Traditional search engines fail to capture the notion of \perspective" in their search results and at times present the results skewed towards a particular topic. Under most of these cases even query reformulation fails to retrieve desired search results and the underlying reason for such failure is often the bias within the document collection itself (e.g., news articles). A perspective-aware search interface enabling users to look into search results for some \perspective" terms may be of great use for certain information needs. In this paper we describe such a system.
It is often the case that when using a search engine for
information seeking users have an underlying intent [
Consider the case of a user who wishes to nd more about a certain event (say, a bomb attack in a certain region). The search results returned contain a majority of news reports blaming Islam relating it with 1This may also be seen as topic drifts within a document. Presented at EuroHCIR2013. Copyright c 2013 for the individual papers by the papers' authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.. terrorism in most of the cases. This prompts the user to explicitly evaluate how much Islam is related to terrorism in the returned search results.
Consider the case of a user who wishes to nd out about roles and rights of women in Islam but the search engine returns articles that contain a high amount of terms highlighting oppression against women instead of women rights and roles. In this case the user is prompted to check the correlation between women and oppression within the search results that have been returned.
Note that the perspective given by most search results (Islam in our motivating example (1) and oppression in our 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 in observing the amount of perspective tendencies in various news reports.
This paper proposes the concept of a \perspective-aware"
search interface that enables the user to explicitly analyse
search results for information from a particular
perspective with respect to an issued query. To the best of our
knowledge, previous research within Human-Computer
Interaction and Information Retrieval has failed to capture
the notion of \perspective" within the information retrieval
process. Early research related to Interactive Information
Retrieval by Belkin [
Recently the information retrieval community has turned
attention to diversi cation of search results which aims to
tackle the issue of query ambiguity on the user side [
This section presents the essential details of the proposed
perspective-aware search interface along with the underlying
implementation details. We keep the interface as simple as
possible on account of research suggesting users' reluctance
in switching from a simple search form [
The underlying perspective detection algorithm makes use
of the encyclopedic structure in Wikipedia; more speci
cally the knowledge encoded in Wikipedia's graph structure
is utilized for the discovery of various perspectives in
documents 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
a kind of semantic tag for that article [
The underlying perspective detection algorithm within our system requires the perspective term/phrase to match the title of a Wikipedia article. This may seem to impose a cognitive load on the user at search time. However, this is not the case: as shown in Figure 3 the entered text automatically turns green when a certain user-speci ed perspective term matches the title of a Wikipedia article, and symmetrically the entered text automatically turns red in case of a mismatch.
Once the perspective term is entered correctly the system fetches the Wikipedia article corresponding to the perspective term referred to as Seed Perspective Article (PAseed) along with the categories to which it belongs and we use 3We say taxonomy-like because it is not strictly hierarchical due to the presence of cycles in the Wikipedia category graph. \terrorism" is shown in Figure 4. As evident from the top search result, there is a high perspective of terrorism within the returned document and perspective terms that our algorithm fetches are as follows: a) the war on terrorism, b) ayman al zawahiri, and c) osama bin laden.
PC04 to refer to these categories. After fetching of Wikipedia categories in PC0, the system retrieves sub-categories of PC0 until depth 2 i.e., PC1 and PC25 and collectively these categories related to PAseed are referred to as PC (where PC is union of PC0, PC1 and PC2.). Next, the set of all articles within the Wikipedia category set PC is retrieved and we refer to this set as Expanded Perspective Article Set (PAexpanded). The system then retrieves all categories associated with the set PAexpanded which we refer to as WC ; note that PC is a subset of WC. Finally, the intersection between PC and WC is retrieved which is a set of categories representative of the domain of the perspective term originally input by the user, we refer to this set of representative categories as RC.
After building the Wikipedia category sets as de ned above6 i.e., PC, RC and WC we match variable-length n-grams within a document with articles in the set PAexpanded, and we check for cardinality of RC and WC. The cardinality scores along with n-gram frequencies are used to compute a perspective score for each document.
There have been many e orts in the information retrieval research to present to users information regarding the relationship between the query and the answer set and the query and document collection. Capturing this information during the retrieval process provides the user with much valuable information (e.g. whether a term is overly speci c, or whether a term is ambiguous etc.). Various attempts have been made to tackle this problem, ranging from the de nition of snippets to the de nition of approaches to cluster search results (Clusty.com), to the presentation of diversi ed search results in the rst position of the ranked list o ered to the users.
Recently there has been a resurgence of interest in de ning visualization techniques of search results that o er an e ec2.2 Search Results Presentation tive and more informative alternative to usual and scarcely informative ranked lists. Pioneer visualization systems are
The perspective scores computed in section 2.1 are dis- represented by Tilebar [
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 reforgraph structure as explained previously. A sample search mulate their query more e ciently). corresponding to search query \india pakistan relations" and Our proposed system, although related in that we also attempt to give the user an insight into the answer set and its 4These are basically perspective categories at depth zero. relation to the query, di ers in a fundamental manner. Our t5wToh.ese are basically perspective categories at depth one and system, we posit, allows the user to gain insight into the an6The 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 in a given direction) to obtain extra terms to represent the perspective of interest to the user. This knowledge (perspective term and related terms) does not modify the query (as would an additional query term), but is instead used to highlight the presence of a perspective in the answer set.
In this paper we have proposed a novel approach for capturing the relationship between a user's query and the returned answer set. We do not rely on evidence in the document collection or the query stream, but rather instead extract terms from an external source of evidence to help users quickly see the presence of a particular perspective in the document collection and answer set.
Having built the system and undertaken preliminary user evaluations7, we aim at undertaking a complete and systematic review of the approach. This will comprise a number of separate user evaluation tasks. The initial experiments will involve comparing our search approach with and without the perspective-aware component over a number of tasks to see if the additional context and information provided by our perspective aware system aids the users in a range of information-seeking tasks. Our second planned experiments will be focussed on persons seeking information from newspaper articles, a domain wherein a degree of bias often exists. We wish to explore the users' experience with regards to any perceived bias in the considered corpora. 7The preliminary user evaluations have not been shared in this paper.