Conference PaperPDF Available

Acquiring Explicit User Goals From Search Query Logs

Authors:

Figures

Content may be subject to copyright.
Acquiring Explicit User Goals From Search Query Logs
Markus Strohmaier
Graz University of Technology
and Know-Center
markus.strohmaier@tugraz.at
Peter Prettenhofer
Graz University of Technology
peter.prettenhofer@
student.tugraz.at
Mark Kröll
Graz University of Technology
mkroell@tugraz.at
Abstract
Knowledge about user goals is crucial for realizing
the vision of intelligent agents acting upon user intent
on the web. In a departure from existing approaches,
this paper proposes a novel approach to the problem
of user goal acquisition: The utilization of search
query logs for this task. The paper makes the following
contributions: (a) it presents an automatic method for
the acquisition of user goals from search query logs
with useful precision/recall scores (b) it provides
insights into the nature and some characteristics of
these goals and (c) it shows that the goals acquired
from query logs exhibit traits of a long tail distribution.
1. Introduction
To realize the vision of intelligent, goal-oriented
agents on the web, agents must have programmatic
access to the set and variety of human goals, in order to
reason about them and to provide services that help
satisfy users’ needs. In Berner’s Lee vision, an agent
aiming to “plan a trip to Vienna” would need to have
some means to understand that “plan a trip” is likely to
involve a set of other goals or services, such as “contact
a travel agency” and “book a hotel”. This type of
knowledge has been characterized as commonsense
knowledge, i.e. knowledge that humans are generally
assumed to possess, but which is extremely difficult
for computers to acquire. Examples of current research
projects aiming to capture and organize commonsense
knowledge, including knowledge about human goals,
are CyC [6] or Openmind / ConceptNet [7]. However,
existing attempts suffer from two main problems: 1)
the goal acquisition problem (or bottleneck), which
refers to the costs associated with knowledge
acquisition and 2) the goal coverage problem, which
refers to the difficulty of capturing the tremendous
variety and range in the set of human goals. These
problems have hindered progress in capturing broad
knowledge about human goals, and have hindered the
development of intelligent agents, services and
applications on the web.
On the web, search engines represent a primary
instrument through which users exercise their intent
today. This allows search queries to indirectly convey
knowledge about users’ goals and intentions, which are
usually latent, implicit, dynamic and private. Given
that existing attempts to capture knowledge about
human goals are usually limited, an interesting
question in the context of search is: Can we
automatically acquire knowledge about a large variety
of user goals from search query logs? In this paper, we
study if, how and to what extent it is possible to
automatically acquire knowledge about explicit user
goals (such asbook a hotel”) from search query logs.
2. Human Subject Study
In order to gauge the results of an automatic
acquisition approach addressing this problem, we first
conducted a human subject study aiming to 1) define
the notion of explicit user goals more rigorously and 2)
to learn about its principal agreeability.
Definition of Explicit User Goals: We define
queries containing explicit user goals in the following
way:
A search query is regarded to contain an explicit
user goal (or short: explicit goal) whenever the query
1) contains at least one verb and 2) describes a
plausible state of affairs that the user may want to
achieve or avoid in 3) a recognizable way.
An example of such a query would be “book a
hotel”. A query does not contain an explicit goal when
it is difficult or extremely hard to elicit some specific
goal from the query. Examples include blank queries,
or queries such as “car” or “travel”, which embody user
goals on a very general, ambiguous and mostly implicit
level.
Questionnaire Design: To explore the utility of
this definition, we have conducted a questionnaire in
which four human subjects (Computer Science
graduate students) were instructed to manually label
3000 queries randomly obtained from the AOL search
query log [8] (after a number of sanitization and pre-
processing steps were performed). The subjects were
required to independently answer a single question for
each of the 3000 queries. The question for each query
followed this schema: Given a query X, Do you think
that Y (with Y being the first verb in X, plus the
remainder of X) is a plausible goal of a searcher who
is performing the query X? To give two examples:
Given query: “how to increase virtual memory
Question: Do you think thatincrease virtual memory” is a plausible
goal of a searcher who is performing the query “how to increase
virtual memory”? Potential Answer: Yes
Given query: “boys kissing girls” Question: Do you think that
kissing girls” is a plausible goal of a searcher who is performing
the query “boys kissing girls” Potential Answer: No
After the question-answering task, we assigned the
answers for each query to the corresponding categories
ourselves in the following way: each answer “Yes”
resulted in classifying the query as a “query containing
an explicit goal”, each answer “No” resulted in
classifying the query as a “query not containing an
explicit goal”. The results are reported next.
Agreeability of Constructs: We calculated a
function e(q) per query, which is the percentage of
human subjects who labeled a given query as
containing an explicit goal (cf. [5]). The chart in Figure
1 shows that 243 queries out of 3000 have been labeled
as containing an explicit goal by all 4 subjects (8.1%,
right most bar), and 134 queries as containing an
explicit goal by 3 out of 4 subjects. The majority of
queries (79.2%, left most bar) has been labeled as not
containing an explicit goal unanimously by all
subjects. A relatively small number of queries was
controversial (middle bar, 3.3%). Figure 1 shows that
e(q) approximates a dichotomous agreement
distribution, which provides preliminary evidence for
the agreeability of our constructs. To further explore
agreeability, we calculated the inter-rater agreement κ
[2] between all pairs of human subjects A, B, C and D.
Cohen’s κ measures the average pairwise agreement
corrected for chance agreement when classifying N
items into C mutually exclusive categories. The κ
values in our human subject study range from 0.65 to
0.76 (see Figure 1). Both measures combined, the
inter-rater agreement κ and the distribution of e(q), hint
towards a principle (yet not optimal) agreeability of
our construct definition. In the remainder of this paper,
we use these results to inform the development of an
automatic classification approach.
)(1
)()(
CP
CPOP
=
κ
Pai
r
A - B 0.70
A - C 0.69
A - D 0.65
B - C 0.76
B - D 0.75
C - D 0.76
Figure 1. e(q) Distribution and
3. Acquiring Explicit User Goals
Based on the human subject study, we now
introduce an inductive classification approach that
aims to perform the task of classifying queries into one
of the two categories (containing/not containing an
explicit goal) automatically.
Training Set: We have created a manually labeled
dataset for the purpose of training an automatic
classification approach. The manually labeled dataset
is based on the majority vote among the human
subjects of the human subject study presented
previously. Out of the 3000 labeled queries, the
negative examples were defined by the two bars on the
left hand side of Figure 2 (2525 total), and the positive
examples were defined by the two bars on the right
hand side (377). The bar in the middle represents the
controversial queries which were removed.
The approach for classifying queries consists of two
basic steps: POS tagging and classification.
POS Tagging: We can assume that queries
containing explicit goals can, to some extent, be
identified by the occurrence of certain syntactical, part-
of-speech patterns. To investigate this, we used a
Maximum Entropy Tagger for part-of-speech tagging
all queries. We used the Penn Treebank tag set
containing 36 word classes which provides a simple
yet adequately rich set of tag classes for our purpose.
Feature Set Description: The following feature
types were utilized:
Part-of-Speech Trigrams: Each query can be
translated from a sequence of tokens into a
sequence of POS tags. Trigrams were generated by
moving a fixed sized window of length 3 over the
POS sequence. The sequence boundaries were
expanded by introducing a single marker ($) at the
beginning and at the end allowing for length two
POS features. The query “buying/VBG a/DT
car/NN” would yield the following trigrams:
$ VBG DT; VBG DT NN; DT NN $
Stemmed unigrams: Queries can be represented
as binary word vectors or ‘Set of Words’ (SoW).
The Porter stemming algorithm was used for word
conflation and stopwords were removed.
We chose a linear Support Vector Machine (SVM)
using all the features as our classification method. The
performance of this approach is discussed next.
Evaluation: Table 1 presents the confusion matrix
on the manually labeled dataset and corresponding
True Positive (TP), False Positive (FP), False Negative
(FN) and True Negative (TN) scores.
Table 1. Confusion matrix
Classified as Æ Containing an
Explicit Goal
Not Containing an
Explicit Goal
Containing an
Explicit Goal 239 (TP) 138 (FN)
Not Containing an
Explicit Goal 73 (FP) 2452 (TN)
Our approach achieves a precision of 0.77, a recall
of 0.63 and an F1 score of 0.69. All values refer to the
class that represents queries containing goals. A
precision of 77% means that in 77% of cases, our
approach agrees with the majority of human subjects.
These results represent a significant improvement over
previous approaches [9]. Although there are slight
differences in the evaluation procedure and the type of
knowledge captured, the precision of explicit goals
acquired with our approach is roughly comparable to
precision scores reported for the ConceptNet
commonsense knowledge database.
4. Results
In the following, we present the results of applying
our automatic classification approach to a pre-
processed version of the entire AOL search query log
containing more than 20 million search queries.
Selected Statistics: Applying our automatic
classification method yielded a result set containing
explicit user goals consisting of 118.420 queries,
97.454 of them unique. With a precision of 77%, the
result set comprises an estimated 75.039 true positives
(actual queries containing explicit goals).
The 20 most frequent queries from the result set are
presented in Table 2. Each example is accompanied by
the rank and the number of different users who
submitted the query (frequency). Queries containing
the token ”http” were filtered out and those queries
containing expletives / objectionable content were
replaced by “deleted”. Some of the most frequent
queries containing goals relate to commonsense
knowledge goals, such as lose weight”, get pregnant” or
listen to music”, which provides some evidence of the
suitability of search query logs for the knowledge
acquisition task. Yet, the bias introduced by the corpus
(search queries) and the population (i.e. AOL users)
deserves attention: Many frequent queries deal with
web-related or AOL specific issues, such as the queries
add screen name” or “cancel aol service”. Entries such
as “wedding cake toppers”, “pimp my ride”, and “skating
with celebrities” represent false positives.
Table 2. 20 most frequent goals
Nr. Query #Users Nr. Query #Users
1 add screen name 205 11 cancel aol
service
54
2 create screen
name
137 12 pimp my
myspace
53
3 rent to own 120 13 cancel aol
account
50
4 listen to music 108 14 “deleted” 49
5 pimp my space 102 15 ”deleted” 48
6 pimp my ride 97 16 how to lose
weight
47
7 assist to sell 93 17 how to get
pregnant
47
8 wedding cake
toppers
64 18 change my
password
46
9 skating with
celebrities
58 19 discover credit
card
46
10 lose weight fast 56 20 check my
computer
43
If search query logs would be utilized for
knowledge acquisition, a relevant question to ask is:
How diverse is the set of goals contained in search
query logs? The diversity of goals would ultimately
constrain the utility of a given dataset for expanding
existing knowledge bases. In order to explore this
question, we present a rank/frequency plot of the data
depicted in Table 2. In Figure 2, goals are plotted
according to their rank and the set of different users
who share them.
Figure 2. Rank-frequency plot of goals
The distribution in Figure 2 shows that while there
are very few popular goals, a majority of goals is
shared by only a few users. In other words, the curve
approximates a power-law distribution, implying the
existence of a long tail effect of user goals. This
suggests that the explicit goals in the result set are
diverse and cover a broad range of different goals.
Qualitative Analysis: We selected an arbitrary set
of verbs and corresponding goals for more detailed
inspections. In Table 3, the 10 most frequent goals
which contain the verbsget”, “make”, “change” or
be” are listed. Frequency refers to the occurrence of
the goal in the result set.
The goals in Table 3 are the result of identifying the
first verb in a query containing a goal, and truncating
any tokens prior to this verb. Queries marked with a
“*” represent queries that are contained in
ConceptNet’s commonsense knowledge base (v2.1) as
well. Many goals in Table 3 are related to existing
commonsense knowledge goals, such as “be pregnant”,
be rich” or “be funny”.
Table 3. 10 most frequent goals containing
get, make, change and be
# Verb: get Verb: make Verb: change Verb: be
1 get
pregnant*
(141)
make
money* (87)
change my
password (100)
be anorexic*
(26)
2 get rid of
ants (28)
make your
own website
(43)
change my
screen name
(38)
be pregnant*
(19)
3 get out of
debt
planner
(19)
make money
at home (41)
change screen
name (32)
be bulimic
(12)
4 get rich or
die tryin
(17)
make money
fast (39)
change my aol
password (28)
be rich* (11)
5 get rid of
love
handles(17)
make money
online (34)
change
password (24)
be emo (8)
6 get married
(15)
make the
band 3 (30)
change my
profile (21)
be funny* (8)
7 get rich*
(15)
make money
from home
(25)
change your
name (21)
be happy*
(8)
8 get rich
with trump
(15)
make new
screen name
(24)
change* ( 20) be sexy* (7)
9 get out of
debt* (15)
make up (23) change my
email address
(17)
be in love*
(7)
10 get rid of
moles (14)
make out
(21)
change aol
password (14)
be an actress
(7)
5. Related Work and Conclusions
In previous research, He et al. [3] have studied the
acquisition of explicit user goals from search result
snippets (i.e. the segments of text listed on the result
pages of search engines). Our work is different in the
sense that it studies search queries themselves as a
source of explicit goals, which can be suspected to
better reflect user intent. Broder’s high level taxonomy
of search intent, proposing a distinction between three
classes of search goals, has stimulated a series of
follow-up research on category refinement and
automatic query categorization [4][5]. While previous
research has achieved considerable progress in the
categorization of queries into high-level goal
taxonomies serving a primarily functional purpose (to
improve search), this work focuses on the acquisition
of goal instances (explicit goals) from search query
logs for knowledge capture purposes.
Our work shows that search query logs have the
potential to address the two problems (goal acquisition
and goal coverage) of acquiring knowledge about
human goals on the web. In a departure from existing
approaches, we present an automatic classification
approach and experimental results that introduce
search query logs as a feasible, yet largely untapped
resource for this task.
Acknowledgements: This work is funded by the FWF
Austrian Science Fund Grant P20269 TransAgere and
the Know-Center. The Know-Center is funded within
the Austrian COMET Program.
REFERENCES
[1] A. Broder, A taxonomy of web search, SIGIR Forum,
vol. 36, no. 2, pp. 3-10, 2002.
[2] J. Cohen. A coefficient of agreement for nominal scales.
Educational and Psychological Measurement, (20)1:37,
1960.
[3] K.Y. He, Y.S. Chang and W.H. Lu. Improving
identification of latent user goals through search-result
snippet classification. In Proceedings of the
International Conference on Web Intelligence, 683-686,
IEEE Computer Society, 2007.
[4] B.J. Jansen, D.L. Booth and A. Spink. Determining the
informational, navigational, and transactional intent of
web queries. Information Processing and Management,
(44)3:1251-1266, Elsevier, 2008.
[5] U. Lee, Z. Liu and J. Cho. Automatic identification of
user goals in web search. In Proceedings of WWW
2005, ACM Press, New York, USA, 2005.
[6] D.B. Lenat. CYC: A large-scale investment in
knowledge infrastructure. Communications of the ACM,
(38)11:33-38, 1995.
[7] H. Liu and P. Singh. ConceptNet - A practical
commonsense reasoning tool-kit. BT Technology
Journal, (22)4:211-226, 2004.
[8] G. Pass, A. Chowdhury and C. Torgeson. A picture of
search. In Proceedings of the 1st International
Conference on Scalable Information Systems, ACM
Press New York, NY, USA, 2006.
[9] M. Strohmaier, P. Prettenhofer and M. Lux. Different
degrees of explicitness in intentional artifacts - studying
user goals in a large search query log. In Proceedings of
the CSKGOI'08 Workshop on Commonsense
Knowledge and Goal Oriented Interfaces, held in
conjunction with IUI'08, Canary Islands, Spain, 2008.
... Results from a larger human subject study corroborate the existence of these two classes and furthermore hint towards a theoretical separability (Strohmaier et al., 2008). To tap into Search Query Logs for knowledge acquisition purposes, we propose to automatically identify and extract queries which contain explicit goals. ...
... We refer to the task of acquiring goals from textual resources as Goal Mining. This problem covers a broad range of interesting aspects, including the acquisition of goals from scientific articles (Hui & Yu, 2005), organizational policies ( Potts et al., 1994), organizational guidelines and procedures ( Liaskos et al., 2006), Search Query Logs ( Strohmaier et al., 2008) and others. ...
... A counterexample is represented by the query living on the moon. It is important to note that it would be rather difficult to completely verify this assessment solely based on data from an anonymous query log due to the inherent goal verification problem of such a task ( Strohmaier et al., 2008). However, the objectives of our work are more modest: we are interested in acquiring plausible human goals for knowledge acquisition purposes. ...
Article
A better understanding of what motivates humans to perform certain actions is relevant for a range of research challenges including generating action sequences that implement goals (planning). A first step in this direction is the task of acquiring knowledge about human goals. In this work, we investigate whether Search Query Logs are a viable source for extracting expressions of human goals. For this purpose, we devise an algorithm that automatically identifies queries containing explicit goals such as find home to rent in Florida. Evaluation results of our algorithm achieve useful precision/recall values. We apply the classification algorithm to two large Search Query Logs, recorded by AOL and Microsoft Research in 2006, and obtain a set of ∼110,000 queries containing explicit goals. To study the nature of human goals in Search Query Logs, we conduct qualitative, quantitative and comparative analyses. Our findings suggest that Search Query Logs (i) represent a viable source for extracting human goals, (ii) contain a great variety of human goals and (iii) contain human goals that can be employed to complement existing commonsense knowledge bases. Finally, we illustrate the potential of goal knowledge for addressing following application scenario: to refine and extend commonsense knowledge with human goals from Search Query Logs. This work is relevant for (i) knowledge engineers interested in acquiring human goals from textual corpora and constructing knowledge bases of human goals (ii) researchers interested in studying characteristics of human goals in Search Query Logs.
... Moreover, goal search can take advantage of previous queries that the user placed on the search engine. Some interesting work on this topic has already been done by M. Strohmaier and his colleagues [41, 79]. We also need to experiment further with techniques for elicitation, capture, review and maintenance of user preferences, since these are paramount for the success of our proposed methodology. ...
Conference Paper
Full-text available
The personal web vision promises to give users a highly personalized experience on the web. This paper proposes and describes a Personal Web Workflow Methodology, designed to elicit, operationalize and execute a personal web user's goals. Our approach relies heavily on our prior research in goal modeling and operationalization, model matching and merging, and web service monitoring and recovery. We integrate this research with the social networking concept of crowd-sourcing to create a novel methodology for allowing users to produce customized workflows in order to accomplish their unique goals.
Conference Paper
People willingly provide more and more information about themselves on social media platforms. This personal information about users’ emotions (sentiment) or goals (intent) is particularly valuable, for instance, for monitoring tools. So far, sentiment and intent analysis were conducted separately. Yet, both aspects can complement each other thereby informing processes such as explanation and reasoning. In this paper, we investigate the relation between intent and sentiment in weblogs. We therefore extract ~90,000 human goal instances from the ICWSM 2009 Spinn3r dataset and assign respective sentiments. Our results indicate that associating intent with sentiment represents a valuable addition to research areas such as text analytics and text understanding.
Chapter
Over the last years we have observed a remarkable shift of media spendings from offline brand building activities to online performance advertising as well as a noticeable increase in “green marketing” efforts and sustainability communication by companies of various branches. In this paper we bring these two research streams together. We develop and perform a non reactive A/B-test that enables us to evaluate the influence of sustainability information on the customers decision to buy a product by clicking on an ad on a search engine results page (SERP). We analyze campaign performance data from a European e-commerce retailer, apply a Bayesian parameter estimation to compare the two groups, and discuss the implications of the results.
Chapter
Full-text available
This paper focusses on conceptualizing the quantification of the Carbon Footprint of IT-Services (CFIS). Initially, the increasing relevance of Carbon Footprint to the IS-community is pointed out. Based on literature review, we pre-sent related work that describes underlying concepts e.g. the Carbon Footprint of Products, Life Cycle Assessment as well as IT energy and performance measure-ment. We apply a transfer-oriented approach (design science) to propose a meth-odological framework for CFIS that is based on the phases of Life Cycle Assess-ment, and furthermore provide an example for the calculation. To our opinion the conceptualization of CFIS is an inevitable step to advance Green IS, since it quantifies dependencies between IT-Services, IT energy consumption and related greenhouse gas emissions. Thus, the paper contributes to the IS community by providing an applicable and novel method to IT service providers for calculating the CFIS and by identifying further important research directions in this field.
Chapter
Access to knowledge about user goals represents a critical component for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the manual acquisition of knowledge about user goals is costly and often infeasible. In a departure from existing approaches, this paper proposes Goal Mining as a novel perspective for knowledge acquisition. The research presented in this chapter makes the following contributions: (a) it presents Goal Mining as an emerging field of research and a corresponding automatic method for the acquisition of user goals from web corpora, in the case of this paper search query logs (b) it provides insights into the nature and some characteristics of these goals and (c) it shows that the goals acquired from query logs exhibit traits of a long tail distribution, thereby providing access to a broad range of user goals. Our results suggest that search query logs represent a viable, yet largely untapped resource for acquiring knowledge about explicit user goals.
Article
Full-text available
In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.
Article
Full-text available
Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.
Conference Paper
On the web, search engines represent a primary instrument through which users exercise their intent. Understanding the specific goals users express in search queries could improve our theoretical knowledge about strategies for search goal formulation and search behavior, and could equip search engine providers with better descriptions of users’ information needs. However, the degree to which goals are explicitly expressed in search queries can be suspected to exhibit considerable variety, which poses a series of challenges for researchers and search engine providers. This paper introduces a novel perspective on analyzing user goals in search query logs by proposing to study different degrees of intentional explicitness. To explore the implications of this perspective, we studied two different degrees of explicitness of user goals in the AOL search query log containing more than 20 million queries. Our results suggest that different degrees of intentional explicitness represent an orthogonal dimension to existing search query categories and that understanding these different degrees is essential for effective search. The overall contribution of this paper is the elaboration of a set of theoretical arguments and empirical evidence that makes a strong case for further studies of different degrees of intentional explicitness in search query logs.
Conference Paper
We survey many of the measures used to describe and evaluate the efficiency and effectiveness of large-scale search services. These measures, herein visualized versus verbalized, reveal a domain rich in complexity and scale. We cover six principle facets of search: the query space, users' query sessions, user behavior, operational requirements, the content space, and user demographics. While this paper focuses on measures, the measurements themselves raise questions and suggest avenues of further investigation.
Conference Paper
There has been recent interests in studying the "goal" behind a user's Web query, so that this goal can be used to improve the quality of a search engine's results. Previous studies have mainly focused on using manual query-log investigation to identify Web query goals. In this paper we study whether and how we can automate this goal-identification process. We first present our results from a human subject study that strongly indicate the feasibility of automatic query-goal identification. We then propose two types of features for the goal-identification task: user-click behavior and anchor-link distribution. Our experimental evaluation shows that by combining these features we can correctly identify the goals for 90% of the queries studied.
Conference Paper
In this paper, we propose an enhanced approach to improving our previous method which employs syntactic structures (verb-object pairs) to identify latent user goals. Our new approach employs a supervised-learning method to learn hint verbs and considers URL information and title information to classify snippets into three coarse categories, which are resource-seeking, informational, and navigational. Also, we propose three different models to identify three different categories of specific latent user goals from the classified snippets.
Article
We describe ConceptNet, a freely available semantic network presently consisting of over 250,000 elements of commonsense knowledge. Inspired by Cyc, ConceptNet includes a wide range of commonsense concepts and relations, and inspired by WordNet, it is structured as a simple, easy-to-use semantic network. ConceptNet supports many of the same applications as WordNet, such as query expansion and determining semantic similarity, but it also allows simple temporal, spatial, affective, and several other types of inferences. This paper is structured as follows. We first discuss how ConceptNet was built and the nature and structure of its contents. We then present the ConceptNet toolkit, a reasoning system designed to support textual reasoning tasks by providing facilities for spreading activation, analogy, and path-finding between concepts. Third, we provide some quantitative and qualitative analyses of ConceptNet. We conclude by describing some ways we are currently exploring to improve ConceptNet.