BOOSTING ITEM FINDABILITY: BRIDGING THE SEMANTIC
GAP BETWEEN SEARCH PHRASES AND ITEM INFORMATION
Hasan Davulcu, Hung V. Nguyen, Viswanathan Ramachandran
Department of Computer Science and Engineering, Arizona State University Tempe, AZ 85287, USA
Keywords: E-commerce, Data Mining, Frequent Itemsets, Web Data, Information Retrieval, Information Extraction,
Relevance Feedback.
Abstract: Most search engines do their text query and retrieval based on keyword phrases. However, publishers
cannot anticipate all possible ways in which users search for the items in their documents. In fact, many
times, there may be no direct keyword match between a search phrase and descriptions of items that are
perfect “hits” for the search. We present a highly automated solution to the problem of bridging the
semantic gap between item information and search phrases. Our system can learn rule-based definitions that
can be ascribed to search phrases with dynamic connotations by extracting structured item information
from product catalogs and by utilizing a frequent itemset mining algorithm. We present experimental results
for a realistic e-commerce domain. Also, we compare our rule-mining approach to vector-based relevance
feedback retrieval techniques and show that our system yields definitions that are easier to validate and
perform better.
1 INTRODUCTION
Most search engines do their text query and retrieval
using keywords. The average keyword query length
is under three words (2.2 words (Crescenzi, 2000)).
Recent research (Andrews, 2003) found that 40
percent of companies rate their search tools as “not
very useful” or “only somewhat useful.” Further, a
review of 89 sites (Andrews, 2003) found that 75
percent have keyword search engines that fail to
retrieve important information and put results in
order of relevance; 92 percent fail to provide guided
search interfaces to help offset keyword deficiencies
(Andrews, 2003), and seven out of 10 web shoppers
were unable to find products using the search
engine, even when the items were stocked and
available.
The Defining Problem: Pub
lishers cannot
anticipate all possible ways in which users search for
the items in their documents. In fact, many times,
there may be no direct keyword match between a
search phrase and descriptions of items that are
perfect “hits” for the search. For example, if a
shopper uses “motorcycle jacket” then, unless the
publisher or search engine knows that every “leather
jacket” is a “motorcycle jacket”, it cannot produce
all matches for user’s search. Thus, for certain
phrases, there is a semantic gap between the search
phrase used and the way the corresponding matching
items are described. A serious consequence of this
gap is that it results in unsatisfied customers. Thus
there is a critical need to boost item findability by
bridging the semantic gap that exists between search
phrases and item information. Closing this gap has
the strong potential to translate web search traffic
into higher conversion rates and more satisfied
customers.
Issues in Bridging the Semantic Gap: We
denote a sea
rch phrase to be a “target search
phrase” if does not directly match certain relevant
item descriptions. The semantics of items matching
such “target search phrases” is implicit in their
descriptions. For phrases with fixed meanings i.e.
their connotations do not change such as in “animal
print comforter”, it is possible to close the gap by
extracting their meaning with a thesaurus (Voorhees,
1998) and relating it to product descriptions, such as
“zebra print comforter” or “leopard print bedding”
etc. Where they pose a more interesting challenge is
when their meaning is subjective, driven by
perceptions, and hence their connotations change
over time as in the case of “fashionable handbag”
and “luxury bedding”. The concept of a fashionable
handbag is based on trends, which change over time,
and correspondingly the attribute values
characterizing such a bag also changes. Similarly,
48
Davulcu H., V. Nguyen H. and Ramachandran V. (2005).
BOOSTING ITEM FINDABILITY: BRIDGING THE SEMANTIC GAP BETWEEN SEARCH PHRASES AND ITEM INFORMATION.
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 48-55
DOI: 10.5220/0002525800480055
Copyright
c
 SciTePress

the concept of “luxury bedding” depends on the
brands and designs available on the market that are
considered as luxury and their attributes. Bridging
the semantic gap therefore is in essence the problem
of inferring the meaning of search phrases in all its
nuances.
Our Approach: In this paper we present an
algorithm that (i) structures item information and (ii)
uses a frequent itemset mining algorithm to learn the
“target phrase” definitions.
2 RELATED WORKS
In (Aholen, 1998), generalized episodes and episode
rules are used for Descriptive Phrase Extraction.
Episode rules are the modification of association
rules and episode is the modification of frequent set.
An episode is a collection of feature vectors with a
partial order; authors claimed that their approach is
useful in phrase mining in Finnish, a language that
has the relaxed order of words in a sentence. In our
previous work (Nguyen, 2003), we present a co-
occurrence clustering algorithm that identifies
phrases that frequently co-occurs with the target
phrase from the meta-tags of Web documents.
However, in this paper we address a different
problem; we attempt to mine the phrase definitions
in terms of extracted item information, thus, the
mined definitions can be utilized to connect “search
phrases” to real items in all their nuances.
The frequent itemset mining problem is to
discover a set of items shared among a large number
of records in the database. There are two main
search strategies to find the frequent items set.
Apriori (Agrawal, 1994) and several other Apriori
like algorithms adopt Breadth-First-Search model,
while Eclat (Zaki, 2000) and FPGrowth (Han, 2000)
are well known algorithms that employ Depth-First
manner to search all frequent itemsets of a database.
Our algorithm also searches for frequent itemsets in
a Depth-First manner. But, unlike the lattice
structure used in Eclat or the conditional frequent
pattern tree used in FPGrowth, we propose the so
called 2-frequent itemset graph and utilize heuristic
syntheses to prune the search space in order to
improve the performance. We plan to further
optimize our algorithm and conduct detailed
comparisons to the above algorithms.
The relevance feedback (Salton, 1990) method
can also be used to refine the original keyword
phrase by using the document vectors (Baeza-Yates,
1999) of the extracted relevant items as additional
information. In Section 6, we present experimental
results and show that the rules that our system
learns, by utilizing the extracted relevant item
information, are easier to validate and perform better
than retrieval with the relevance feedback method.
3 SYSTEM DESCRIPTION
I. Item Name Structuring: This component takes a
product catalogue and extracts structured
information for mining the phrase based and
parametric definitions. Details are discussed in
Section 4.
II. Mining Search Phrase Definitions: In this
phase, we divide the phrase definition mining
problems into two sub problems (i) mining the
parametric definitions from extracted attribute value
pairs of items, and (ii) mining phrase based
definitions from the long item descriptions. Details
are discussed in Section 5.
4 DATA LABELING
This section presents the techniques for an e-
commerce domain, for the sake of providing
examples. Our techniques can be customized for
different domains. The major tasks in this phase are
structuring and labeling of extracted data. The
readers are also referred to (Davulcu, 2003) for more
information in details.
4.1 Labeling and Structuring
Extracted Data
This section describes a technique to partition the
short product item names into their various
attributes. We achieve this by grouping and aligning
the tokens in the item names such that the instances
of the same attribute from multiple products fall
under the same category indicating that they are of
similar types.
The motivation behind doing the partition is to
organize data. By discovering attributes in product
data and arranging the values in a table, one can
build a search engine which can enable quicker and
precise product searches in an efficient way.
4.2 The Algorithm
Before proceeding to the algorithm, it helps to
identify item names as a sequence of tokens obtained
when white-space is used as a delimiter. Since the
sequences of tokens obtained from item names are
BOOSTING ITEM FINDABILITY: BRIDGING THE SEMANTIC GAP BETWEEN SEARCH PHRASES AND ITEM
INFORMATION
49

all from a single web page and belong to the same
category, they are likely to have a similar pattern. As
mentioned before, our algorithm is designed to
process collections of such item names without any
labeling whatsoever. So it can be performed on the
fly as and when data is extracted from the web sites.
Following are the general properties of the data our
algorithm can process:
Super-Tokens: Any pair of tokens t
1
, t
2
that always
co-occur together and occur more than once belong
to a multi token instance of a type.
Context: All single tokens occurring between
identical attribute types belong to the same type.
This means that if two tokens t
1
and t
2
from distinct
item names occur in between same types T
L
and T
R
then they should be of the same type.
Anchor Type: A token that uniquely occurs within
all item names should belong to a unique type,
which we call an Anchor Type.
Density: Attribute types should be densely
populated. Meaning that, every type should occur
within the majority of item names.
Ordering: Pairwise ordering of all types should be
consistent within a collection.
Tokenization: The item names are tokenized by
using white space characters as delimiters. Tokens
are stemmed so using the Porter Stemmer (Porter,
1980).
Super Tokenization: The second step identifies
multi-token attributes.
Initialization of Types: To initialize, every item
name is prefixed and suffixed with a Begin and an
End token.
Context Based Inference: This step aligns tokens
from different item names under a single type. This
step takes advantage of tokens repeating across
descriptions and operates based on the first
assumption, Context, that tokens within similar
contexts have similar attribute types.
If a token sequences t
x
,t, t
y
and t'
x
, t', t'
y
exist in
D such that t
x
, t'
x
∈T
p
and t
y
, t'
y
∈T
q
, then combine
and replace the types of tokens t and t' with a new
type T
n
= Typeof(t) U Typeof(t') .
Type Ordering: In this step, the set of inferred
types T are sorted based on their ordering in the
original item names. We utilize the Pairwise Offset
Difference (POD) metric to compare different types.
POD between types T
i
and T
j
is defined as:
where f
x
is the token offset of x from the start of
its item name and f
y
is the token offset of y. If this
value is greater than zero, then the type T
i
comes
after type T
j
in the sorted order.
Due to space constraints, tokens have been
aligned such that those from the same type are offset
at the same column. The type numbers the tokens
belong to are indicated at the top.
___________________________________
Algorithm 1: Item Name Partition
Type Merging: A careful observation shows that
some of the neighbouring types are fillers for each
other. Meaning that, they are not instantiated
together for any item name. Such types are
candidates for merging and are called merge
compatible. Merging at this point is logical because
of our assumption that the types are densely
populated.
Merge Concatenation: Finally, merge-
concatenation is performed to eliminate sparsely
populated types. Sparsely populated types are those
with a majority of missing values. By our
assumption, collections of item names should have
dense attributes. This implies that the tokens of a
sparsely populated type should be concatenated with
the values of one of the neighbouring types.
4.3 Experimental Results
To evaluate the algorithm, our DataRover system
was used to crawl and extract list-of-products from
the following five Web sites: www.officemax.com,
(1)
ICEIS 2005 - SOFTWARE AGENTS AND INTERNET COMPUTING
50

www.officedepot.com, www.acehardware.com,
www.homeclick.com and www.overstock.com.
Three metrics were used to measure the
effectiveness of the algorithm. The first two evaluate
the ability to identify fragments of the descriptions
to the correct type and the last one indicates the
correctness of the number of attributes.
Precision indicates how correctly type-value
pairs are identified.
Recall This quantity indicates if every
existing type-value pair is being identified.
Attributes Error Rate indicates the error in
the number of attributes described in the set of
product names.
Table 1: Summary of Evaluation Measures for Different
Web Sites for the Items Name Structuring Algorithm
5 MINING THE DEFINITION OF A
TARGET PHRASE
In this section, we introduce the problem of mining
definitions of a phrase from product data extracted
from the matching Web pages. Using extraction
techniques discussed in Section 4 we can retrieve
tabular parametric attributes of matching products as
well as their long descriptions. Next, we apply
frequent itemset mining algorithms to learn the
parametric definitions and phrase-based definitions
of target phrases from the extracted product data.
First, in Sections 5.1 thru 5.4 we introduce an
algorithm that finds all frequent itemsets from a
database. Section 5.5 discusses the problem of
mining parametric definitions. In Section 5.6 textual
definition mining is discussed. Since their
introduction in 1994 by Agrawal et al. (Agrawal,
1994), the frequent itemset and association rule
mining problems have received a lot of attention
among data mining research community. Over the
last decade, many research papers (Han, 2001) have
been published presenting new algorithms as well as
improvements on existing algorithms to tackle the
efficiency of frequent itemset mining problems. The
frequent itemset mining problem is to discover a set
of items shared among a large number of transaction
instances in the database. For example, consider the
product information database matching ‘trendy
shoes’ that we extract from retail Web sites. Here,
each instance represents the collection of product’s
<attribute, value>pairs for attributes such as brand,
price, style, gender, color and description. The
discovered patterns would be the set of <attribute,
value> pairs that most frequently co-occur in the
database. These patterns define the parametric
description of the target phrase ‘trendy shoe’.
5.1 Boolean Representation of the
Database
The advantage of Boolean representation is that
many logical operations such as superset, subset, set
subtraction, OR, XOR, etc between any number of
attribute vectors can be performed extremely fast.
5.2 Constructing 2-frequent Itemsets
Graph
The set of 2-frequent itemsets plays crucial role in
finding all frequent itemsets. The main idea is that,
from the observation that if {I
i….
I
j
} is a frequent
itemset then all pairs of items in this set must also be
a frequent itemset. Using this property of a frequent
set, our algorithm will first create a graph that
represents the 2-frequent itemsets among all items
that satisfy the minimum support threshold.
The the 2-frequent itemset graph is the directed
graph G(V,E) which is constructed as follows:
V = I; I is the set of items that satisfy the
minimum support in database D.
E = {(v
i
,v
j
) | {i,j} is a 2-frequent itemset and i<j).
We sort the frequent single items into
lexicographical order and for a 2-frequent itemset,
we construct a directed edge from the node (item)
whose index is lower to the node whose index is
higher.
BOOSTING ITEM FINDABILITY: BRIDGING THE SEMANTIC GAP BETWEEN SEARCH PHRASES AND ITEM
INFORMATION
51

Example 1.
I
1
I
2
I
3
I
4
1 1 1 0
0 1 1 1
1 0 1 0
1 1 0 0
0 0 1 1
1 1 0 0
0 1 0 1
1 0 0 1
Figure 1: Database I and its 2-frequent item graph.
For this database, if minimum support δ is set to
25%, then the 2-frequent itemsets are I
1
I
2,
I
2
I
3
, I
2
I
4
,
I
3
I
4
. The 2-frequent itemsets graph would be as in
Figure 1.
5.3 Searching for Frequent Itemsets
The algorithm iteratively starts from every node in
the graph and recursively traverses depth-first to its
descendants. At any step k (k>1), the algorithm will
choose to go to a child node v of the current node so
that the path from the beginning node to v forms a k-
frequent itemset. If so, the algorithm will continue
expand to v’s children to search for (k+1)-frequent
itemset and so on. There are several algorithms [8,
16] that generate frequent itemsets in depth-first
manner. A distinguishing feature of our algorithm is
that it searches on the 2-frequent itemset graph.
Finding all 2-frequent set takes O(n
2
) operations
where n is the number of frequent single items. Our
algorithm utilizes the following heuristics to guide
the search.
Heuristic 1: At step k, choose only children
nodes of node v
k-1
that have incoming degree greater
than or equal to the number of visited nodes,
counted from the beginning node. Incoming degree
of a node v, denoted as deg(v) is the number of
nodes that point to v. The meaning of this heuristic is
that, if deg(v) is smaller than the number of visited
nodes (nodes in the path) then there exists at least
one node among the set of previously visited k-1
nodes that does not point to v. In other words, there
exists at least one node in the current path that does
not form a 2-frequent itemset with v. Therefore the
k-1 nodes in the path (visited nodes) and v cannot
form a k-frequent itemset hence it is pruned out
without candidate itemset generation.
Heuristic 2: At step k, choose only children
nodes of node v
k-1
that have the set of incoming
nodes that is a superset of the set of all k-1 nodes in
the visited path. This heuristic, which is applied after
Heuristic 1, ensures that all previously visited nodes
in the current path, must point to the node in
consideration. This is also a necessary precondition
that each visited node forms a 2-frequent itemset
with the node in consideration.
Heuristic 1 is efficient since the 2-frequent
itemset graph is already constructed and the degree
of all nodes is stored before the search proceeds.
Heuristic 2 superset testing operation can also be
performed efficiently using the bit-vector
representation. Consequently, by utilizing these
heuristic estimates, we can prune a lot of nodes that
cannot be added to the visited nodes to form a
frequent itemset and eliminate a lot of candidate
itemset generation.
5.4 Mining Parametric Definition of
Phrases
Note that, since we extract data from the Web by
posing a search phrase query to a web search engine,
all the instances in the data we get contain search
phrase. Therefore, the association rule generation
becomes simple by just putting the search phrase
into the header of association rules and the body of
rules is frequent itemsets. The support of obtained
association rules equals to the support of frequent
items set in their body since for a rule, the search
phrase occurs in all instances that the frequent
itemset (in the body of the rule) occurs. Next, we
would like to utilize the extracted product
information to mine parametric phrase definition
rules made up from conjunctions of distinct
<attribute, value> pairs, like:
Trendy shoe ←
brand = Steve Madden,
Color = black,
material = leather
5.5 Mining Textual Definitions of
Target Phrases
Another resource of rich phrase definitions is the
long product descriptions of the matching products.
In the Section 4, we have already described how we
plan to collect long product descriptions from
product Web pages that matches a given target
search phrase. In this section we describe the
proposed algorithm for mining phrase definitions
ICEIS 2005 - SOFTWARE AGENTS AND INTERNET COMPUTING
52

that can connect hidden phrases to product
descriptions themselves. In order to generate
candidate phrases first we perform part-of-speech
(POS) tagging and noun and verb phrase chunking
(Finch, 1997) on the long description to obtain a
more structured textual description. Part-of-speech
(POS) tagging and chunking the above description
yields the following structure. In the next step, we
utilize the noun phrases as transaction instances and
mine frequently used phrases from all the noun
phrases of all the product descriptions that we have
collected from the Web documents.
___________________________________
Algorithm 2: Frequent Itemset Mining
Next, we use the mined frequent phrases as
items and create transaction instances by marking all
of the frequently used phrases matching anywhere in
the long description. This would yield transaction
instances made-up from frequently used phrases
matching the product descriptions.
Next we mine the frequent itemsets among
instances corresponding to the long descriptions to
find the phrase definitions. Note that, due to our way
to construct the items, all items are combinations of
single words; therefore, there are items that subsume
other items. As a subsequence, there are a lot of
redundant final resultant frequent itemsets. For
example a long description might yield the following
items: “suede”, “pump”, “suede pump”, “fashion”,
“savvy”, “woman”, “fashion savvy”, “savvy
woman”, “fashion savvy woman”. Hence, we only
want to mine the frequent itemset “suede pump”,
“fashion savvy woman” because these frequent
itemsets subsume the former frequent itemsets.
6 EXPERIMENTAL RESULTS
The tables below show some of the definitions that
were mined. It is a relatively easy task for a domain
expert to inspect and evaluate the quality of such
rule-based definitions.
6.1 Comparison to Relevance
Feedback Method
In order to compare the performance of our
definition miner to standard relevance feedback
retrieval method we mined a large database of shoes
(33,000 items) from a collection of online vendors.
Next, we keyword queried the database with the
target exemplary search phrase “trendy shoe”.From
the 166 keyword matching shoes, we mined rule-
based phrase definitions for “trendy shoes” yielding
rules such as fashionable sneaker, platform shoes
etc. that were validated by a domain expert. These
mined rules matched 3,653 additional shoes.
Alternatively, we also computed the relevance
feedback query vector using the above 166 matching
shoes. We also identified a similarity threshold by
finding the maximal cosine theta, Θ, between the
relevance feedback query vector and all of the 166
shoe vectors. Retrieval using the relevance feedback
vector with this threshold yields more than 29,000
matches out of 33,000! The light colored bars in
Figure 3 illustrates the histogram plot of the 29,293
instances that falls into various similarity ranges.
Similarly, the dark colored bars plots the similarity
ranges of the 3,653 shoes that were retrieved by
matching with our mined definitions. As can be seen
from the distributions in the above chart, the items
retrieved with our mined definitions have a very
uniform similarity distribution (with around 300 of
these being below the threshold), as opposed to
having a skewed distribution towards the higher
values of similarity. Since dark colored bars
correspond to relevant “trendy shoes” matching our
rules, which were validated by an expert, most of
BOOSTING ITEM FINDABILITY: BRIDGING THE SEMANTIC GAP BETWEEN SEARCH PHRASES AND ITEM
INFORMATION
53

these items should have ranked towards the higher
end of the similarity spectrum. However, relevance
feedback measure failed to rank them as such;
hence, it performed poorly for this task.
6.2 Comparison to Relevance
Feedback with LSI
The plot of similarity ranges obtained by ranking the
3,653 shoes, retrieved with our mined rules, using
relevance feedback with and without
latent semantic
indexing
(LSI) (Deerwester, 1990) technique is
shown in Figure 2. The light colored dashed line
represents the cosine theta threshold Θ for the
relevance feedback ranking, similarly the dark
colored dashed line represents the cosine theta
threshold for the relevance feedback with LSI. The
recall for relevance feedback is nearly 93%,
however, since it matches 88% of a random
collection of shoes, its precision is lower. On the
other hand, even though the ranking of relevance
feedback with LSI falls onto a higher similarity
range, it appears to have a much lower recall (of
25%) for this experiment with exemplary target
phrase “trendy shoes”.
7 CONCLUSIONS AND FUTURE
WORK
Our initial experimental results for mining phrase
definitions are promising according to our retail
domain expert who is the Webmaster of an affiliate
marketing web site. We plan to scale up our
experiments to hundreds of product categories and
thousands of phrases. Also, we would like to
perform experiments to determine how precisely our
algorithm learns the definitions of phrases that
changes their meaning over time.
Parametric Rules Support
Brand = Jil Sander, material = leather, type = clutch Î fashion handbags 4.25%
Brand = Carla, design = mancini, material = leather Î fashion handbags 2.4%
Brand = Butterfly, design =beaded Î fashion handbags 2.4%
Brand = Sven, material = leather Î fashion handbags 10.2%
Design = beaded, color = pink Î fashion handbags 2%
Fashion
handbags
Design = beaded, color = blue, type = tote Î fashion handbags 3.2%
Design = Baffled box, material = cotton Î luxury beddings 5%
Design = Waterford, material = linen Î luxury beddings 6%
Material = silk Î luxury beddings 3%
Luxury
beddings
Design = Sussex, material = polyester Î luxury beddings 6%
Design = All American, material = polyester Î sport beddings 6%
Design = All star, material = polyester Î sport beddings 9%
Design = Big and bold Î sport beddings 17%
Sport
beddings
Design = sports fan Î sport beddings 45%
Textual Rules Support
Egyptian cotton mate-lass Î luxury beddings 0.6%
Silk, smooth, King set Î luxury beddings 0.75%
Piece ensemble Î luxury beddings 0.75%
American sport ensemble Î sport beddings 0.4%
Paraphernalia sport Î sport beddings 0.6%
fashionable sneaker Î trendy shoes 7%
Wedge edge Î trendy shoes 5%
Platform shoes Î trendy shoes 6%
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54

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0
0.
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