=Paper=
{{Paper
|id=None
|storemode=property
|title=A System for Perspective-Aware Search
|pdfUrl=https://ceur-ws.org/Vol-1033/paper9.pdf
|volume=Vol-1033
|dblpUrl=https://dblp.org/rec/conf/eurohcir/QureshiYOPT13
}}
==A System for Perspective-Aware Search==
https://ceur-ws.org/Vol-1033/paper9.pdf
A System for Perspective-Aware Search
M. Atif Qureshi*† , Arjumand Younus*† , Colm O’Riordan* , Gabriella Pasi , Nasir
Touheed†
*
Computational Intelligence Research Group, Information Technology, National University of Ireland,
Galway, Ireland
Information Retrieval Lab, Informatics, Systems and Communication, University of Milan Bicocca,
Milan, Italy
†
Web Science Research Group, Faculty of Computer Science, Institute of Business Administration,
Karachi, Pakistan
muhammad.qureshi,arjumand.younus@nuigalway.ie, colm.oriordan@nuigalway.ie,
pasi@disco.unimib.it, ntouheed@iba.edu.pk
ABSTRACT terrorism in most of the cases. This prompts the user
Traditional search engines fail to capture the notion of “per- to explicitly evaluate how much Islam is related to ter-
spective” in their search results and at times present the re- rorism in the returned search results.
sults skewed towards a particular topic. Under most of these
• Consider the case of a user who wishes to find out
cases even query reformulation fails to retrieve desired search
about roles and rights of women in Islam but the search
results and the underlying reason for such failure is often
engine returns articles that contain a high amount of
the bias within the document collection itself (e.g., news ar-
terms highlighting oppression against women instead
ticles). A perspective-aware search interface enabling users
of women rights and roles. In this case the user is
to look into search results for some “perspective” terms may
prompted to check the correlation between women and
be of great use for certain information needs. In this paper
oppression within the search results that have been
we describe such a system.
returned.
Categories and Subject Descriptors Note that the perspective given by most search results
H.1.2 [User/Machine Systems]: Human factors; H.3.3 (Islam in our motivating example (1) and oppression in our
[Information Search and Retrieval]: Search process motivating example (2)) may or may not be aligned with
the user’s query intent. In case of search results not being
aligned with his/her query intent he/she may be interested
General Terms in observing the amount of perspective tendencies in various
Human Factors, Performance news reports.
This paper proposes the concept of a “perspective-aware”
Keywords search interface that enables the user to explicitly analyse
search results for information from a particular perspec-
Perspective, Wikipedia, Bias
tive with respect to an issued query. To the best of our
knowledge, previous research within Human-Computer In-
1. INTRODUCTION AND RELATED WORK teraction and Information Retrieval has failed to capture
It is often the case that when using a search engine for in- the notion of “perspective” within the information retrieval
formation seeking users have an underlying intent [1]. Tra- process. Early research related to Interactive Information
ditional search interfaces fail to capture the user intent for Retrieval by Belkin [2] and Ingwersen [6] suggests the inte-
certain topics and at times return results that may be skewed gration of cognitive aspects within the information retrieval
towards a certain perspective. Here, perspective as defined process: in line with this suggestion we argue for incorporat-
by the Oxford Dictionary refers to a “point of view”1 within ing the essential cognitive element of “perspectives”2 within
the search results that may or may not be something what the search engine interface.
user is looking for. We explain further through the following Recently the information retrieval community has turned
motivating examples: attention to diversification of search results which aims to
tackle the issue of query ambiguity on the user side [8]. How-
• Consider the case of a user who wishes to find more
ever, even when formulating a non-ambiguous query users
about a certain event (say, a bomb attack in a certain
may have an intent that influences the perspective from
region). The search results returned contain a ma-
which the query terms can be interpreted in a text; in case of
jority of news reports blaming Islam relating it with
2
1 According to Wikipedia the definition of perspective states
This may also be seen as topic drifts within a document. the following: “Perspective in theory of cognition is the
Presented at EuroHCIR2013. Copyright c 2013 for the individual pa- choice of a context or a reference (or the result of this choice)
pers by the papers’ authors. Copying permitted only for pri- from which to sense, categorize, measure or codify experi-
vate and academic purposes. This volume is published and copy- ence, cohesively forming a coherent belief, typically for com-
righted by its editors.. paring with another.”
Figure 1: Entry Point of Perspective-Aware Search Interface
the entry point of the interface which resembles the standard
type-keywords-in-entry-form interface with the augmenta-
tion of an additional input text box for entry of perspective
terms.
The underlying perspective detection algorithm makes use
of the encyclopedic structure in Wikipedia; more specifi-
cally the knowledge encoded in Wikipedia’s graph structure
is utilized for the discovery of various perspectives in docu-
ments returned by the search engine. Wikipedia is organized
into categories in a taxonomy-like3 structure (see Figure 2).
Each Wikipedia category can have an arbitrary number of
subcategories as well as being mentioned inside an arbitrary
number of supercategories (e.g., category C4 in Figure 1 is
a subcategory of C2 and C3 , and a supercategory of C5 , C6
and C7 .) Furthermore, in Wikipedia each article can belong
to an arbitrary number of categories, where each category is
Figure 2: Wikipedia Category Graph Structure along a kind of semantic tag for that article [11]. As an example,
with Wikipedia Articles in Figure 2, article A1 belongs to categories C1 and C10 ,
article A2 belongs to categories C3 and C4 , while article A3
belongs to categories C4 and C7 . It can be seen that the
perspective mismatch between the user intent and the doc- articles and the Wikipedia Category Graph are interlinked
uments returned in first positions by a search engine, users and our system makes use of these interlinks for the detec-
may find the retrieved results annoying or subjective to a tion of a certain perspective within a document retrieved by
non-agreed perspective [7]. One may argue that a query re- the search engine.
formulation technique could be employed to tackle this prob-
lem [5]; e.g. considering the motivating example (2), the user
could issue a reformulated query such as “roles and rights of
2.1 Underlying Algorithm
women in islam”. However, for some topics query reformu- The underlying perspective detection algorithm within our
lation may fail to retrieve the desired search results, and the system requires the perspective term/phrase to match the
underlying reason for such failure is often the bias within the title of a Wikipedia article. This may seem to impose a cog-
document collection itself (e.g., news articles) [10]. Under nitive load on the user at search time. However, this is not
such a scenario it would be interesting to provide a search the case: as shown in Figure 3 the entered text automati-
interface that would enable the users to look into the search cally turns green when a certain user-specified perspective
results for some “perspective” terms and we describe such a term matches the title of a Wikipedia article, and symmet-
system in this paper. rically the entered text automatically turns red in case of a
mismatch.
Once the perspective term is entered correctly the system
2. PERSPECTIVE-AWARE SEARCH INTER- fetches the Wikipedia article corresponding to the perspec-
FACE AND IMPLEMENTATION DETAILS tive term referred to as Seed Perspective Article (PAseed )
This section presents the essential details of the proposed along with the categories to which it belongs and we use
perspective-aware search interface along with the underlying
implementation details. We keep the interface as simple as 3
We say taxonomy-like because it is not strictly hierarchi-
possible on account of research suggesting users’ reluctance cal due to the presence of cycles in the Wikipedia category
in switching from a simple search form [3]. Figure 1 shows graph.
Figure 3: Automatic Text Color Changing to Test Match of Perspective Term with Wikipedia Article Title
PC0 4 to refer to these categories. After fetching of Wikipedia “terrorism” is shown in Figure 4. As evident from the top
categories in PC0 , the system retrieves sub-categories of PC0 search result, there is a high perspective of terrorism within
until depth 2 i.e., PC1 and PC2 5 and collectively these cat- the returned document and perspective terms that our al-
egories related to PAseed are referred to as PC (where PC gorithm fetches are as follows: a) the war on terrorism, b)
is union of PC0 , PC1 and PC2 .). Next, the set of all ar- ayman al zawahiri, and c) osama bin laden.
ticles within the Wikipedia category set PC is retrieved
and we refer to this set as Expanded Perspective Article Set 3. DISCUSSION
(PAexpanded ). The system then retrieves all categories as-
There have been many efforts in the information retrieval
sociated with the set PAexpanded which we refer to as WC ;
research to present to users information regarding the rela-
note that PC is a subset of WC. Finally, the intersection be-
tionship between the query and the answer set and the query
tween PC and WC is retrieved which is a set of categories
and document collection. Capturing this information during
representative of the domain of the perspective term origi-
the retrieval process provides the user with much valuable in-
nally input by the user, we refer to this set of representative
formation (e.g. whether a term is overly specific, or whether
categories as RC.
a term is ambiguous etc.). Various attempts have been made
After building the Wikipedia category sets as defined above6
to tackle this problem, ranging from the definition of snip-
i.e., PC, RC and WC we match variable-length n-grams
pets to the definition of approaches to cluster search results
within a document with articles in the set PAexpanded , and
(Clusty.com), to the presentation of diversified search results
we check for cardinality of RC and WC. The cardinality
in the first position of the ranked list offered to the users.
scores along with n-gram frequencies are used to compute a
Recently there has been a resurgence of interest in defining
perspective score for each document.
visualization techniques of search results that offer an effec-
tive and more informative alternative to usual and scarcely
2.2 Search Results Presentation informative ranked lists. Pioneer visualization systems are
The perspective scores computed in section 2.1 are dis- represented by Tilebar [4], and Infocyrstal [9], and these
played within the search results, and based on the perspec- attempts have been aimed to provide the user with more
tive score a document receives , we define four levels of information than that provided by the traditional ranked
perspective adherence as follows: a) High, b) Medium, c) list.
Low, and d) Neutral. Moreover, in case of documents with This additional information can help the user in their
high, medium and low scores we also report the top-scoring search task (e.g. allowing them to navigate the collection
perspective terms that were extracted using the Wikipedia more easily or providing evidence to allow the user to refor-
graph structure as explained previously. A sample search mulate their query more efficiently).
corresponding to search query “india pakistan relations” and Our proposed system, although related in that we also at-
4 tempt to give the user an insight into the answer set and its
These are basically perspective categories at depth zero. relation to the query, differs in a fundamental manner. Our
5
These are basically perspective categories at depth one and system, we posit, allows the user to gain insight into the an-
two.
6
The set building phase is performed through a cus- swer set and its relation to the query, but moreover, allows
tom Wikipedia API that has pre-indexed Wikipedia to the user to gain an insight into a perspective inherent in
data and hence, it is computationally fast. For details the answer set. Our system uses an external and collectively
http://www3.it.nuigalway.ie/cirg/prj/WikiMadeEasy.html created knowledge resource (which is less likely to be biased
Figure 4: Search Results within Perspective-Aware Search
in a given direction) to obtain extra terms to represent the [3] M. A. Hearst. ’natural’ search user interfaces.
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7
The preliminary user evaluations have not been shared in
this paper.