A Novel Approach to Query Expansion based on Semantic Similarity
Measures
Flora Amato, Aniello De Santo, Francesco Gargiulo, Vincenzo Moscato, Fabio Persia,
Antonio Picariello and Giancarlo Sperli
DIETI, University of Naples ”Federico II”, Naples, Italy
Keywords:
Semantic Search, Query Expansion, Information Retrieval.
Abstract:
In this paper, we present a framework supporting information retrieval over corpora of documents using an
automatic sematic query expansion approach. The main idea is to expand the set of words used as query terms
exploiting the notion of semantic similarity between the concepts related to the search terms. We leverage
existing lexical resources and similarity metrics computed among terms to generate - by a proper mapping
into a vectorial space - an index for the fast retrieval of a set of terms “semantically correlated” to a given
query term. The vector of expanded terms is then exploited in the query stage to retrieve documents that are
significantly related to specific combinations of the query terms. Preliminary experimental results concerning
efficiency and effectiveness of the proposed approach are reported and discussed.
1 INTRODUCTION
As is known, semantic search aims at improving re-
trieval accuracy by understanding and exploiting the
“meaning” of the search terms (keywords) in order to
generate more relevant results with respect to users’
intent and needs.
The real limitation to adopting a semantic engine
for searches through the Web lies in the fact that:
• the majority of content available on the Web
which could be useful for indexing goals is still
in an unstructured shape and spread within a large
amount of web pages;
• each search term can assume several meanings de-
pending on the considered semantic domain, thus
arising additional disambiguation problems.
However, semantic search techniques turn out to
be very suitable for retrieval tasks over corpora of
semi-structured documents (e.g. clinical records, job
offers, etc.) related to specific domains whose lexicon
is rather restricted and where the semantic “meaning”
to the different terms is usually well-defined.
For this kind of documents, some of the existing
Information Retrieval (IR) techniques (Rinaldi, 2008;
Albanese et al., 2005; Amato et al., 2008) are quite
promising candidates to solve the semantic search
problem: they can effectively retrieve a set of ranked
objects based on their relevance respect to the query
in order to satisfy the user needs related to the topic of
interest. Indeed, common IR techniques used for such
purposes make use of two main approaches: query ex-
pansion and ontology-based.
Systems leveraging query expansion techniques
semantically “enrich” the user query adding words
that have semantic relationships (e.g. synonyms, hy-
pernyms, hyponyms, etc.) with the search terms us-
ing as support domain vocabularies or taxonomies or
ad-hoc lexical resources, for improving the effective-
ness of the information retrieval process (Carpineto
and Romano, 2012). Query expansion techniques are
quite simple to realize and their performances depend
on the “goodness” of expansion phase (Rivas et al.,
2014).
On the other hand, ontology-based techniques
(Amato et al., 2009; Fern
´
andez et al., 2011; Jain and
Singh, 2013) exploit ad-hoc ontologies (manually or
automatically built) representing the main concepts
and relationships for documents’ corpora in combi-
nation with standard domain ontologies according to
the linked data paradigm. Then, queries are properly
computed mapping the user’s request into the related
ontology concepts and leveraging the defined data and
object properties (Vallet et al., 2005; Castells et al.,
2007).
The main challenge when applying ontology-
based techniques is surely represented by the initial
phase of the ontology building: in fact, the ontology
344
Amato F., De Santo A., Gargiulo F., Moscato V., Persia F., Picariello A. and Sperlì G..
A Novel Approach to Query Expansion based on Semantic Similarity Measures.
DOI: 10.5220/0005579703440353
In Proceedings of 4th International Conference on Data Management Technologies and Applications (KomIS-2015), pages 344-353
ISBN: 978-989-758-103-8
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
has to be able to effectively capture and describe the
entire knowledge related to the corpus of documents.
Manual approaches can generate very effective on-
tologies but are usually time consuming and involve
complex activities. In turn, automatic approaches are
more efficient but can generate more approximate re-
sults that can influence the search effectiveness. Thus,
it is our belief that hybrid strategies should be pre-
ferred.
In this paper, we present a novel semantic ap-
proach to query expansion,with the aim of supporting
information retrieval over corpora of documents. In
particular, the main idea behind our work is to expand
the set of words used as query terms exploiting the
notion of semantic similarity between the concepts re-
lated to search terms and those associated to the avail-
able domain vocabularies or taxonomies.
More in details, in the indexing stage we leverage
one or more vocabularies or domain taxonomies and
a similarity metric among terms (e.g. Wu & Palmer if
we have a taxonomy) to generate by a proper mapping
into a vectorial space an index for the fast retrieval of
the set of terms “semantically correlated” to a given
query term. In our implementation, we use the k-d
Tree and its k-nearest neighbor or a range search ca-
pabilities. The vector of expanded terms is then ex-
ploited in the query stage to retrieve documents that
contain several combinations of possible query terms.
Finally, documents are opportunely ranked with re-
spect to initial user query.
The paper is organized as follows. Section 2
shows a brief motivating example for our work. Sec-
tion 3 describes the of some recent approaches for
automatic query expansion problem. Section 4 il-
lustrates the document semantic retrieval framework
with several implementation details. Section 5 reports
some preliminary experimental results, while Section
6 finally discusses some conclusions and the future
work.
2 MOTIVATING EXAMPLE
The diffusion of Web-centric services for the job mar-
ket is rapidly expanding all over the world, and en-
abling a significant part of the job demand to be
routed through Web portals, services, and applica-
tions. As a consequence, the Web is increasingly
used by both employers and job seekers to adver-
tise demand and supply, and it is becoming increas-
ingly important to find new ways of enhancing the
recruitment-related activities.
In particular, there is the need for job seekers to re-
trieve job offers most representative of their curricu-
lum and skills as fast as possible, especially consid-
ering the increasing competition due to the ongoing
financial crisis. Moreover, to be effective the job re-
search must also be as complete as possible, in order
to help people make the best choice for them accord-
ing to their backgrounds, skills and ways of life.
Clearly, simple syntactic queries are highly inad-
equate to this goal: for instance, if a job seeker is a
computer science engineer and he should type just
’Computer Science Engineer’ while performing his
search, he would definitely miss offers which do not
esplicitly contain these words either in their titles or
in their description.
Our work is grounded in the belief that this is-
sue could be better addressed by exploiting semantic
search approaches instead. In fact, a query expansion
approach based on semantic techniques would allow
the seeker to also retrieve the offers related to words
like Java Programmer, Engineer, Linux Systems rather
than just Computer Science Engineer.
Thus, the main aim of this paper is to design
and develop a semantic framework for performing
query expansion tasks on large corpora of documents.
Specifically, we will focus the experimental phase on
a set of job related announcements in Italy.
3 STATE OF THE ART
Search Engines are essential tools for most computer
users in a wide variety of contexts. As a result, in-
formation retrieval has become an important field of
research over the last 30 years (Amato et al., 2014).
IR (Sagayam et al., 2012) is the process related to
the organization and retrieval of information from a
large number of documental corpus.To this purpose,
many document indexing and retrieval systems have
been proposed which have been shown to be gener-
ally effective. However, a deeper analysis reveals that
even though these techniques improve average perfor-
mance, there is often wide variation in the impact of
each chosen technique on the retrieval effectiveness
for specific queries. Particularly, the problem known
as the vocabulary problem - consisting in the index-
ers and the users not using the same word set (Furnas
et al., 1987) - is the most critical issue for retrieval ef-
fectiveness. Furthermore, synonymy (different words
with the same or similar meanings, such as tv and tele-
vision) together with word inflections (such as with
plural forms,television versus televisions), may result
in a failure to retrieve relevant documents, with a de-
crease in recall (the ability of the system to retrieve
all relevant documents) while polysemy (same word
with different meanings, such as java) may cause re-
ANovelApproachtoQueryExpansionbasedonSemanticSimilarityMeasures
345
trieval of erroneous or irrelevant documents, thus im-
plying a decrease in precision (the ability of the sys-
tem to retrieve only relevant documents) (Moscato
et al., 2010b; Moscato et al., 2010a).
To cope with the vocabulary problem , different
approaches have been proposed: among these we can
list interactive query refinement, relevance feedback,
word sense disambiguation and search results cluster-
ing. One of the most common techniques is to ex-
pand the original - typically short - query through the
addition of related keywords to it. These additional
keywords (or expansion terms) generally increase the
likelihood of a match between the query and relevant
documents during retrieval, thereby improving user
satisfaction.
Automatic Query Expansion (AQE) has a long his-
tory in IR, as has been suggested since 1960 by Maron
and Kuhns (Maron and Kuhns, 1960).
Following (Carpineto and Romano, 2012) AQE
techniques can be classified into five main groups ac-
cording to the conceptual paradigm used for finding
the expansion features: linguistic methods, corpus-
specific statistical approaches, query-specific statis-
tical approaches, search log analysis and Web data.
Linguistic methods leverage global language proper-
ties such as morphological, lexical, syntactic and se-
mantic word relationships to expand or reformulate
query terms. They are typically based on dictionar-
ies, thesauri, or other similar knowledge representa-
tion sources such as WordNet. These techniques are
usually generated independently of the full query and
of the content of the database being searched, and they
are usually more sensitive to word sense ambiguity.
In (Buey et al., 2014), Buey et al. present a
semantic query expansion methodology called SQX-
Lib, combining techniques such as lemmatization,
NER and semantics for information extraction from
a relational repository. The proposal includes a dis-
ambiguation engine that calculates the semantic rela-
tions between words and - in case any ambiguity is
found - selects the best meaning from those available
for the specific word. SQX-Lib has been integrated
by a major Media Group in Spain.
Dalton et al. (Dalton et al., 2014) propose an AQE
method based on annotations of entities from large
general purpose knowledge bases, such as Freebase
and the Google Knowledge Graph. They proposed a
new technique, called Entity Query Feature Expan-
sion (EQFE) which enriches the query with features
from entities and their links to knowledge bases, in-
cluding structured attributes and text. One limitation
of this work is that it depends upon the success and
accuracy of the entity annotations and linking.
Bouchoucha et al. (Bouchoucha et al., 2014)
present a unified framework to integrate multiple re-
sources for a Diversified Query Expansion. By imple-
menting two functions, one to generate candidate ex-
pansion terms and the other to compute the similarity
between two terms, any resource can be plugged into
the framework. Experimental results show that com-
bining several complementary resources performs
better than using one single resource.
In (Pal et al., 2014), a new way of using WordNet
for query expansion is proposed with a combination
of three AQE methods that take into account different
aspects of a candidate expansion term’s usefulness.
For each candidate expansion term, this method con-
siders its distribution, its statistical association with
query terms, and also its semantic relation with the
query.
Corpus-specific statistical approaches analyze the
contents of a full database to identify features used
in similar ways. Most early statistical approaches
to AQE were corpus-specific and generated correla-
tions between pairs of terms by exploiting term co-
occurrence, either at the document level, or to better
handle topic drift, in more restricted contexts such as
paragraphs, sentences, or small neighborhoods.
In (Huang et al., 2013), the authors propose a
new method - called AdapCOT - applyingadaptive co-
training to select feedback documents for boosting ef-
fectiveness. Co-training is an effective technique for
classification over limited training data, which is par-
ticularly suitable for selecting feedback documents.
The proposed AdapCOT method makes use of a small
set of training documents, and labels the feedback
documents according to their quality through an it-
erative process.
Colace et al. (Colace et al., 2015) use a minimal
relevance feedback to expand the initial query with a
structured representation composed of weighted pairs
of words. Such a structure is obtained from the rele-
vance feedback through a method for pairs of words
selection based on the Probabilistic Topic Model. The
proposed approach computes the expanded queries
considering only endogenous knowledge.
Query-specific techniques take advantage of the
local context provided by the query. They can be more
effective than corpus-specific techniques because the
latter might be based on features that are frequent in
the collection but irrelevant for the query at hand.
Ermakova et al. (Ermakova et al., 2014) propose a
AQE method that estimates the importance of expan-
sion candidate terms by the strength of their relation
to the query terms. The method combines local anal-
ysis and global analysis of texts.
Yang et al. (Yang et al., 2014) suggest a method
that applies a linguistic filter and a C-value method to
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346
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82"&#()*9,/:6%/(
1%-23"/+(
;"+&6"<,=(
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Figure 1: Workflow.
extend the query terms, and then uses the Normalized
Google Distance-based method to calculate the term
weight and choose Top-N terms as extended query.
The authors claim that the Normalized Google Dis-
tance (NGD) with some global factors enhance the
relevance between initial query and extended query,
and improve the accuracy of the search results of the
expert finding system.
Search log analysis paradigm is based on analysis
of search logs. The idea is to mine query associations
that have been implicitly suggested by Web users.
Finally, Web data techniques rely on the presence
of anchor texts. Anchor texts and real user search
queries are very similar because most anchor texts are
succinct descriptions of the destination page.
4 DOCUMENT RETRIEVAL
FRAMEWORK
In this section, we first provide a brief overview of
the AQE process workflow which, as shown in Fig-
ure 1, can be divided into the following three steps:
Keyword Extraction & Document Tagging, Semantic
Query Expansion and Document Retrieval.
The basic idea behind our proposal is that the re-
sults of a user’s query on a document corpus from a
specific domain could be significantly improved by
expanding the set of words used to search the corpus
of documents at hand through a process exploiting se-
mantic proximity between concepts.
Thus, the input of this process is a Document Cor-
pus - a set of documents related to a relevant topic-
which will be firstly processed through a Keyword ex-
traction & Document Tagging step.
In fact, we need not only to identify the concepts
(also, keywords) that can be relevant to each docu-
ment, but we also to limit the set of words that users
could use to query the corpus, thus identifying all pos-
sible words that need to be semantically linked to-
gether.
Therefore, in a first step each document belong-
ing to the corpus is parsed by proper NLP algorithms
in order to identify the salient words in the text and
link them to the document itself enriching its content
through a tagging process.
At this point, the Semantic Query Expansion step
will allow us to semantically enrich the user query
in order to improve the performance of the document
retrieval process by enlarging its semantic scope. In
particular, there are three main phases of the semantic
expansion process:
1. In the first phase, semantic distances between the
keywords recognized in the first step are com-
puted, based on different semantic similarity mea-
sures (Resnik, 1999) - such as Wu and Palmer,
Resnik or Leacock and Chodorow. A formalized
description of the knowledge of the domain - such
as ad hoc ontologies, taxonomies or vocabularies
- is needed for these measures to work effectively;
2. then, the keywords and their related distances -
computed as output of previous bloc - are em-
bedded in a data structure accurately chosen to
efficiently support information retrieval tasks on
large sets of data: for instance, m-tree, kd-tree, or
different kinds of NO-SQL databases, such graph-
databases, are particularly appropriate for these
kind of applications;
3. The last phase is the actual query expansion step,
which allows to enrich the scope of the user query
through appropriate operations performed on the
data structure.
Finally, the Documental Retrieval phase returns a
set of documents (Query Results) significantly related
to the user query by exploiting the semantic expansion
operated through the data structure and the tagging of
the documents resulting from the NLP stage.
4.1 System Architecture
The proposed system has been developed according
to the architecture shown in Figure 2. In particular we
can distinguish three main sections in the framework
structure.
The first section is represented by an NLP Module
devoted to extracting significant keywords from the
set of documents we are interested in querying and,
subsequently, to tagging each document with the re-
lated relevant keywords.
ANovelApproachtoQueryExpansionbasedonSemanticSimilarityMeasures
347
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."/012*(
3+42$'&1%(
(5(
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8$--,%-(
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3+:$%;,1%(
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8$+1%1#/(
97"2/(
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<";7>4;(
?12:7;(
8$--"*(
?12:7;(
Figure 2: System Architecture.
A second section is represented by the Semantic
Expansion Module, which is undoubtedly the central
feature of our proposal. It is composed by a subcom-
ponent devoted to building (in the indexing stage) a
semantic indexing of the extracted keywords exploit-
ing a chosen taxonomy or vocabulary, and of a sub-
component devoted to the actual query expansion task
(in the querying stage).
Synthetically, this macro-module takes as its in-
puts the set of keywords extracted from the corpus of
documents and the query the user wants to perform on
documents’ set, while its output is a set of keywords
semantically linked to the original query.
Finally, a Document Retrieval Module takes as in-
put the expanded query and the tagged corpus and
returns the documents matching the set of expanded
keywords.
In what follows, we will describe each of these
module in more detail.
4.1.1 NLP Module
The first thing we need for our framework to work
efficiently is to extract a set of significant keywords
from the documents we are interested in retrieving.
These keywords are those which can be chosen by
users to express their queries on the document set.
Therefore, we want the NLP module to imple-
ment an extraction technique able to retrieve signifi-
cant keywords from the documents, while keeping the
set of possible keyword restricted to those terms users
will adopt while querying the corpus.
In this work, we decided to implement a method-
ology previously proposed by some of the authors in
the context of ontology population for e-health appli-
cations (Amato et al., 2015b). In particular, the work
was aimed at using techniques from natural language
processing for populating and enriching an ontology
descriptive of the domain of Medical records in Cam-
pania region starting from a set of Medical records
and from a simple Ontology for the medical record
domains built with the help of a domain experts.
Essentially, the authors start from the set of med-
ical records available to them in free text, and obtain
structured text in the form of RDF triples through
lexicon-grammar based NLP techniques exploiting
the NooJ environment
1
for domain specific informa-
tion retrieval. The local grammars needed for the in-
formation retrieval task can be built with the help of
both linguists and domain experts.
This approach is particularly interesting to us,
since it allows the retrieval of keywords through the
definition of lexicon-grammars built upon a taxonomy
specifically chosen for the domain of interest. For
each document, this methodology will produce a set
of relevant keywords descriptive of the document and
limited to the vocabulary of the taxonomy. Once a list
of keywords is extracted from the document, it is also
added to the document description through a tagging
procedure. The tagging of the documents will later
ease the querying of the corpus and the retrieval task.
4.1.2 Semantic Expansion Module
Let us recall that the purpose of our framework is to
implement an effective way for semantically expand-
ing a user query on a set of documents, in order to
improve the retrieval of significant elements in large,
domain specific corpora.
Thus, the set of keywords produced by the NLP
1
http://www.nooj4nlp.net/
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348
module and the taxonomy/vocabulary used to guide
the NLP task are used as input to a Semantic Expan-
sion Module. This module implements the central
features of our framework: namely, the building of
a data structure indexing in a vectorial space the ex-
tracted keywords by means of semantic distances and
the expansion of a user generated query through the
retrieval of a set of semantically related keywords.
First of all, we are interested in recognizing the se-
mantic relationships between each every word in the
keyword list and in using them to compute distances
between those words.
Since by construction each term in the key-
word set is matched by a concept in the taxon-
omy/vocabulary used in the NLP stage, we can use the
available taxonomy to compute semantic distances
between the keywords.
There are several metrics that can be exploited to
compute a semantic distance between two concepts in
a taxonomy, every one of them having its advantages
and disadvantages (Resnik, 2011). Although the main
features of the proposed framework are not dependent
on the metric chosen to compute the distances, the re-
sults of the document retrieval phase could indeed be
affected by the goodness of the metrics, often depen-
dent on the available related taxonomy/vocabulary.
While the choice of the metric to adopt may vary
from domain to domain, in this first implementation
we decided to perform our experiments exploiting a
distance based on the Wu-Palmer similarity measure .
At this point, having applied a semantic distance
to the word list, we have put the whole set of words
into a metric space. There are several possible data
structures which will allow us to exploit this space for
targeted information retrieval. However, to fully ex-
ploit the potential of querying a list of words seman-
tically linked to each other, we chose to implement a
semantic index.
In particular, in this paper we propose to use
SemTree, a semantic index for supporting retrieval
of information from huge document collections, pro-
posed by some of the authors in (Amato et al., 2015a).
Essentially, SemTree is a distributed version of Kd-
Trees for information retrieval, leveraging the map-
ping of words in a vector space by means of a proper
semantic distance between concepts in a taxonomy.
In a first stage, our experiments will be run us-
ing a sequential implementation of a standard kd-
tree. However, using the distributed implementa-
tion of SemTree, we will also present several experi-
ments on a distributed cluster showing the efficiency
of the distributed approach in managing big amounts
of data.
Note that, to effectively implement this index, we
first need to map the set of words from a metric space
to a vector space. This can be done through the use of
several existing algorithms, such as FastMap or MDS
(Faloutsos and Lin, 1995).
In fact, both these methods are able to map ob-
jects into points in some n-dimensional space (where
n is user-defined, and in this application will be set to
3), such that the dis-similarities between objects are
preserved.
Experiments on real and synthetic data have
shown that FastMap is faster than MDS (being lin-
ear, as opposed to quadratic, on the database size N),
while the latter is more accurate in preserving dis-
tances between objects and the overall structure of the
data-set. Thus, feeding the list of word and an accu-
rately chosen semantic metric to one of these algo-
rithm will produce a set of points in an n-dimensional
space, each point representing one of the words from
the original list.
4.1.3 Document Retrieval Module
Leveraging a chosen semantic measure and the kd-
tree based index, we are now able to produce - through
a simple search on the tree - a set of words which
are semantically similar to the one the user choose to
query.
Furthermore, both the kd-tree’s k-nearest and
range query produce results ordered according to
their relevance to the queried term. In particular, a
k-nearest query on the tree would produce the set of
k terms in the index which are closest to the query’s
original word. The list of words returned by an inter-
rogation of the tree is ranked according to the prox-
imity of each word to the initial term.
This ranking can be exploited in the document re-
trieval phase, where the rank of each word in the list
is used to assign a significancy score, representing the
related documents’ probability of being relevant with
respect to the expanded query. The actual query on
the corpus of interest is performed at this point.
Although the problem of ranking the results of an
expanded query is complex and - as shown in Sec-
tion 3 -currently widely discussed in the existing lit-
erature, in this first evaluation we decided to perform
a weighted AND combining-operation of the results,
exploiting each word’s semantic proximity.
Let us consider a list of expanded query terms
t
1
, ..., t
n
, numbered according to their relevance to the
original query term. Then, the expanded query is per-
formed as:
(t
1
AND ... AND t
m
) AND (t
m+1
OR ... OR t
n
)
where t
1
, ..., t
m
are the terms that have a semantic
proximity respect to the query term greater than a
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349
given threshold.
Significant documents are retrieved from the
tagged corpus through a matching phase with the key-
word list, each documents having been assigned a list
of tags representing the words more descriptive of the
document contents.
Obviously, the output to the user query is a ranked
set of documents, presented with significancy scores.
5 PRELIMINARY
EXPERIMENTAL RESULTS
In order to evaluate the goodness of our proposal, we
hereby present a preliminary experimental evaluation
of our protocol’s retrieval effectiveness, using stan-
dard evaluation metrics, such as precision and recall,
and efficiency.
5.1 Experimental Setup for Job
Announcements Retrieval
First of all, in order to estimate our framework ef-
fectiveness in retrieving relevant documents starting
from a single-term queries, we considered a dataset
of about 1000 documents concerning job announce-
ments in Italy, crawled from commonly used job
search engines.
In order to efficiently conduct the NLP phase, we
also exploited the Italian version of the ESCO taxon-
omy
2
. Thus, our first step was to process the corpus of
untagged documents as described in the previous sec-
tions; then we performed 100 k-nearest queries - each
of them starting from the input of a single job related
term chosen from those listed in the ESCO taxonomy.
Furthermore, we decided to evaluate the frame-
work efficiency focusing on the index’s retrieval times
- both for its sequential implementation and for its
distributed implementation, as presented in (Amato
et al., 2015a).
This choice of focusing on the index searching
times is supported by the fact that the NLP process-
ing phase and the index building have to be performed
just once for each considered document corpus, thus
the delays associated to these phases can be consid-
ered non-significant for an initial evaluation of the
framework.
5.2 Effectiveness
The effectiveness has been computed using the Pre-
cision/Recall metrics, by comparing the output of our
2
https://ec.europa.eu/esco/home
algorithms against a ground truth provided by human
annotators.
Here, we present a preliminary evaluation of our
approach effectiveness with respect to the described
case study. In particular, we want to show feasibil-
ity of the proposed framework in automatically find-
ing job announcements significantly related to the job
term a user chose to query.
Specifically, we performed 100 different k-nearest
queries using as query points a set of concepts ex-
tracted from the ESCO taxonomy (i.e. Ingegnere Elet-
tronico, Farmacista, Softwarista, Giardiniere).
In order to evaluate the output of our framework
we asked a group of human testers to perform a simi-
lar retrieval task by hand, namely searching the tagged
document corpus for job announcements semantically
related to the job they were asked to retrieve, each job
corresponding to one of the query we performed au-
tomatically through our framework.
Denoting with T the set of documents returned by
the k-nearest query related to a given target job (i.e.
the user searching for ingegnere) and with T
∗
the set
of documents provided by the human ground truth
when searching for the same job, Precision (P) and
Recall (R) were computed using:
P =
|T ∩ T
∗
|
|T |
R =
|T ∩ T
∗
|
|T
∗
|
Figure 3 shows the average Precision and Recall
values for the 100 k-nearest query cases we performed
on the the tagged document corpus of job announce-
ments, when varying k.
As expected, the lower is k, the higher is P and the
lower is R; vice-versa, when the value of k is high, the
value of R grows up while the value of P decreases.
5.3 Efficiency and Considerations on
Scalability Issues
In order to get an accurate evaluation on the efficiency
of our index for a big data scenario, we used SCOPE
3
, a General Purpose Supercomputing Infrastructure.
SCOPE is an infrastructure provided by the Infor-
mation System Center of University of Naples, com-
posed of about 300 computing nodes (64 bit Intel
Xeon with 8 GB RAM).
In particular, we used at most 65 processors for
evaluating the performances of our index holding a
maximum number of 8,000,000 points.
3
www.scope.unina.it
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0"
0.1"
0.2"
0.3"
0.4"
0.5"
0.6"
0.7"
0.8"
0.9"
1"
10" 20" 30" 40" 50"
Value&
K&
Precision"
Recall"
Figure 3: Average Effectiveness Measures on 100 Queries
0,00E+00%
5,00E+10%
1,00E+11%
1,50E+11%
2,00E+11%
2,50E+11%
3,00E+11%
3,50E+11%
4,00E+11%
4,50E+11%
5,00E+11%
1,00E+06% 2,00E+06% 3,00E+06% 4,00E+06% 5,00E+06% 6,00E+06% 7,00E+06% 8,00E+06%
Running&'me&(ns)&
Number&of&points&
Index&Building&'me&
1%processor%
3%processors%
9%processors%
17%processors%
65%processors%
Figure 4: Index Building Time in SCOPE
More in details, Figure 4 shows the running time
achieved for building the index when varying the size
of the input data and the number of used processors
of the supercomputing infrastructure.
Figure 5 reports the running time of the distributed
K-nearest algorithm when varying the number of pro-
cessors.
Similarly, Figure 6 shows the running time of our
algorithm for distributed range query. All the listed
case studies confirm the positive trend achieved dur-
ing the preliminary experimentation (Amato et al.,
2015a).
As expected, the most costly procedure is the in-
dex building; however, the achieved running times are
very good and become extremely low whether we de-
cide to use 65 processors, even if our index holds
8,000,000 points.
6 CONCLUSIONS
In this paper, we proposed a document retrieval
framework based on semantic query expansion tech-
0,00E+00%
1,00E+04%
2,00E+04%
3,00E+04%
4,00E+04%
5,00E+04%
6,00E+04%
7,00E+04%
1,00E+06% 2,00E+06% 3,00E+06% 4,00E+06% 5,00E+06% 6,00E+06% 7,00E+06% 8,00E+06%
Running&'me&(ns)&
Number&of&points&
K5nearest&'me&
1%processor%
3%processors%
9%processors%
17%processors%
65%processors%
Figure 5: K-Nearest Time in SCOPE
Figure 6: Range Query Time in SCOPE
niques, developed exploiting state-of-the-art NLP
techniques and a combination of well known semantic
metrics within a kd-tree indexing structure.
The idea behind this work is that expanding a user
query on a corpus of documents with a set of se-
mantically related words (within the documents their-
selves) would increase both the number of documents
returned by the query, and the relevance of documents
to the original query.
Thus, we firstly presented a general architecture
ANovelApproachtoQueryExpansionbasedonSemanticSimilarityMeasures
351
for our framework and then proposed a preliminary
experimental evaluation both in terms of efficiency
and effectiveness, considering as case study a corpus
of job announcements in Italy.
The experimental evaluation showed that our ap-
proach offers good results in terms of retrieval pre-
cision and recall. Furthermore, we evaluated the ex-
ecution time of the core data structure - the kd-tree-
both in its sequential version and in a distributed one,
showing that the proposed approach is easily scalable.
REFERENCES
Albanese, M., Capasso, P., Picariello, A., and Rinaldi,
A. M. (2005). Information retrieval from the web: an
interactive paradigm. Advances in Multimedia Infor-
mation Systems, pages 17–32.
Amato, F., De Santo, A., Gargiulo, F., Moscato, V., Persia,
F., Picariello, A., and Poccia, S. (2015a). Semindex:
an index for supporting semantic retrieval of docu-
ments. In Proceedings of the IEEE DESWeb ICDE
2015.
Amato, F., De Santo, A., Moscato, V., Picariello, A.,
Serpico, D., and Sperli, G. (2015b). A lexicon-
grammar based methodology for ontology population
in e-health applications. In The 9-th International
Conference on Complex, Intelligent, and Software In-
tensive Systems (CISIS-2015). Blumenau, Brazil.
Amato, F., Mazzeo, A., Moscato, V., and Picariello, A.
(2009). A system for semantic retrieval and long-
term preservation of multimedia documents in the e-
government domain. International Journal of Web
and Grid Services, 5(4):323–338.
Amato, F., Mazzeo, A., Moscato, V., and Picariello, A.
(2014). Exploiting cloud technologies and context in-
formation for recommending touristic paths. In In-
telligent Distributed Computing VII, pages 281–287.
Springer.
Amato, F., Mazzeo, A., Penta, A., and Picariello, A. (2008).
Knowledge representation and management for e-
government documents. IFIP International Federa-
tion for Information Processing, 280:31–40.
Bouchoucha, A., Liu, X., and Nie, J.-Y. (2014). Integrat-
ing multiple resources for diversified query expansion.
In Advances in Information Retrieval, pages 437–442.
Springer.
Buey, M. G., Garrido,
´
A. L., and Ilarri, S. (2014). An ap-
proach for automatic query expansion based on nlp
and semantics. In Database and Expert Systems Ap-
plications, pages 349–356. Springer.
Carpineto, C. and Romano, G. (2012). A survey of auto-
matic query expansion in information retrieval. ACM
Computing Surveys (CSUR), 44(1):1.
Castells, P., Fernandez, M., and Vallet, D. (2007). An adap-
tation of the vector-space model for ontology-based
information retrieval. Knowledge and Data Engineer-
ing, IEEE Transactions on, 19(2):261–272.
Colace, F., De Santo, M., Greco, L., and Napoletano, P.
(2015). Weighted word pairs for query expansion.
Information Processing & Management, 51(1):179–
193.
Dalton, J., Dietz, L., and Allan, J. (2014). Entity query fea-
ture expansion using knowledge base links. In Pro-
ceedings of the 37th international ACM SIGIR confer-
ence on Research & development in information re-
trieval, pages 365–374. ACM.
Ermakova, L., Mothe, J., and Ovchinnikova, I. (2014).
Query expansion in information retrieval: What can
we learn from a deep analysis of queries? In Inter-
national Conference on Computational Linguistics-
Dialogue 2014, volume 20, pages pp–162.
Faloutsos, C. and Lin, K.-I. (1995). FastMap: A fast al-
gorithm for indexing, data-mining and visualization
of traditional and multimedia datasets, volume 24.
ACM.
Fern
´
andez, M., Cantador, I., L
´
opez, V., Vallet, D., Castells,
P., and Motta, E. (2011). Semantically enhanced in-
formation retrieval: an ontology-based approach. Web
Semantics: Science, Services and Agents on the World
Wide Web, 9(4):434–452.
Furnas, G. W., Landauer, T. K., Gomez, L. M., and Du-
mais, S. T. (1987). The vocabulary problem in human-
system communication. Communications of the ACM,
30(11):964–971.
Huang, J. X., Miao, J., and He, B. (2013). High per-
formance query expansion using adaptive co-training.
Information Processing & Management, 49(2):441–
453.
Jain, V. and Singh, M. (2013). Ontology based informa-
tion retrieval in semantic web: A survey. International
Journal of Information Technology and Computer Sci-
ence (IJITCS), 5(10):62.
Maron, M. E. and Kuhns, J. L. (1960). On relevance, proba-
bilistic indexing and information retrieval. Journal of
the ACM (JACM), 7(3):216–244.
Moscato, V., Picariello, A., and Rinaldi, A. M. (2010a).
A combined relevance feedback approach for user
recommendation in e-commerce applications. In
Advances in Computer-Human Interactions, 2010.
ACHI’10. Third International Conference on, pages
209–214. IEEE.
Moscato, V., Picariello, A., and Rinaldi, A. M. (2010b).
A recommendation strategy based on user behavior
in digital ecosystems. In Proceedings of the Interna-
tional Conference on Management of Emergent Digi-
tal EcoSystems, pages 25–32. ACM.
Pal, D., Mitra, M., and Datta, K. (2014). Improving
query expansion using wordnet. Journal of the As-
sociation for Information Science and Technology,
65(12):2469–2478.
Resnik, P. (1999). Semantic similarity in a taxonomy:
An information-based measure and its application to
problems of ambiguity in natural language.
Resnik, P. (2011). Semantic similarity in a taxonomy:
An information-based measure and its application to
problems of ambiguity in natural language. CoRR,
abs/1105.5444.
DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications
352
Rinaldi, A. M. (2008). A content-based approach for doc-
ument representation and retrieval. In Proceedings of
the eighth ACM symposium on Document engineer-
ing, pages 106–109. ACM.
Rivas, A. R., Iglesias, E. L., and Borrajo, L. (2014). Study
of query expansion techniques and their application
in the biomedical information retrieval. The Scientific
World Journal, 2014.
Sagayam, R., Srinivasan, S., and Roshni, S. (2012). A sur-
vey of text mining: Retrieval, extraction and indexing
techniques. International Journal Of Computational
Engineering Research, 2(5).
Vallet, D., Fern
´
andez, M., and Castells, P. (2005). An
ontology-based information retrieval model. In The
Semantic Web: Research and Applications, pages
455–470. Springer.
Yang, K.-H., Lin, Y.-L., and Chuang, C.-T. (2014). Using
google distance for query expansion in expert finding.
In Digital Information Management (ICDIM), 2014
Ninth International Conference on, pages 104–109.
IEEE.
ANovelApproachtoQueryExpansionbasedonSemanticSimilarityMeasures
353