How Do Users Express Goals on the Web? - An Exploration of Intentional Structures in Web Search

Abstract

Many activities on the web are driven by high-level goals of users, such as “plan a trip” or “buy some product”. In this paper,
we are interested in exploring the role and structure of users’ goals in web search. We want to gain insights into how users
express goals, and how their goals can be represented in a semi-formal way. This paper presents results from an exploratory
study that focused on analyzing selected search sessions from a search engine log. In a detailed example, we demonstrate how
goal-oriented search can be represented and understood as a traversal of goal graphs. Finally, we provide some ideas on how
to construct large-scale goal graphs in a semi-algorithmic, collaborative way. We conclude with a description of a series
of challenges that we consider to be important for future research.

Full text

How Do Users Express Goals on the Web? -
An Exploration of Intentional Structures in Web Search
M. Strohmaier1, M. Lux2, M. Granitzer3, P. Scheir1,3, S. Liaskos4, E. Yu5
1Graz University of Technology, 8010 Graz, Austria
2Klagenfurt University, 9020 Klagenfurt, Austria
3Know-Center Graz, 8010 Graz, Austria
4York University, Toronto, Canada
5University of Toronto, Toronto, Canada
markus.strohmaier@tugraz.at, mlux@itec.uni-klu.ac.at, mgrani@know-center.at,
peter.scheir@tugraz.at, liaskos@yorku.ca, yu@fis.utoronto.ca
Abstract. Many activities on the web are driven by high-level goals of users,
such as “plan a trip” or “buy some product”. In this paper, we are interested in
exploring the role and structure of users’ goals in web search. We want gain
insights into how users express goals, and how their goals can be represented in
a semi-formal way. The paper presents results from an exploratory study that
focused on analyzing selected search sessions from a search engine log. In a
detailed example, we demonstrate how goal-oriented search can be represented
and understood as a traversal of goal graphs. Finally, we provide some ideas on
how to construct large-scale goal graphs in a semi-algorithmic, collaborative
way. We conclude with a description of a series of challenges that we consider
to be important for future research.
Keywords: information search, search process, goals, intentional structures
1 Motivation
In a highly influential article regarding the future of the web [1], Tim Berners-Lee
sketches a scenario that describes a set of agents collaborating on the web to address
different needs of users – such as “get medication”, “find medical providers” or
“coordinate appointments”.
In fact, many activities on the web are already implicitly driven by goals today.
Users utilize the web for buying products, planning trips, conducting business, doing
research or seeking health advice. Many of these activities involve rather high-level
goals of users, which are typically knowledge intensive and often benefit from social
relations and collaboration. Yet, the web in its current form is largely non-intentional.
That means the web lacks explicit intentional structures and representations, which
would allow systems to, for example, associate users’ goals with resources available
on the web. As a consequence, every time users turn to the web for a specific purpose
they are required to cognitively translate their high-level goals into the non-intentional
structure of the web. They need to break down their goals into specific search queries,
tag concepts, classification terms or ontological vocabulary. This prevents users from,
In Proceedings of the International Workshop on Collaborative Knowledge Management for Web Information Systems
(We.Know'07), held in conjunction with the 8th International Conference on Web Information Systems Engineering
(WISE 2007)

2 M. Strohmaier1, M. Lux2, M. Granitzer3, P. Scheir1,3, S. Liaskos4, E. Yu5
for example, effectively assessing the relevance and context of resources with respect
to their goals, benefiting from the experiences of others who pursued similar goals
and also prevents them from assessing conflicts or systematically exploring
alternative means.
In a recent interview, Peter Norvig, Director of Google Research, acknowledged that
understanding users' needs to a greater extent represents an “outstanding” research
problem. He explains that Google is currently looking at “finding ways to get the user
more involved, to have them tell us more of what they want.” [2]. Having explicit
intentional representations and structures available on the web would allow users to
express and share their goals and would enable technologies and other users to
explore, comprehend, reason about and act upon them.
It is only recently that researchers have developed a broad interest in the goals and
motivations of web users. For example, several researchers studied intentionality and
motivations in web search logs during the last years [3,4,5]. Because web search
today represents a primary instrument through which users exercise their intent,
search engines have a tremendous corpus of intentional artifacts at their disposal. We
define intentional artifacts broadly to be electronic artifacts produced by users or user
behaviour that contain recognizable “traces of intent”, i.e. implicit traces of users’
goals and intentions.
This paper represents our initial attempt towards exploring the role and structure of
users’ goals in web search queries. We want to learn in detail how users express their
goals on the web - as opposed to what goals they have, which is in the focus of other
studies [3,4,5]. We also want to explore how search goals can be represented in an
explicit, semi-formal way and we are interested in learning about the different ways in
which explicit goal representations could be useful, and to what extent. From our
preliminary findings of an exploratory study, we want to give a qualitative account of
identified potentials and obstacles in the context of goal-oriented search.
2 State of the Art
We will discuss two main streams of research that are relevant in the context of this
paper: The first stream of research focuses on identifying and understanding what
goals users pursue in web search. The second stream focuses on developing goal-
oriented technical solutions, i.e. solutions that depend on the explicit articulation of
user goals or automatic inference thereof.
In the first stream, researchers have proposed categories and taxonomies of user
goals [4,5] and automatic classification techniques to classify search queries into goal
categories [3]. Goal taxonomies include, for example, navigational, informational and
transactional categories [3]. Different categories are assumed to have different
implications on users’ search behaviour and search algorithms. To give some
examples: Navigational search queries (such as the query “citeseer”) characterize
situations where a user has a particular web site in mind and where he is primarily
interested in visiting this page. Informational search queries (such as the query
“increase wine crop”) are queries where this is not the case, and users intend to visit
multiple pages to, for example, learn about a topic [3]. Further research aims to

How Do Users Express Goals on the Web? -
An Exploration of Intentional Structures in Web Search 3
empirically assess the distribution of different goal categories in search query logs via
manual classification and subsequent statistical generalization [4] and/or Web Query
Mining techniques [3,6]. There is some evidence that certain categories of goals can
be identified algorithmically based on different features of user behaviour, such as
“past user-click behaviour” and an analysis of “click distributions” [3]. Recently, a
community of researchers with an interest in Query Log Analysis has formed at the
World Wide Web 2007 conference as a separate workshop.
A second stream of research attempts to demonstrate the principle feasibility of
implementing goal-orientation on an operational level. GOOSE, for example, is a
prototypical goal-oriented search engine that aims to assist users in finding adequate
search terms for their goals [7]. Miro, another example, is an application that
facilitates goal-oriented web browsing [8]. The Lumiere Project focused on inferring
goals of software users based on Bayesian user modeling [10]. Work on goal-oriented
acquisition of requirements for hypermedia applications [11] shows that it is possible
to translate high-level goals of stakeholders into (among other things) low level
content requirements for web applications. Another example [12] facilitates
purposeful navigation of geospatial data through goal-driven service invocation based
on WSMO. WSMO is a web service description approach that decouples user desires
from service descriptions by modeling low-level goals (such as
“havingATripConfirmation”) and non-functional property constructs [13]. In addition
to these approaches, there have been several studies in the domain of information
science that focus on different search strategies (such as top-down, bottom-up, mixed
strategies) of users [14].
Apart from these isolated, yet encouraging, attempts, current research lacks a deep
understanding about how users express their goals, and what explicit representations
could be suitable to describe them.
3 How do Users Express Goals in Web Search?
We initiated an explorative study in response to the observation that there is a lack
of research on how users express their goals in web search. In the following we will
present preliminary findings from this study.
Data sources: We have used the AOL search database [15] as our main data
source1. In addition to the AOL search database, several other web search logs are
available [16]. We have used the AOL search database because it provides
information about anonymous User IDs, time stamps, search queries, and clicked
links. To our knowledge, the AOL search database is also the most recent corpus of
search queries available (2006). We are aware of the ethic controversies arising from
using the AOL search database. For example, although the User IDs are anonymous, a
New York Times reporter was able to track back the identity of one of the users in the
dataset [17]. As a consequence, we masked the search queries that are presented in
1 Because the AOL search database was retracted from AOL shortly after releasing it, we
obtained a copy from a secondary source: http://www.gregsadetsky.com/aol-data/ last
accessed on July 15th, 2007.

4 M. Strohmaier1, M. Lux2, M. Granitzer3, P. Scheir1,3, S. Liaskos4, E. Yu5
this paper by maintaining their semantic frame structure, but exchanging certain
frame element instantiations [19]. We will elaborate on this later on. In following
such an approach, we aim to protect the real identity of the users being studied while
retaining necessary temporal and intentional relations of search queries.
Methodology: In this study we were interested in how users express, refine, alter
and reformulate their goals while searching. We have searched the AOL search
database for different verbs that are considered to indicate the presence of goals,
including verbs such as achieve, make, improve, speedup, increase, satisfied,
completed, allocated, maintain, keep, ensure and others [18]. We subsequently
annotated random results (different search queries) with semantic frame elements
obtained from Berkeley’s Framenet [19]. Framenet is a lexical database that aims to
document the different semantic and syntactic combinatory possibilities of English
words in each of its senses. It aims to achieve that by annotating large corpora of text.
It currently provides information on more than 10.000 lexical units in more than 825
semantic frames [19]. A lexical unit is a pairing of a word with a meaning. For
example, the verb “look” has several lexical units dealing with different meanings of
this verb, such as “direct one’s gaze in a specified direction” or “attempt to find”.
Each different meaning of the word belongs to a semantic frame, which is “a script-
like conceptual structure that describes a particular type of situation, object or event
along with its participants and props” [19]. Each of these elements of a semantic
frame is called frame elements. Semantic frames are evoked by lexical units. To give
an example, the semantic frame “Cause_change_of_position_on_a_scale” is evoked by a
set of lexical units, such as decline, decrease, gain, plummet, rise, increase, etc, and
has the core frame elements Agent [], Attribute [Variable], Cause [Cause] and Item [Item].
Agent refers to the person who causes a change of position on a scale, attribute refers to
the scale that changes its value, cause refers to non-human causes to the change, and
item refers to the entity that is being changed.
Example: The search query “Increase Computer Speed” can be annotated with
Frame Elements from Framenet’s lexicon. The lexical unit “increase” evokes the
frame “Cause_change_of_position_on_a_scale”, which we can use to annotate “Increase
Computer Speed” in the following way: “Increase [item Computer] [attribute Speed]”. The
frame elements Agent and Cause do not apply here.
Selected Results: One verb we were using to explore the dataset was “increase”.
The query history depicted in Table 1 below presents an excerpt of the search history
of a single user that performed search queries containing the verb “increase”. We
picked this particular search log because it demonstrates several interesting aspects of
the role of goals in web search. We do not claim that this user’s search behaviour is
typically or representative for a larger set of users or queries. In fact, the majority of
search queries in the AOL search database is of a non-intentional nature. We discuss
the implications of this observation in the Section 5.
We obtained the complete search record of the selected user, frame-annotated his
intentional queries based on the FrameNet lexicon and classified the queries from an
intentional perspective (e.g. refinement, generalization, etc). The particular frame
used during annotation was “Cause_change_of_position_on_a_scale”, which is evoked
by the verb “increase”. For privacy reasons, we modified the search queries in the

How Do Users Express Goals on the Web? -
An Exploration of Intentional Structures in Web Search 5
following way: We retained the verbs and attributes which were part of the original
query, but modified the contents of the semantic frame element item (e.g. wine crop)
and cause (e.g. fertilizer) as well as time stamps (maintaining relative time differences
with an accuracy of +/- 60 seconds). We’d like to remark that the users’ search history
below was interrupted by other, non-intentional queries (queries such as “flickr.com”)
and also other more complex intentional queries. For reasons of illustration and
simplicity, we leave these out in Table 1.
Nr. Query Frame Annotation Time Stamp Goal
#1 How to get more wine
crop How to
get more
[itemwine crop]
2006-03-30
19:29:59
Formulation
#2 Fertilizer or
insecticide to increase
wine crop
[cause Fertilizer] or
[cause insecticide] to
increase
[itemwine crop]
2006-03-30
19:45:28 Refinement
#3 Fertilizer to increase
wine crop [cause Fertilizer] to
increase
[item wine crop]
2006-03-30
19:46:11 Refinement
[further non-intentional queries, not related to wine crop]
#4 Increase wine crop increase
[item wine crop] 2006-03-30
19:48:25 Generali-
zation
#5 How to get rich wine
crop How to
get rich
[item wine crop]
2006-04-07
06:29:19 Different
Goal
Formulation
[non-intentional query “wine crop”]
#6 How to get good wine
crop How to
have good
[item wine crop]
2006-04-07
06:40:45 Re-
formulation
[further non-intentional queries and further more complex intentional queries
related to “wine crops”]
Table 1. Frame-based Annotation of Selected Queries from a Single Search Session
From a semantic frame perspective, it is interesting to see that it is not possible to
annotate all of the above queries consistently. While the verb increase evokes the
corresponding frame “Cause_change_of_position_on_a_scale” in queries #2, #3 and #4,
the other queries #1, #5, and #6 do not contain increase and therefore do not evoke
the same frame. Although FrameNet contains lexical entries for the verbs get and
have and the adjectives good, rich and more, the word senses get more, get rich and
have good are not yet captured as lexical units in the FrameNet lexicon. However, it is
easily conceivable that an expanded or customized version of FrameNet (possibly in
combination with WordNet) would contain these units and that they could be
associated with the same semantic frame.
From a goal-oriented perspective, we will use our findings to develop a set of

6 M. Strohmaier1, M. Lux2, M. Granitzer3, P. Scheir1,3, S. Liaskos4, E. Yu5
hypothesis that we believe are relevant and helpful to further study the role and
structure of users’ goals on the web.
Several things are noteworthy in the search history of the above user: First, the user
started off with a goal formulation (#1 how to get more wine crop) and then
proceeded with a refinement of this goal in a second query (#2 Fertilizer or insecticide
to increase wine crop). The provided time stamps reveal that in this case, the time
difference between the two queries was more than 15 minutes! Although it is hard to
assess the real cause for this time lag, the AOL search database provides a possible
explanation by listing the websites that the user visited in response to query #1, which
includes a discussion board website hosting discussions on different strategies to get
more “wine crop” (including “insecticides” and “fertilizer”). This allows us to
hypothesize that H1: Goal refinement is a time-intensive process during search.
In query #3, the user performed a further refinement of his goal to “fertilizer to
increase wine crop” and in #4, he performs a generalization to “Increase wine crop”.
This is interesting again from a goal-oriented perspective: Instead of refining his goals
in a strict top-down approach, the user alternates between top-down (refining) and
bottom up (generalizing) goal formulations. We consider this observation in a
hypothesis 2 that claims that, from a goal-oriented perspective, user search is neither a
strict top-down, nor a purely bottom-up approach, but a combination of both. While
we focus on informational queries only, previous studies have found that the type of
approach does not only depend on the type of task, but also different types of users
[14]. This leads us to hypothesize H2: Users search by iteratively refining,
generalizing and reformulating goals, in no particular order.
In query #5 the user performs a different goal formulation: “How to get rich wine
crop”. Instead of focusing on quantity (“get more” / “increase”), the search now can
be interpreted to focus on the quality of wine crop (“get rich”). In query #6, a goal re-
formulation is performed. This can be regarded to represent the same goal, but
articulated in a slightly different way (“get good” instead of “get rich” wine crop).
Another very interesting observation is that there is a time span of more than 7 days
between queries #1-#4 and queries #5-#6! Although we have no information about
what the user might have done in between these search activities, we use this evidence
to tentatively hypothesize that identifying different, but related, goals is difficult for
users, and it involves significant time and potentially cognitive efforts. In a more
intuitive way, we can say that it seems that, especially with high-level, knowledge
intensive goals, users learn about their goals as they go. We formulate this
observation in hypothesis H3: Exploring related goals is more time-intensive than
goal refinement.
And finally, we can observe that a smaller amount of time is passing between
search queries #5 and #6. The question that is interesting to ask based on this
observation is whether goal refinements require more time and cognitive investments
from users than goal re-formulations. One might expect that users with search
experience become skilled in tweaking their queries based on the search engines’
responses without modifying their initial goal. We express this question in our
hypothesis H4: Goal re-formulation requires less time than goal exploration or
goal refinement. Next, we will explore some implications of these observations.

How Do Users Express Goals on the Web? -
An Exploration of Intentional Structures in Web Search 7
Analysis: If hypothesis H1 would be corroborated in future studies, offering users
possible goal refinements would be very likely to be considered a useful concept. If
hypothesis H2 would be supported in further studies, goal-oriented search would not
only need to focus on goal refinement, but also on providing a range of different
intentional navigation structures, allowing to flexibly alternate between refining,
generalizing and exploring goals. If the exploration of goals represents a very time
intensive process (H3), then users can be assumed to greatly benefit from having
access to the goals of other users. And finally, if goal re-formulation does not require
significant amounts of time (H4), there might be little motivation for researchers to
invest in semantic similarity of web searchers, but more motivation to invest in
intentional similarity.
Surprisingly, when analyzing current search technologies such as Google, we can
see that there is almost no support for any of these different goal-related search tasks
(refinement, generalization, etc) identified. Although Google helps in reformulating
search queries (“Did you mean X?”), this – at most – can be regarded to provide some
support for users in goal re-formulation on a syntactic level, but not on a truly
intentional level (help in goal refinement, generalization, etc).
These observations immediately raise a set of interesting research questions: Do
the formulated hypotheses hold for large sets of search sessions? How can the
hypotheses be further refined to make them amenable to algorithmic analysis? And
how can the identified goals be represented in more formal structures? While we are
interested in all of these questions, in this paper we will only discuss the issue of more
formal representations in some greater detail.
4 Representing Search Goals as Semi-Formal Goal Graphs
We have modeled the goals of a user who is interested in “wine crop” with the
agent-and goal-oriented modeling framework i* [20]. When applying i*, we focused
on goal aspects and neglected agent-related concepts such as actors, roles and others.
The i* framework provides elements such as softgoals, goals, tasks, resources and a
set of semantic relations between them. The goal graph in Figure 1 was constructed
by one of the authors of this paper based on the frame-annotated goals depicted in
Table 1. In the diagram, the goals of the users are represented through oval-shaped
elements. Means-ends links are used to indicate alternative ways (means) by which a
goal (ends) can be fulfilled. Goals represent states of affairs to be reached, and tasks,
which are represented through hexagonal elements, describe specific activities that
can be performed for the fulfillment of goals. Soft-goals, which are represented
through cloud-shaped elements, describe goals for which there is no clear-cut
criterion to be used for deciding whether they are satisfied or not. Thus, soft-goals are
fulfilled or denied to a certain degree, based on the presence or absence of relevant
evidence. In i* diagrams, links such as "help" or "hurt" are used to represent how a
belief about the fulfillment or denial of a soft-goal depends on the satisfaction of other
goals. From the goal-graph in Figure 1 we can infer that the goal “increase wine crop”
can be achieved through a variety of means: Fertilizer, Insecticides and Irrigation all
represent means to achieve the end of increasing wine crop. The goal “Increase wine

8 M. Strohmaier1, M. Lux2, M. Granitzer3, P. Scheir1,3, S. Liaskos4, E. Yu5
crop” and the related goal “Improve wine crop” both have “help” contribution links to
the overarching soft-goal “Winery be successful”.
Fig. 1. Representing Users’ Search Goals in a Semi-Formal Goal Graph
Assuming that such goal graphs can be constructed for a range of different
domains (which is evident in a broad set of published examples from the domain of
requirements engineering), it would be interesting to see how the different goal-
related activities of users during search (such as goal formulation, goal refinement,
goal generalization, etc) can be represented as a traversal of such a goal graph. We
will explore this question next.
4.1 How Can Search be Understood as a Traversal through A Goal Graph?
Modifying search engines’ algorithms to exploit knowledge about users’ goals has
a high priority for search engine vendors [5]. Being able to relate search queries to
nodes in a goal graph could enable search engines to provide users goal-oriented
support in search. This could mean that software could offer users to refine their
search goals, generalize them or propose related goals from other users.
Figure 2, depicts the results of manually associating the search queries presented in
Table 1 with the goal graph introduced in Figure 1. We can see that the user starts his
search by formulating a version of the goal “increase wine crop” in query #1. This
goal is refined in query #2 “Fertilizer or insecticides to increase wine crop” which can
be mapped onto the two means “Fertilizer to increase wine crop” and “Insecticides to
increase wine crop”. Query #3 “fertilizer to increase wine crop” represents a further
refinement. In query #4, the user generalizes his search goal to “increase wine crop”
again. Query #5 and #6 relate to a different goal: “Improve wine crop”. Query #5 and
#6 can be considered to be re-formulations of the same goal.
Interestingly, the goal graph reveals that the user did not execute search queries
related to the means “Irrigation to increase wine crop” or the soft-goal “Winery be
successful”, although one can reasonably expect that the user might have had a
genuine interest in these goals too (although validation of this claim is certainly hard
without user interaction).

How Do Users Express Goals on the Web? -
An Exploration of Intentional Structures in Web Search 9
Fig. 2. Goal-Oriented Search as a Traversal of Goal Graphs
As a consequence, a major benefit of having goal graphs available during search
could be pointing users to refined goals or making sure that users do not miss related
goals. But assuming that having such goal graphs would be beneficial, how can they
be constructed?
4.2 How Can Large-Scale Goal Graphs be Constructed?
Mapping search queries onto goal graphs presumes the existence and availability of
goal graphs. In our example, we have hand-crafted a goal graph for illustration
purposes. However, manually constructing such goal graphs is costly, and anticipating
the entirety, or even a large proportion, of users’ goals on the internet would render
such an approach unfeasible. So how can we construct large-scale goal graphs that do
not rely on the involvement of expert modelers? Automatic user goal identification is
an open research problem [6], and answering this question satisfyingly would go well
beyond the scope of this paper, but we’d like to discuss some pointers and ideas: The
recent notion of folksonomies has powerfully demonstrated that meaningful relations
can emerge out of collective behaviour and interactions [21]. We would like to briefly
explore this idea and some of its implications for constructing large-scale goal graphs
based on frame-analysis of intentional artifacts.
Let’s assume that a system has the capability to come up with frame-based
annotations of search queries. The search query “fertilizer or insecticide to increase
wine crop” would then be annotated in a way that is depicted on the left side of Figure
3. Based on such annotations, a goal graph construction algorithm could use heuristics
to construct a goal graph similar to the one depicted on the right side of Figure 3.
Heuristic rules could, for example, prescribe that the root goal is represented by the
central verb (“increase”) and its corresponding item (“wine crop”), and that the means

10 M. Strohmaier1, M. Lux2, M. Granitzer3, P. Scheir1,3, S. Liaskos4, E. Yu5
to this end are represented by the frame elements cause (“fertilizer”, “insecticide”).
Each time a user formulates an intentional search query, the goal graph construction
algorithm could construct such small, atomic goal graphs heuristically.
Fig. 3. Heuristic Construction of Atomic Goal Graphs via Frame-Annotation of Search Queries
In a next step, these atomic goal graphs constructed from different users’ search
queries would need to be connected to larger whole. Considering hypothesis 2, this
appears to be a task that is hard to perform by algorithms alone. Nevertheless, usage
data analysis, explicit user involvement or semi-automatic, collaborative model
construction efforts (as e.g. pursued by the ConceptNet project [9]) might help to
overcome this issue, which can be considered to represent a non-trivial research
challenge.
5 Implications and Threats to Validity
We are aware that our particular research approach puts some constraints on the
results of our work: Due to our focus, the search queries we analyzed were not
required to be representative and, in fact, they are not. To obtain some quantitative
evidence, two of the authors have categorized a pseudo-random sample (based on
java.util.Random randomizer) of 2000 out of 21,011,340 queries into intentional and
non-intentional categories, based on the criterion whether a query contains at least
one verb (infinitive form, excluding gerund) and at least one noun. For each of these
candidates, two authors of this paper judged whether it would be possible to envisage
the goal a user might have had based on a specific query (such as “increase computer
speed”). From our analysis, only 2.35% (47 out of 2000) of the searches from the
AOL search database can be considered to be such “intentional queries”. The
probability of occurrence then results in a 95% confidence interval of [0.0169,
0.0301] for the probability of a query being intentional according to our criteria.
In contrast to these findings, related studies found somewhat higher numbers. A study
reported in [4] suggests that 35% of search sessions have a general, high-level
information research goal (such as questions, undirected requests for information, and
advice seeking). The difference in numbers might be explained by different levels of
analysis and a more relaxed understanding of goals in [4], which allows a broader set
of queries (including queries that do not have verbs) to be labelled as goal-related.

How Do Users Express Goals on the Web? -
An Exploration of Intentional Structures in Web Search 11
There are several implications of this discrepancy: While users often have high-level
goals when they are searching the web, they are currently not rewarded for
formulating (strictly) intentional queries. In fact, one can assume that formulating
non-intentional queries represents a (locally) successful strategy in today’s search
engine landscape. As a result, users might have adapted to the non-intentional mode
in which Google, Yahoo and other search engines operate today. However, this
situation makes it necessary for users to cognitively translate their high-level goals
into search queries and perform reasoning about their goals in their mind. This
potentially increases the cognitive burden of users and makes it hard for systems to
connect them with other users who pursue similar goals or allowing them to benefit
from the experiences made by other searchers.
We do not believe that these implications put constraints on our results: With a
collaborative goal modeling approach, even a small percentage of strictly intentional
queries could be used to construct large-scale goal graphs. Even if the percentage of
intentional queries among the entirety of search queries would be as low as 1% or
even lower, the sheer amount of queries executed on the World Wide Web would still
provide algorithms with a rich corpus to construct large-scale goal graphs. On the
web, such an approach is by far not unusual: For example, on wikipedia, a minority of
users contributes content that is being used by a majority. However, the task of
constructing large-scale goal graphs would obviously become much easier if users
actually would be aware that search engines would interpret their queries as an
expression of intent rather than an input that is being used for text string matching.
6 Conclusions
Based on our preliminary findings, we can formulate a set of interesting research
challenges: First, how can large-scale goal graphs be represented and constructed?
How can intentional artifacts (such as search queries) be associated with nodes in
such goal graphs? How can goals and web resources be associated? And how can
collaboration on the internet support the construction of such intentional structures?
Our work represents an initial attempt towards understanding the role and structure
of goals in web search. We have demonstrated how search processes can be
understood as a traversal through goal graphs and have provided some ideas on how
to construct large scale goal graphs. In future work, we are interested in further
investigating and shaping intentional structures on the web.
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