it’s purpose is found out then the search engines finds out
the best person who can help the question initiator to solve the problem. The out-
put generated in the social search engine is the solution generated by the answerer
in more natural language and more understandable format for the question initiator.
So it can also be said that the Social Search Engine makes the Question/query
reach the expert solution provider or the Answerer while the Hyper Textual only
leads to a document. More than one solutions can be also obtained for a single
query in the form of suggestions in Social Search Engine.

3. Contents
List of Figures ii
1 Introduction 1
1.1 What is Village Paradigm? . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 What is Aardvark? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Outline 4
3 Motivation 5
4 Why Aardvark was Created? 6
5 Aardvark Architecture 8
5.1 Sign Up Process of a User . . . . . . . . . . . . . . . . . . . . . . . . 8
5.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.3 Working . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6 How Aardvark Works? 12
6.1 Work Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
6.2 Social Crawling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6.3 Indexing People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.4 Question Analyzer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6.5 Ranking Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.6 User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6.7 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
7 Future Works 26
8 Conclusion 28
Bibliography 29
i

4. List of Figures
1.1 Screen Shot of Google Result. . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Aardvark Search Engine and Animal . . . . . . . . . . . . . . . . . . 3
4.1 Aardvark Search Engine versus Google’s Search Engine . . . . . . . . 7
5.1 Schematic of the architecture of Aardvark . . . . . . . . . . . . . . . 10
6.1 Aardvark in iPhone . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.2 Aardvark’s working using a pictorial description . . . . . . . . . . . . 22
6.3 Frequent Questions asked in Aardvark . . . . . . . . . . . . . . . . . 23
6.4 A random set of queries from Aardvark . . . . . . . . . . . . . . . . . 23
6.5 Example 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.6 Example 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.7 Example 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
7.1 Increasing number of users in Aardvark. . . . . . . . . . . . . . . . . 26
ii

5. Chapter 1
Introduction
A web search engine is a software system that is designed to search for information
on the World Wide Web. The search results are generally presented in a line of
results often referred to as Search Engine Results Pages (SERPs). The information
may be a mix of web pages, images, and other types of files. Some search engines
also mine data available in databases or open directories. Unlike web directories,
which are maintained only by human editors, search engines also maintain real-time
information by running an algorithm on a web crawler.
The famous search engine Google works on the principal of large-scale hyper textual
web searching process that is based on the inputted keywords the searching process
is done over the concept of page ranking. The page ranking means the number of
visits/votes achieved to the related web pages of the inputted key word. But there
is a drawback what if the input was ”Do you have any good babysitter recommen-
dations in Palo Alto for my 6-year-old twins? I’m looking for somebody that won’t
let them watch TV.”. When this query was given to Google it generated the result
which is shown in the Figure:1.1. This is not the answer to the input provided by the
user so in order to generate more suitable and more accurate solution a query the
approach of large - scale social search engine is used. The large - scale social search
engine takes input via instant message, email, web input, text message, or voice.
The search engine then routes the question to the person in the user’s extended
social network most likely to be able to answer that question. As compared to a
traditional web search engine, where the challenge lies in finding the right document
to satisfy a user’s information need, the challenge in a social search engine lies in
finding the right person to satisfy a user’s information need.
1.1 What is Village Paradigm?
From the name itself it is very clear that the village paradigm is based on the con-
cept of village, i.e the village is the source of Information Retrieval. The hyper
textual search engine and the social search engine works, works in simple terms. As
mentioned early the Google works on the concept of the hyper textual search engine
and in this paradigm (concept) of Information Retrieval (IR) from a Library is used.
The Google’s search engine does the process of IR from the Stanford Digital Library.
The social search engine works on the paradigm called The Village Paradigm. The
1

6. Aardvark : Large - Scale Social Search Engine
Figure 1.1: Screen Shot of Google Result.
village paradigm states that the data which is needed for the user is possessed or
known by a person or individual in the village and we then find that particular
person by passing information from one person to another.
The differences how information can be obtained from a library versus a village
suggest some useful principles for designing a social search engine. We can see the
clear cut difference between a library and a village that is the library consist of set
of chunks of books while a village consist of chunks of living people. In a library,
the user uses a keyword to search. The knowledge is extracted from a small number
of content publishers, and trust is based on authority of the data publishers. While
in a village, user uses natural language to ask questions and the answers are gener-
ated in real-time by anyone in the community, and trust is based on intimacy. The
real-time response for a highly contextualized and subjective query, for example, the
query ”Do you have any good babysitter recommendations in Palo Alto for my 6-
year-old twins? I’m looking for somebody that won’t let them watch TV.” is better
answered by a real time friend than the library. These differences in information
retrieval paradigm states that the social based search engine is entirely different
from that of the traditional hyper textual search engine. Thus as the concept is
entirely different than comparing with the traditional search engine it uses a new
architecture. Aardvark is the only social search engine based on the village
paradigm up till now.
So a study about Aardvark will be like the study of Social Search Engine.
Dept. of Computer Science & Engg. 2 SIMAT, Vavanoor

7. Aardvark : Large - Scale Social Search Engine
1.2 What is Aardvark?
Actually the aardvark is a medium - sized, burrowing, nocturnal mammal (Figure
:4.1a) native to Africa. It is a nocturnal feeder, it subsists on ants and termites,
which it will dig out of their hills using its sharp claws and powerful legs. It also
will utilize its digging ability to create burrows in which to live and rear its young.
It receives a ”least concern” rating from the IUCN, although its numbers seem to
be decreasing.
(a) Aardvark Animal (b) Search Engine Logo
Figure 1.2: Aardvark Search Engine and Animal
Aardvark is a social search engine (Figure:4.1b) based on the village paradigm.
Detailed description is also provided based on the architecture, ranking algorithms,
and user interfaces in Aardvark, and the design considerations that was being moti-
vated . Aardvark is believed to be useful to the research community for two reasons.
First, the argument made in the original Anatomy paper[1] still holds true since
most search engine development is done in industry rather than academia, the re-
search literature describing end-to-end search engine architecture is limited to their
own company as a result knowledge is not flourishing out. Second, the shift in
paradigm opens up a number of interesting research questions in information re-
trieval, for example around expertise classification, implicit network construction,
and conversation design.
Following the architecture description, a statistical analysis of usage patterns in
Aardvark is also presented. It was found that, as compared to traditional search,
Aardvark queries tend to be long, highly contextualized and subjective. In simple
terms the Aardvark queries tend to be the types of queries that are not well-serviced
by traditional search engines. It was also found out that the vast majority of ques-
tions got answered promptly and satisfactorily, and the users were surprisingly ac-
tive, both in asking and answering. Aardvark performs very well on queries that deal
with opinion, advice, experience, or recommendations, while traditional text-based
search engines remain a good choice for queries that are factual or navigational.
Dept. of Computer Science & Engg. 3 SIMAT, Vavanoor

8. Chapter 2
Outline
This report is presenting the entirely new concept in the field of search engine which
is called the ”Social Search Engine”. The most famous traditional search engine
which is none other than the ”Google” is a hypertext based search engine. While
this Social Search Engine is a search engine which connects the query inputed by
the user into the social network of the user’s people to get the solution of the query.
The first chapter is about describing out an introduction about the core concepts of
the Social Search Engine. This chapter also describes about the introductory parts
of the Social Search Engine along with the Introduction of the Aardvark Search
Engine. The third chapter describes about the relevance and motivation of this
Search Engine. The fourth chapter gives the solution to various questions like Why
Aardvark was created? and What it makes to be different from other Search Engine.
The fifth chapter describes about the Architecture and the various important com-
ponents of this Search Engine. The Sixth chapter details about the entire working
of this Search Engine along with descriptions of various important key things of
Aardvark. The seventh chapter states about the current status of Aardvark and the
details about the Social Search Engines which are in market. The final chapter is
the conclusion where a summarized format of the entire report is described. The
conclusion chapter is followed by the Bibliography.
4

9. Chapter 3
Motivation
The main motivation of this method is to create a general awareness about the new
form of search engine which is more closer to the humans’ natural language and far
more realistic. This social based search engine will improvise the searching process
on the web by making the user interface part more closer to humans. The other
purpose behind this search engine is for the academic and research purpose so as
more social based engine’s advanced version comes into market. On implementation
of this project the network paradigm has to be changed resulting in the entire
manufacturing process of the network and the search engines. The world will be
more like a village as the querying process leads the user to a single and the best
resource provider, here a user, from any part of the world.
5

10. Chapter 4
Why Aardvark was Created?
It is a general question to ask why the Aardvark search engine was created. As men-
tioned in the introduction part , the main goal of Aardvark is to increase the search
result operation for all type of queries. Its an extended version of the traditional
search approaches. Aardvark is a social search service that connected users live with
friends or friends-of-friends who were able to answer their questions, also known as
a knowledge market. Users submitted questions via the Aardvark website, email or
instant messenger and Aardvark identifies and facilitates a live chat or email con-
versation with one or more topic experts in the asker’s extended social network.
In the field of computer science the query is a string in English language which
represent a question as an input The method of asking questions in English to a
search engine are of four types.
1. Objective Questions:- Those questions which have a predefined answer are
called objective questions. Example ”Who is the current President of India?”.
The output for these types of questions are 100% accurate and fully satisfac-
tory.
2. Subjective Questions:- Those questions for which the solution/answer varies
from user to user is called subjective question. Example ”Which is your fa-
vorite President of India?” . The search result generated by the search engines
are not full satisfactory. It only leads to another factor to browse further.
3. Natural Language Questions:- The process of translating the thoughts of a
human into keyword is the main asset here. But this concept is very tough
and the current progress in this field is insufficient as there are lots of keywords
that can not be put into consideration.
4. Q&A not available in Web:- It is true that there are enormous number of infor-
mational resources available in the web. But on comparison of this resources
with the resources that can be obtained form every single human on earth is
unlimited. For example ”The information that you possess is not there in web.
Your Favorite restaurant, your own information which is local to yourself.”
Now looking into an example (Figure:4.1) where the evaluation of a query done
in two search engines, the Social Search Engine and the Hypertextual Search Engine,
are shown.
6

11. Aardvark : Large - Scale Social Search Engine
(a) Google (b) Aardvark App in iPhone
Figure 4.1: Aardvark Search Engine versus Google’s Search Engine
It is clear in the Figure that when the Query ”Where can i find a great half day
hike within a short drive of San Francisco? I just moved out here looking for some-
thing great views and varied terrain(I am a strong hiker). Any Tips appreciated.”
was provided as input to the Google’s Search engine its best result was a set of
web links which pointed out to famous hiking organization providing hiking stuffs.
While on the contrary the Aardvark gave the result of the perfect reply as expected
by the user. So this results points that the Hyper textual search engine looks for
the keywords in the query and generate the related documents. So it is nearly as a
static reply and this search engine doesn’t goes for the meaning for the query. The
meaning for a query can only be understood by a human as a result Aardvark gave
the optimal result as it is not static and its not form a library.
The main intention to launch Aardvark was Resource Enlargement. If there was
provision provided in this system that keep tracks of the ongoing queries being pro-
cessed and then store the optimal result for all kind of questions then the Search
Engine will be more and more smarted and will be more like a Non Living Friend
with whom a user can clarify the queries. Aardvark is used for asking subjective
questions for which human judgment or recommendation was desired. It was also
used extensively for technical support questions. Users could also review question
and answer history and other settings on the Aardvark website. Google acquired
Aardvark for 50 million dollars on February 11, 2010.[3][4]. In September 2011,
Google announced it would discontinue a number of its products, including Aard-
vark.
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12. Chapter 5
Aardvark Architecture
The main components of Aardvark are:
1. Crawler and Indexer - These two parameters are used to find and label re-
sources that contains the correct information about the query being asked
here the resources are in the sense of the human beings not documents.
2. Query Analyzer - As mentioned earlier the Query which is provided as an
input is a highly complex query. From this query the meaning or the purpose
has to be extracted not the important keywords.
3. Ranking Function - As per the Hypertextual Search engine where the resource
pages are ranked, here some what a similar process i carried out where the
answerer is ranked based on the result the answerer generated for a query.
4. User Interface - To present the information to the user in an accessible and
interactive form. The main forms of user interface is by mobile phone apps, e
mails, Web pages, Instant Messaging.
Most document - based search engines have similar key components with similar
aims[1], but the means of achieving those aims are quite different. Before discussing
in detail about Aardvark , it is useful to describe what happens behind the scenes
when a new user joins Aardvark and when a user asks a question to Aardvark.
5.1 Sign Up Process of a User
The strength of the Aardvark lies in the number of users in the Aardvark network,
because more the number of user ,more is the information resources in the network.
Every individual may possess information of same or different or new things, but
the level of information varies from user to user. Some user may have high knowl-
edge about a particular topic while some possess less knowledge. Based on these
factors individuals are separated form each other. So the prime goal here is to know
the information possessed by the user and this process has to be done in a simple
format. When a new user first joins Aardvark, the Aardvark system performs a
number of indexing steps in order to be able to direct the appropriate questions to
her for answering.
8

13. Aardvark : Large - Scale Social Search Engine
The questions in Aardvark are routed to the user’s extended network, the first step
involves indexing friendship and affiliation information. For this process of affiliating
information, the data structure responsible for is the Social Graph [16]. The Social
Graph in the Internet context is a graph that depicts personal relations of Internet
users. In short, it is a social network, where the word graph has been taken from
graph theory to emphasize that rigorous mathematical analysis will be applied as
opposed to the relational representation in a social network. The social graph has
been referred to as ”the global mapping of everybody and how they’re related”.
Aardvark is not to aiming to build a social network, but rather to allow people
to make use of their existing social networks like facebook, twitter, hangouts, gmail
contacts etc. . So in the sign-up process, a new user has the option of connecting to
a/many social network such as Facebook or LinkedIn, importing their contact lists
from a web mail program, or manually inviting friends to join. Additionally, any-
body whom the user invites to join Aardvark is appended to their Social Graph and
such invitations are a major source of new users. Finally, Aardvark users are con-
nected through common groups which reflect real-world affiliations they have, such
as the schools they have attended and the companies they have worked at; these
groups can be imported automatically from social networks, or manually created by
users. Aardvark indexes this information and stores it in the Social Graph, which
is a fixed width ISAM index sorted by userId. ISAM stands for Indexed Sequential
Access Method, a method for indexing data for fast retrieval. In an ISAM system,
data is organized into records which are composed of fixed length fields. Records are
stored sequentially, originally to speed access on a tape system. A secondary set of
hash tables known as indexes contain ”pointers” into the tables, allowing individual
records to be retrieved without having to search the entire data set.
Simultaneously, Aardvark indexes the topics about which the new user has some
level of knowledge or experience. This topical expertise can be gathered from sev-
eral sources by using several methods: a user can indicate topics in which he believes
himself to have expertise; a user’s friends can indicate which topics they trust the
user’s opinions about; a user can specify an existing structured profile page from
which the Topic Parser parses additional topics; a user can specify an account on
which they regularly post status updates (e.g. Twitter or Facebook), from which the
Topic Extractor extracts topics (from unstructured text) in an ongoing basis and
finally, the Aardvark also observes the user’s behavior on Aardvark, in answering
a type of questions,or electing not to answer to which kind of questions or about
particular topics.
The set of topics associated with a user is recorded in the Forward Index, which
stores each userId, a scored list of topics, and a series of further scores about a
user’s behavior (e.g., responsiveness or answer quality). From the Forward Index,
Aardvark constructs an Inverted Index. The Inverted Index stores each topicId and
a scored list of userIds that have expertise in that topic. In addition to topics, the
Inverted Index stores scored lists of userIds for features like answer quality and re-
sponse time. Once the Inverted Index and Social Graph for a user are created, the
user is now active on the system and ready to ask her first question.
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14. Aardvark : Large - Scale Social Search Engine
5.2 Architecture
Figure 5.1: Schematic of the architecture of Aardvark
The Figure:5.1 shows the schematic Architecture of the Aardvark Search Engine.
The flow of the request and reply is in a systematic order and each component of
this architecture have a unique role.
• Gateways: - The modes of getting inputs from the user and passing it through
the network to the user which will generate the result. This gateway can be
an E mail, iPhone App, SMS, Website etc.
• Conversation Manger & Question Analyzer- The Social Search engine has two
interesting kind of responsibilities. The first is to do Dialog Management so
if a user is interesting with a social search engine over IM, there need to be
a little bot-mind that knows how to interpret all the commands that user
is passing into the network. If a user just types in a question the bot will
recognize whether the input is a question or an answer to somebody’s else
question. If a user type command like ”Show my past history”, the bot has
to recognize that. So part of conversation management is basically being a
Natural Language Bot. The other part is supervising the question to make
sure that it gets the answer. So it is kind of being a mission control for finding
all the people who might be relevant, and then contacting them one by one,
seeing if the relevant answerers are responding and moving on to the other
people if there is no response. Organizing the whole episode of from user
submitting a question to getting an answer is the key role of the conversation
manager..
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15. Aardvark : Large - Scale Social Search Engine
• Routing Engine:- It’s job is to do the actual ranking of candidate answer. So
it is very much usual to ask that how in a web search engine designed socially
does the task of ranking as ranking is mostly done over the documents.Here
ranking of people on a user’s social network in order of who the algorithm
think is best to answer user’s question.
• Database & Importers : After the entire discussion it is very clear that the
database is nothing but the number of users/ participants in the Aardvark
Network. The Importers are those source which gives the related users of a
specific user like for example the mutual friend concept in Facebook. This will
help to increase the number of user in the Aardvark Network.
5.3 Working
A user begins by asking a question, most commonly through instant message (on
the desktop) or text message (on the mobile phone). The question gets sent from
the input device to the Transport Layer, where it is normalized to a Message data
structure, and sent to the Conversation Manager. Once the Conversation Manager
determines that the message is a question, it sends the question to the Question
Analyzer to determine the appropriate topics for the question. The Conversation
Manager informs the asker which primary topic was determined for the question,
and gives the asker the opportunity to edit it. It simultaneously issues a Routing
Suggestion Request to the Routing Engine. The routing engine plays a role analo-
gous to the ranking function in a text based search engine. It accesses the Inverted
Index and Social Graph for a list of candidate answerers, and ranks them to reflect
a combination of how well it believes they can answer the question and how good
of a match they are for the asker. The Routing Engine returns a ranked list of
Routing Suggestions to the Conversation Manager, which then contacts the poten-
tial answerers one by one, or a few at a time, depending upon a Routing Policy
and asks them if they would like to answer the question, until a satisfactory answer
is found. The Conversation Manager then forwards this answer along to the asker,
and allows the asker and answerer to exchange follow up messages if they choose.
Dept. of Computer Science & Engg. 11 SIMAT, Vavanoor

16. Chapter 6
How Aardvark Works?
6.1 Work Model
The core functionality of Aardvark is a statistical model which does the job of routing
questions to potential answerers, because the answer and the question generator both
are a fine end users. Aardvark uses a network variant of what has been called an
aspect model[5], that has two primary features. First, it associates an unobserved
class(i.e the question and topic being asked) variable t ∈ T with each observation
(i.e., the successful answer of question q by user ui). This probability is to find the
relevance of the user ui in answering the topic t. In other words it can be stated
that the probability p(ui|q) that user i will successfully answer question q depends on
whether q is about the topics t in which ui has expertise and it is query dependent:
p(ui, q) =
t∈T
p(ui|t)p(t|q) (6.1)
The second main feature of the model is that it defines a query-independent prob-
ability of success for each potential asker/answerer pair (ui, uj), based upon their
degree of social connectedness, the intimacy and profile similarity between the asker
and the answerer. This is indirectly referencing to the bondage between two friends
for solving each others problem though the answerer may not be expertise in an-
swering the topic. In other words, we define a probability p(ui|uj) that user ui will
deliver a satisfying answer, i.e the quality of the answer to the topic, to user uj ,
regardless of the question. We then define the scoring function s(ui, uj, q) as the
composition of the two probabilities.
s(ui, uj, q) = p(ui, uj).p(ui, q) = p(ui, uj).
t∈T
p(ui|t)p(t|q) (6.2)
The main goal in the ranking problem is: given a question q from user uj, return a
ranked list of users ui ∈ U that maximizes s(ui, uj, q).
It is very interesting to note that the scoring function is composed of a query depen-
dent relevance score p(ui|q) and a query-independent quality score p(ui|uj). This
bears similarity to the ranking functions of traditional text-based search engines
such as Google[1]. The difference is that unlike quality scores like Page Rank[6],
Aardvark’s quality score aims to measure intimacy between the askers and the an-
swerers rather than authority, of a document. And unlike the relevance scores in
12

17. Aardvark : Large - Scale Social Search Engine
hypertext-based search engines, Aardvark’s relevance score aims to measure a user’s
potential to answer a query, rather than a document’s existing capability to answer
a query. Computationally, this scoring function has a number of advantages. It al-
lows real-time routing because it pushes much of the computation off line. The only
component probability that needs to be computed at query time is p(t|q). Comput-
ing p(t|q) is equivalent to assigning topics to a question - in Aardvark it is done by
running a probabilistic classifier on the question at query time. The distribution
p(ui|t) assigns users to topics, and the distribution p(ui|uj) defines the Aardvark
Social Graph. Both of these are computed by the Indexer at sign up time, and then
updated continuously in the background as users answer questions and get feedback.
The component multiplications and sorting are also done at query time, but these
are easily parallelize able, as the index is shared by user.
6.2 Social Crawling
The primary goal of any search engine is to intake the query, study the query to
understand the user’s need and then based on the query generate the solution or
solutions to the user. A Knowledge Base (KB)[11] is a technology used to store
complex structured and unstructured information used by a computer system. A
comprehensive Knowledge Base is important for search engines as query distribu-
tions tend to have a long tail [7]. Long tail in the sense the string length of the
query is longer. A Web crawler is an Internet bot that systematically browses the
World Wide Web, typically for the purpose of Web indexing. Web search engines
and some other sites use Web crawling software to update their web content or in-
dexes of others sites’ web content. Web crawlers can copy all the pages they visit for
later processing by a search engine that indexes the downloaded pages so that users
can search them much more quickly. In text-based search engines, the crawling pro-
cess is achieved by large scale crawlers and thoughtful crawl policies. In Aardvark,
the knowledge base consists of people rather than documents, so the methods for
acquiring and expanding a comprehensive knowledge base are quite different.
With Aardvark, the more active users there are in network, the more potential
answerers there are, and therefore the more comprehensive the coverage. More im-
portantly, because Aardvark looks for answerers primarily within a user’s extended
social network, the denser the network, the larger the effective knowledge base. This
suggests that the strategy for increasing the knowledge base of Aardvark crucially
involves creating a good experience for users so that they remain active and are
inclined to invite their friends.
Given a set of active users on Aardvark, the effective breadth of the Aardvark
knowledge base depends upon designing interfaces and algorithms that can collect
and learn.
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18. Aardvark : Large - Scale Social Search Engine
6.3 Indexing People
Indexing is the process of allocating the actual or the most suitable resource to the
request. The central technical challenge in Aardvark is selecting the right user to
answer a given question originated from another user. In order to do this, the two
main things Aardvark needs to learn about each user ui are:
1. The topics t that the user ui might be able to answer questions about
psmoothed(t|ui)
2. The users uj to whom which user ui is connected p(ui|uj).
Now moving to the first part which is used to describe about the concept of ”topics”
known to a user ui. Aardvark computes the distribution p(t|ui) of topics known by
user ui from the following sources of information:
• Users are prompted to provide at least three topics which they believe they
have expertise about.
• Friends of a user (and the person who invited a user) are encouraged to provide
a few topics that they trust the user’s opinion about.
• Aardvark parses out topics from users existing on line profiles (e.g., Facebook
profile pages, if provided). For such pages with a known structure, a simple
Topic Parsing algorithm uses regular expressions which were manually devised
for specific fields in the pages, based upon their performance on test data. Like
for example if a user takes too many photographs and has joined various groups
of photography then the Aardvark can understand that user is an expertise in
the topics related to photography.
• Aardvark automatically extracts topics from unstructured text on users ex-
isting on line homepage or blogs if provided. For unstructured text, a linear
SVM, Support Vector Machine[17], is a supervised learning models with as-
sociated learning algorithms that analyze data and recognize patterns, used
for classification and regression analysis. SVM identifies the general subject
area of the text, while an ad-hoc named entity extractor is run to extract
more specific topics, scaled by a variant tf-idf score.tf-idf[18] is a numerical
statistic that is intended to reflect how important a word is to a document in
a collection or corpus.
• Aardvark automatically extracts topics from users status message updates
(e.g., Twitter messages, Facebook news feed items, IM status messages, etc.)
and from the messages they send to other users on Aardvark.
The motivation for using these latter sources of profile topic information is a simple
one: if you want to be able to predict what kind of content a user will generate (i.e.,
p(t|ui)), first examine the content they have generated in the past. In this spirit,
Aardvark uses web content not as a source of existing answers about a topic, but
rather, as an indicator of the topics about which a user is likely able to give new
answers on demand.
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19. Aardvark : Large - Scale Social Search Engine
In simple terms this process involves modeling a user as a content generator, with
probabilities indicating the likelihood he/she will likely respond to questions about
given topics. Each topic in a user profile has an associated score, depending upon
the confidence appropriate to the source of the topic. In addition, Aardvark learns
over time which topics not to send a user questions about by keeping track of cases
when the user:
1. Explicitly ”mutes” a topic
2. Declines to answer questions about a topic when given the opportunity
3. Receives negative feedback on his answer about the topic from another user.
Periodically, Aardvark will run a topic strengthening algorithm. The essential idea
or purpose of this algorithm is that ” if a user has expertise in a topic and most of
his friends also have some expertise in that topic, we have more confidence in that
user’s level of expertise than if he were alone in his group with knowledge in that
area”. Mathematically, for some user ui, has a group of friends U, and user knows
some topic t, if p(t|ui) = 0, then s(t|ui) = p(t|ui)+γ u∈U p(t|u), where γ is a small
constant. The s values are then renormalized to form probabilities.
Aardvark then runs two smoothing algorithms over the list of the topics known
to the user. The purpose of these two algorithms are to record the possibility that
the user may be able to answer questions about additional topics which are not ex-
plicitly recorded in her profile. The first algorithm uses basic collaborative filtering
techniques on topics i.e., based on users with similar topics of the user. For example
if a person knows about photography then he also might knows about the cameras
too. The second algorithm uses semantic similarity. Semantic similarity [19] or se-
mantic relatedness is a metric defined over a set of documents or terms, where the
idea of distance between them is based on the likeness of their meaning or semantic
content as opposed to similarity which can be estimated regarding their syntactical
representation
Once all of these bootstrap, extraction, and smoothing methods are applied, we
have a list of topics and scores for a given user. Normalizing these topic scores so
that t∈T p(t|ui) = 1, we have a probability distribution for topics known by user
ui. Using Bayes Law, we compute for each topic and user:
p(ui|t) =
p(t|ui)p(ui)
p(t)
(6.3)
using a uniform distribution for p(ui) and observed topic frequencies for p(t). Aard-
vark collects these probabilities p(ui|t) indexed by topic into the Inverted Index,
which allows for easy lookup when a question comes in.
Aardvark computes the connectedness between users p(ui|uj) in a number of ways.
While social proximity is very important here, we also take into account similarities
in demographics and behavior. The factors considered here include:
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20. Aardvark : Large - Scale Social Search Engine
• Social connection (common friends and affiliations)
• Demographic similarity
• Profile similarity (e.g., common favorite movies)
• Vocabulary match (e.g., IM shortcuts)
• Chattiness match (frequency of follow-up messages)
• Verbosity match (the average length of messages)
• Politeness match (e.g., use of Thanks!)
• Speed match (responsiveness to other users)
Connection strengths between people are computed using a weighted cosine similar-
ity over this feature set, normalized so that ui∈U p(ui|uj) = 1, and stored in the
Social Graph for quick access at query time. Both the distributions p(ui|uj) in the
Social Graph and p(t|ui) in the Inverted Index are continuously updated as users
interact with one another on Aardvark.
6.4 Question Analyzer
The purpose of the Question Analyzer is to determine a scored list of topics p(t|q)
for each question q representing the semantic subject matter of the question. In
simple terms for a question q , a list of topics are generated which can give the
solution to the question q. This is the only probability distribution in equation 6.2
that is computed at query time.
It is important to note that in a social search system, the requirement for a Ques-
tion Analyzer is only to be able to understand the query sufficiently for routing it
to a likely answerer. This is a considerably simpler task than the challenge facing
an ideal web search engine, which must attempt to determine exactly what piece
of information the user is seeking (i.e., given that the searcher must translate her
information need into search keywords), and to evaluate whether a given web page
contains that piece of information. By contrast, in a social search system, it is the
human answer who has the responsibility for determining the relevance of an answer
to a question and that is a function which human intelligence is extremely well-
suited to perform! Here the only process to be done is the process of match making
two users. While the rest of the process of generating the solution is all with the
answerer. Due to this unique feature there is partial complexity in implementing.
The asker can express his information need in natural language, and the human
answerer can simply use her natural understanding of the language of the question,
of its tone of voice, sense of urgency, sophistication or formality, and so forth, to
determine what information is suitable to include in a response. Thus, the role of
the Question Analyzer in a social search system is simply to learn enough about the
question that it may be sent to appropriately interested and knowledgeable human
answerers.
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21. Aardvark : Large - Scale Social Search Engine
The questions inputed into Aardvark can be classified for better understanding by
following ways[2]:
• NonQuestionClassifier
• InappropriateQuestionClassifier
• TrivialQuestionClassifier
• LocationSensitiveClassifier
A NonQuestionClassifier determines if the input is not actually a question (e.g., is
it a misdirected message, a sequence of keywords, etc.); if so, the user is asked to
submit a new question.
An InappropriateQuestionClassifier determines if the input is obscene, commercial
spam, or otherwise inappropriate content for a public question-answering commu-
nity; if so, the user is warned and asked to submit a new question.
A TrivialQuestionClassifier determines if the input is a simple factual question which
can be easily answered by existing common services (e.g., What time is it now?,
What is the weather?, etc.); if so, the user is offered an automatically generated
answer resulting from traditional web search.
A LocationSensitiveClassifier determines if the input is a question which requires
knowledge of a particular location, usually in addition to specific topical knowledge
(e.g., Whats a great sushi restaurant in Austin, TX?); if so, the relevant location is
determined and passed along to the Routing Engine with the question.
After analyzing and screening a question the next process is to map this question to
relevant topics and from there to the users with knowledge of the topic. The list of
topics relevant to a question is produced by merging the output of several distinct
TopicMapper algorithms[2]:
• KeywordMatchTopicMapper
• TaxonomyTopicMapper
• SalientTermTopicMapper
• UserTagTopicMapper
A KeywordMatchTopicMapper passes any terms in the question which are string
matches with user profile topics through a classifier which is trained to determine
whether a given match is likely to be semantically significant or misleading.
A TaxonomyTopicMapper classifies the question text into a taxonomy of roughly
3000 popular question topics, using an SVM trained on an annotated text of several
millions questions.
A SalientTermTopicMapper extracts salient phrases from the question using a noun-
phrase chunker and a tf-idf based measurer of importance in a document and finds
semantically similar user topics.
A UserTagTopicMapper takes any user tags provided by the asker (or by any would-
be answerers), and maps these to semantically-similar user topics.
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22. Aardvark : Large - Scale Social Search Engine
At present, the output distributions of these classifiers are combined by weighted
linear combination[8]. The Aardvark TopicMapper algorithms are continuously eval-
uated by manual scoring on random samples of 1000 questions. The topics used for
selecting candidate answerers, as well as a much larger list of possibly relevant topics,
are assigned scores by two human judges, with a third judge adjudicating disagree-
ments. For the current algorithms on the current sample of questions, this process
yields overall scores of 89% precision and 84% recall of relevant topics. In other
words, 9 out of 10 times, Aardvark will be able to route a question to someone with
relevant topics in his her profile; and Aardvark will identify 5 out of every 6 possibly
relevant answerers for each question based upon their topics.
6.5 Ranking Algorithm
Ranking in Aardvark is done by the Routing Engine, which determines an ordered
list of users (or candidate answerers) who should be contacted to answer a question.
To this algorithm the inputs are the details of the asker of the question and the
information about the question derived by the Question Analyzer. The core ranking
function is described by equation 6.2; essentially, the Routing Engine can be seen
as computing equation 6.2 for all candidate answerers, sorting, and doing some
post processing. The main factors that determine this ranking of users are Topic
Expertise p(ui|q), Connectedness p(ui|uj), and Availability.
• Topic Expertise: First, the Routing Engine finds the subset of users who are
semantic matches to the question being asked here. Those users whose profile
topics indicate expertise relevant to the topics which the question is about.
Those users whose profile topics are closer match to the question’s topics are
given higher rank. For the questions which are location-sensitive (as defined
above), then only those users with matching locations in their profiles are
considered for answering.
• Connectedness: Second, the Routing Engine scores each user according to
the degree to which he/she herself - as a person, independently of her topical
expertise - is a good match for the asker for this information query. This part
is more like giving score value to those users who gave the correct and the
most relevant answer to the question being asked. The goal of this scoring is
to optimize the degree to which the asker and the answerer feel more friendship
and trust, arising from their sense of connection and similarity, and meet each
other’s expectations for conversational behavior in the interaction.
• Availability: Third, the Routing Engine prioritizes candidate answerers in
such a way so as to optimize the chances that the present question will be
answered, while also preserving the available set of answerers (i.e., the quantity
of answering resource in the system) as much as possible by spreading out the
answering load across the user base. This involves factors such as prioritizing
users who are currently online (e.g., via IM presence data, iPhone usage, etc.),
who are historically active at the present time-of-day, and who have not been
contacted recently with a request to answer a question.
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23. Aardvark : Large - Scale Social Search Engine
Given this ordered list of candidate answerers, the Routing Engine then filters out
users who should not be contacted, according to Aardvarks guidelines for preserving
a high-quality user experience. These filters operate largely as a set of rules:
• Do not contact users who prefer to not be contacted at the present time of
day
• Do not contact users who have recently been contacted as many times as their
contact frequency settings permit etc. Since this is all done at query time,
and the set of candidate answerers can potentially be very large, it is useful
to note that this process is parallelize able.
Each Shard[20] in the Index computes its own ranking for the users in that shard,
and sends the top users to the Routing Engine. Shard is a horizontal partition of
data in a database or search engine. Each individual partition is referred to as a
shard or database shard. Each shard is held on a separate database server instance,
to spread load. This is scalable as the user knowledge base grows, since as more
users are added, more shards can be added.
The list of candidate answerers who survive this filtering process are returned to
the Conversation Manager. The Conversation Manager then proceeds with opening
channels to each of them, serially, inquiring whether they would like to answer the
present question; and iterating until an answer is provided and returned to the asker.
6.6 User Interface
Since social search is modeled after the real-world process of asking questions to
friends, the various user interfaces for Aardvark are built on top of the existing com-
munication channels that people use to ask questions to their friends: IM, email,
SMS, iPhone, Twitter, and Web-based messaging. Experiments were also done us-
ing actual voice input from phones, but this is not live in the current Aardvark
production system.
In its simplest form, the user interface for asking a question on Aardvark is any
kind of text input mechanism, along with a mechanism for displaying textual mes-
sages returned from Aardvark. This kind of very lightweight interface is important
for making the search service available anywhere, especially now that mobile device
usage has increased across in the globe.
However, Aardvark is most powerful when used through a chat-like interface that
enables ongoing conversational interaction. A private 1-to-1 conversation creates
an intimacy which encourages both honesty and freedom within the constraints of
real-world social norms. An answering forums where there is a public audience can
both inhibit potential answerers [14] or motivate public performance rather than
authentic answering behavior[15] is a key criteria when the user interface is being
designed. Further, in a real time conversation, it is possible for an answerer to re-
quest clarifying information from the asker about her question, or for the asker to
follow-up with further reactions or inquiries to the answerer.
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24. Aardvark : Large - Scale Social Search Engine
There are two main interaction flows, in the sense communication flow between
two users, available in Aardvark for answering a question. The primary flow in-
volves Aardvark sending a user a message (over IM, email, etc.), asking if she would
like to answer a question: for example, ”You there? A friend from the Stanford
group has a question about *search engine optimization* that I think you might be
able to answer.”. The example of Aardvark response are shown in the Figure 6.2.
If the user responds affirmatively, Aardvark relays the question as well as the name
of the questioner. The user may then simply type an answer the question, type in
a friend’s name or email address to refer it to someone else who might answer, or
simply ”pass” on this request. Keeping the request and reply messages more user
friendly and trustworthy will make the attention of the user stick to the user itself.
A key benefit of this interaction model is that the available set of potential an-
swerers is not just whatever users happen to be visiting a bulletin board at the time
a question is posted, but rather, the entire set of users that Aardvark has contact
information for. Because this kind of reaching out to users has the potential to be-
come an unwelcome interruption if it happens too frequently, Aardvark sends such
requests for answers usually less than once a day to a given user (and users can
easily change their contact settings, specifying preferred frequency and time-of-day
for such requests). Further, users can ask Aardvark why they were selected for a
particular question, and be given the option to easily change their profile if they do
not want such questions in the future. This is very much like the real-world model
of social information sharing: the person asking a question, or the intermediary in
Aardvark’s role, is careful not to impose too much upon a possible answerer. The
ability to reach out to an extended network beyond a user’s immediate friendships,
without imposing too frequently on that network, provides a key differentiating ex-
perience from simply posting questions to one’s Twitter or Facebook status message.
A secondary flow of answering questions is more similar to traditional bulletin-
board style interactions: a user sends a message to Aardvark (e.g., ”try”) or visits
the Answering tab of Aardvark website or iPhone application, and Aardvark shows
the user a recent question from her network which has not yet been answered and
is related to her profile topics. This mode involves the user initiating the exchange
when she is in the mood to try to answer a question; as such, it has the benefit of an
eager potential answerer - but as the only mode of answering it does not effectively
tap into the full diversity of the user base (since most users do not initiate these
episodes). This is an important point: while almost everyone is happy to answer
questions to help their friends or people they are connected to, not everyone goes
out of their way to do so. This willingness to be helpful persists because when users
do answer questions, they report that it is a very gratifying experience: they have
been selected by Aardvark because of their expertise, they were able to help someone
who had a need in the moment, and they are frequently thanked for their help by
the asker.
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25. Aardvark : Large - Scale Social Search Engine
Figure 6.1: Aardvark in iPhone
In order to play the role of intermediary in an ongoing conversation, Aardvark
must have some basic conversational intelligence in order to understand where to
direct messages from a user: is a given message a new question, a continuation of
a previous question, an answer to an earlier question, or a command to Aardvark?
The details of how the Conversation Manager manages these complications and dis-
ambiguates user messages are not essential so they are not elaborated here; but the
basic approach is to use a state machine to model the discourse context.
In all of the interfaces, wrappers around the messages from another user include
information about the user that can facilitate trust: the user’s Real Name name
tag, with their name, age, gender, and location; the social connection between you
and the user (e.g., Your friend on Facebook, A friend of your friend Marshall Smith,
You are both in the Stanford group, etc.); a selection of topics the user has exper-
tise in; and summary statistics of the user’s activity on Aardvark (e.g., number of
questions recently asked or answered).
Finally, it is important throughout all of the above interactions that Aardvark main-
tains a tone of voice which is friendly, polite, and appreciative. A social search engine
depends upon the goodwill and interest of its users, so it is important to demon-
strate the kind of (linguistic) behavior that can encourage these sentiments, in order
to set a good example for users to adopt. Indeed, in user interviews, users often
express their desire to have examples of how to speak or behave socially when using
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26. Aardvark : Large - Scale Social Search Engine
Figure 6.2: Aardvark’s working using a pictorial description
Aardvark; since it is a novel paradigm, users do not immediately realize that they
can behave in the same ways they would in a comparable real world situation of
asking for help and offering assistance. All of the language that Aardvark uses is
intended both to be a communication mechanism between Aardvark and the user
and an example of how to interact with Aardvark. Overall, a large body of research
[9][10][12][13] shows that when you provide a 1-1 communication channel, use real
identities rather than pseudonyms, facilitate interactions between existing real-world
relationships, and consistently provide examples of how to behave, users in an on-
line community will behave in a manner that is far more authentic and helpful than
pseudonymous multi casting environments with no moderators. The design of the
Aardvarks UI has been carefully crafted around these principles.
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27. Aardvark : Large - Scale Social Search Engine
6.7 Example
The better performance and evaluation of any algorithm can only be understood
using an example. This section is illustrating a working model of the Aardvark
network. Before preceding further moving to a recap of what said early it is very
clear that the Aardvark is not an ordinary search engine. Its more complex and
more natural language based search engine which generates real time results for a
query. The figure 6.4 shows a random sample of questions asked on Aardvark when
the Aardvark was officially launched in the world’s computer network. From the
figure itself it is very clear that not even a single query from this set of query can be
answered by the traditional text based search engine. The main reason is that these
are subjective questions and can only be answered by a human not a machine or a
document. The below Figure 6.3[2] shows the mostly asked topics on Aardvark.
Figure 6.3: Frequent Questions asked in Aardvark
Figure 6.4: A random set of queries from Aardvark
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28. Aardvark : Large - Scale Social Search Engine
In Example 1 as in Figure: 6.5, Aardvark opened 3 channels(conversation) with
candidate answerers, which yielded 1 answer. An interesting aspect of this example
is that the asker and the answerer in fact were already friends , though only listed
asfriends-of-friends in their online social graphs; and they had a quick back-and-
forth chat through Aardvark.
Figure 6.5: Example 1
In Example 2 as in Figure: 6.6, Aardvark opened 4 channels with candidate answer-
ers, which yielded two answers. For this question, the referral feature was useful:
one of the candidate answerers was unable to answer, but referred the question along
to a friend to answer; this enables Aardvark to tap into the knowledge of not just its
current user base, but also the knowledge of everyone that the current users know.
The asker wanted more choices after the first answer, and resubmitted the question
to get another recommendation, which came from a user whose profile topics related
to business and finance (in addition to dining).
In Example 3 as in Figure: 6.7, Aardvark opened 10 channels with candidate an-
swerers, yielding 3 answers. The first answer came from someone with only a distant
social connection to the asker; the second answer came from a coworker; and the
third answer came from a friend-of-friend-of-friend. The third answer, which is
the most detailed, came from a user who has topics in his profile related to both
restaurants and dating.
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29. Aardvark : Large - Scale Social Search Engine
Figure 6.6: Example 2
Figure 6.7: Example 3
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30. Chapter 7
Future Works
From the previous chapters it will be very clear how this Search Engine work, what
are the main criteria and various examples to about how query is being processed
by this search engine. Aardvark was originally developed by The Mechanical Zoo, a
San Francisco-based startup founded in 2007 by Max Ventilla, Nathan Stoll (both
former Google employees), Damon Horowitz and Rob Spiro[21].A prototype ver-
sion of Aardvark was launched in early 2008[with an alpha launch in October 2008.
Aardvark was released to the public in March 2009, although initially new users had
to be invited by existing users. The company did not release usage statistics.
Later Google acquired Aardvark for $50 million on February 11, 2010. The Aard-
vark sucessfully completed two years of development in Google. As shown in the
graph Figure 7.1 the number of Aardvark users were increasing which shows a clear
result of the success of Aardvark. But in September 2011, Google announced
it would discontinue a number of its products, including Aardvark.
Figure 7.1: Increasing number of users in Aardvark.
26

31. Aardvark : Large - Scale Social Search Engine
It was very mysterious because without specifying any kind of reasons the Google
shut down the project. No Flaws or Error was found in the Search Engine but still
it was declined.
As mentioned earlier there is no other application launched or yet launched based
on the concept of Social search engine. Yet there is a new kind of website based
search engine named as Social Searcher. This is a social based search engine but
it is not working as per the concepts of Aardvark. Its retrieving information about
a query not by proper meaning but by taking the keyword into consideration.
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32. Chapter 8
Conclusion
Here a detail explanation and study was made on the concept of Social Search En-
gine and Aardvark. From the detailed study it can be inferred that the concept of
Social based search can revolutionize in the field of Search Engine. Currently there
are several search engines available in market but all of them are derived from the
concept of text based search engine.
Rather than generating a predefined output from a trillions of output, which is
common for every user a new model is designed here. Here when a user queries
something keep the thoughts and the need which is being projected in the query as
the key things to generate an output is achieved here. For the first time humans’
thoughts is becoming one of the dimensions in the field of Information Retrieval.
After the detail study it was learned that the Social Based Search Engine can enrich
the Information Content residing in the World Wide Web. If a provision was pro-
vided to incorporate both the Social Search Engine and Hypertextual Based Search
Engine then a creation of highly informative source can be developed. Along with
that if a process of extracting information from the subjective questions and answers
can make the system more smarter.
28

33. Bibliography
[1] S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search
engine. In WWW Conference, 1998.
[2] D. Horowitz and Sepandar D. Kamvar, The Anatomy of a Large-Scale Social
Search EngineDamon Horowitz. In WWW Coference, 2010
[3] ”Google Acquires Aardvark For 50 million (Confirmed)”. TechCrunch.
techcrunch.com. Archived from the original on February 13, 2010. Retrieved
February 12, 2010.
[4] ”Google Acquires Aardvark”. Official Google Blog. Google. Archived from the
original on February 14, 2010. Retrieved February 12, 2010.
[5] T. Hofmann. ”Probabilistic latent semantic indexing”. In SIGIR, 1999.
[6] L. Page, S. Brin, R. Motwani, and T. Winograd. ”The PageRank citation rank-
ing: Bringing order to the Web”. Stanford University Technical Report, 1998.
[7] B. J. Jansen, A. Spink, and T. Sarcevic. ”Real life, real users, and real needs:
a study and analysis of user queries on the web”. Information Processing and
Management, 2000.
[8] D. Klein, K. Toutanova, H. T. Ilhan, S. D. Kamvar, and C. D. Man-
ning.”Combining heterogeneous classifiers for word-sense disambiguation”. In
SENSEVAL, 2002.
[9] H. Bechar-Israeli. From <Bonehead> to <cLoNehEAd>: Nicknames, Play, and
Identity on Internet Relay Chat. Journal of Computer-Mediated Communica-
tion, 1995.
[10] A. R. Dennis and S. T. Kinney. ”Testing media richness theory in the new me-
dia: The effects of cues, feedback, and task equivocality”. Information Systems
Research, 1998.
[11] Green, Cordell, D. Luckham, R. Balzer, T. Cheatham, C. Rich (1986). ”Report
on a knowledge-based software assistant”. Readings in artificial intelligence and
software engineering (M. Kaufmann): Page Number: 377 - 428.
[12] J. Donath. ”Identity and deception in the virtual community”. Communities in
Cyberspace Conference, 1998.
[13] L. Sproull and S. Kiesler. ”Computers, networks, and work. In Global Net-
works”, Computers and International Communication. MIT Press, 1993.
29

34. Aardvark : Large - Scale Social Search Engine
[14] M. R. Morris, J. Teevan, and K. Panovich. ”What do people ask their social
networks, and why? A Survey study of status message Q&A behavior”. In CHI,
2010.
[15] M. Wesch. ”An anthropological introduction to YouTube”. Library of Congress
Summit, 2008.
[16] C. McCarthy. ”Facebook: One Social Graph to Rule Them All?”. CBS News.
Web July 11, 2010.
[17] R. Cuingnet, C. Rosso, M. Chupin, S. Lehricy, D. Dormont, H. Benali, Y.
Samson and O. Colliot, ”Spatial regularization of SVM for the detection of
diffusion alterations associated with stroke outcome”, Medical Image Analysis
Conference, 2011.
[18] Rajaraman, A. Ullman; ”Data Mining Mining of Massive Datasets”, WWW
Conference, 2011
[19] Harispe S., Ranwez S. Janaqi S., Montmain J. ”Semantic Measures for the Com-
parison of Units of Language, Concepts or Entities from Text and Knowledge
Base Analysis”, Arxiv Corporation, August 2013.
[20] R. Roy. ”Shard - A Database Design”, International Conference on Database
Study, Geneva, July 28, 2008.
[21] D. Horowitz.”Mechanical Zoo Gets $6 Million To Build Aardvark Social Search
Product”. TechCrunch. techcrunch.com.Web. March 14, 2009.
Dept. of Computer Science & Engg. 30 SIMAT, Vavanoor