Geographical Queries Reformulation using Parallel FP-Growth for
Spatial Taxonomies Building
Omar El Midaoui
, Btihal El Ghali
and Abderrahim El Qadi
LRIT Associated Unit to the CNRST - URAC n°29, Faculty of Sciences, Mohammed V University in Rabat, Morocco
SmartiLab, Ecole Marocaine des Sciences de l'Ingénieur (EMSI), Rabat, Morocco
TIM, High School of Technology, Mohammed V University in Rabat, Morocco
Keywords: Information Retrieval, Parallel FP-Growth Algorithm, Machine Learning, Geographical Query
Reformulation, Spatial Entity, Spark, Big Data.
Abstract: Due to its specificities and hierarchical structure, a geographical query needs a special process of
reformulation by Information Retrieval Systems (IRS). This fact is ignored by most of web search engines.
In this paper, we propose an automatic approach for building a spatial taxonomy that models’ the notion of
adjacency that can be uses in the reformulation of the spatial part of a geographical query. This approach
exploits the documents that are in the top of the list of retrieved results when submitting a spatial entity, which
is composed of a spatial relation and a noun of a city. Then, a transactional database is constructed, considering
each document extracted as a transaction that contains the nouns of the cities sharing the country of the
submitted query’s city. The algorithm FP-Growth is applied to this database in his parallel version (PFP) in
order to generate association rules, that will form the country’s taxonomy in a Big Data context. Experiments
has been conducted on Spark and their results show that query reformulation based on the taxonomy
constructed using our proposed approach improves the precision and the effectiveness of the IRS.
Most human activities are well located in the
geographical area. Thus, it is not surprising that a big
amount of web documents contain geographical
references. A study that was done on the Excite
search engine shows that between every five queries
there is one query which have a geographical context
(Sanderson and Kohler, 2004). Web users searching
for information that are spatially located often require
information, that are geographically specific, such
geographic terms in Web pages and user queries or
even user location (Jiang et al., 2018). However,
retrieval systems currently have limited support to
operationalize a user’s geospatial queries.
Geographic information deals with physical objects
that are in some cases hard to express with words and
that contain most of the time ambiguous terms. These
arguments prove the fact that it will be very useful for
search engines to take into account the spatial scope
of geographical queries.
The current search engines generally handle
queries by adopting a keyword matching approach
without inferring the geographical scope of the spatial
terms. Thus, when the name of a place is typed into a
typical search engine associated with a spatial
preposition (e.g. “near”), web pages that include that
name in the text will be retrieved but most likely, not
places that are close to that specified place.
In order to do a spatial analysis of text, the first
step is the annotation of spatial named entities.
Several techniques have proved their ability for
carrying out this annotation, such as the works of
(Rocío and Erick, 2010) and (Loustau, 2008) that has
elaborated it using external resources named
“gazetteers”. A gazetteer is a dictionary or geographic
directory whose inputs are names of places. Each
entry in the dictionary may be associated with
information such as belonging to one or more
administrative structures (town, region, country, etc.),
the physical characteristic (mountain, river, road,
etc.), statistical data, and a geometric representation
expressed in a geographic referential.
Other works proposes the categorization of these
spatial named entities after identification. Such as,
Buscaldi and Rosso that proposed a technique for
spatial named entities categorization using the
thesaurus Geo-WordNet (Buscaldi and Rosso, 2008).
El Midaoui, O., El Ghali, B. and El Qadi, A.
Geographical Queries Reformulation using Parallel FP-Growth for Spatial Taxonomies Building.
DOI: 10.5220/0009446603750381
In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 375-381
ISBN: 978-989-758-426-8
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Or Bouamor that exploited a document structure in
(Bouamor, 2009), using as a corpus the collaborative
encyclopaedia “Wikipedia”. In his work, the
identification of named entities is done using the title
and their categorization is based on the analysis of the
first sentence of the description part or the category
part at end of the article.
Different external resources have been also
created and used in the most recent works. Such as
the Geographical Information Retrieval model,
proposed by (Fang and Zhang, 2018), that simplifies
the process of user information acquisitions by
analyzing and extracting the attribute features, spatial
features and structure features of Geographic Markup
Language (GML) data. As defined by this work,
GML data is generated by artificial of special services
and stored semi-structured data using the Geographic
Mark-up Language (GML), which is a geographic
information coding specification that was
implemented based on the Extensible Mark-up
Language (XML) and standardized (ISO 19136-
In the other hand, some approaches aim more
particularly to the disambiguation of recognized
places names (Buscaldi, 2009). The ambiguity can be
understood as a word or a phrase that has many
meanings (Vargas et al., 2012). In this case, two types
of ambiguities are to treat (Einat et al., 2004): a
geo/non-geo ambiguity is when the entity has a non-
geographical meaning like the term “Turkey”, and a
geo/geo ambiguity that occurs when the named entity
refers to two different places as Rabat in Malta and
Rabat in Morocco.
A hybrid approach is proposed in (Gaio et al.,
2012) which, first landmark names of places but also
searches for these terms in ontological resources to
identify related terms, potentially geographic.
Domain-Specific taxonomies (Yangqiu et al., 2015)
are also playing an important role in many
applications for improving search results (Xueqing et
al., 2012) or helping with query reformulation as in
(Sadikov et al., 2010) and (Aloteibi, Mark Sanderson,
In this paper, we propose a geographical
taxonomy builder using the parallel FP-Growth
Algorithm (PFP) which inputs are text documents and
we complete the process by suggesting a query
reformulation approach for geographical queries. Our
contributions are also made in the step of
geographical and thematic entities separation giving
a geographical query, in order to reformulate the two
entities in a different manner.
This approach is tested using a collection that has
been created during our experimentations. This
collection contains 50 queries and 2500 documents.
We used 1500 documents, considering 30 retrieved
documents per city for the taxonomy building step, as
we used a list of the 50 most popular cities. In addition
of, 20 retrieved documents per submitted query in the
reformulation step evaluation (10 before and 10 after
reformulation). Thus, 2500 documents have been
used in total. The collection’s documents were
retrieved automatically using the google web services
whenever there was a need.
This article is organized as follows:
The section 2 is introducing our proposed
approach for the construction of a geographical
taxonomy of adjacency using the PFP algorithm,
while we explain our query reformulation technique
in section 3. The results of our experimentations are
presented in section 4. Finally, section 5 draws
conclusions and future works.
A taxonomy consists of a number of names arranged
in a hierarchical system that describe a specific
domain (Enghoff, 2009) by a hierarchical structure. A
taxonomy starts from a general concept of a domain,
and associate to it the terms that describe this specific
domain more precisely while moving down in the
In this work, we introduce an automatic approach
that builds a geographical taxonomy of adjacency. In
this aim, we exploit the best-ranked documents
retrieved using the search engine when submitting a
spatial part of a query, that contain the spatial relation
of adjacency and a noun of a city for which we are
constructing the taxonomy.
The proposed approach is based on the Parallel
FP-Growth algorithm. The geographical query model
used in this work considers two type of entities: the
Absolute and the Relative Spatial Entities (ASE and
RSE). The geographical named entities such as the
city of “London” are well-known named entities and
are defined as an ASE (Absolute Spatial Entity).
While complex spatial entities as “near London” are
labelled as an RSE (Relative Spatial Entities).
2.1 The FP-Growth Algorithm
The FP-growth technique (Han et al., 2004) is an
association rules Machine Learning algorithm, where
“FP” is the acronym of Frequent Pattern. Given as
IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security
input a dataset of transactions, the first step of this
algorithm is to compute item frequencies and identify
the most frequent items. Different from Apriori
algorithm (Najadat et al., 2013) (Al-Maolegi and
Arkok, 2014) designed for the same aim, by its
second step that uses a suffix tree structure, called FP-
tree, to encode transactions without the explicit
generation of candidate sets, which are usually
expensive to generate. After this step, the frequent
item sets are extracted from the FP-trees.
The FP-growth is a two phases algorithm. The
first phase consists on the construction of FP-Trees
and the second mines frequent patterns from the
generated FP-Trees.
The construction of an FP-Tree requires two scans
on the used database. The first scan permits the
selection of the frequent items that are then sorted
based on their frequency in descending order to form
a new structure caller F-list. The second scan
constructs the FP-Tree. First, the non-frequent items
are removed while reordering the database tuples
according to F-list. Then the reordered transactions
are inserted into the FP-Tree. The Input of the Growth
part of the algorithm is the constructed FP-Tree and
the value of minimum support threshold.
FP-Growth traverses’ nodes in the FP-Tree
beginning from the least frequent item in F-list. While
traversing each node, FP-Growth collects items on
the path from the node to the root of the tree. Those
collected items constitute the elements of the
conditional pattern base of the current item in F-list.
The conditional pattern base of an item is defined as
a small database of patterns that co-occur with this
item. Then FP-Growth creates small FP-Trees based
on the conditional pattern bases and re-executes the
algorithm recursively on the new FP-Trees until no
conditional pattern base can be generated.
2.2 The Parallel FP-Growth
The parallelized FP-growth work on distributed
machines (Lingling and Yuansheng, 2015). Its
partitions computation is done in such a way that each
machine executes an independent group of mining
tasks. This method of partitioning eliminates
computational dependencies between machines, and
thereby communication between them.
Given a transaction database DB, the PFP
algorithm’s steps are as follows:
Sharding: splitting DB into successive parts and
storing those parts on n different machines. Each
resultant part is called a shard.
Parallel Counting: counting the support values
of all items appearing in each shard. This step
permits to discover the items' vocabulary
implicitly, which is normally unknown for a
huge Database. The result of this step is an F-
Grouping Items: Considering I the set of
vocabulary discovered, splitting the |I| items
appearing in F-List into Q groups. The groups
list is called G-list, where each group is given a
unique group-id (gid). As F-list and G-list are
both small, this step can be executed on a single
node of the cluster in few seconds.
Parallelizing: Selecting group-dependent
transactions on which the FP-Growth algorithm
is applied in order to build local FP-trees in
parallel and growth their conditional FP-trees
Aggregating: Aggregating the results generated
in Step 4 as our final result.
PFP distributes the growing FP-trees work based on
the transactions’ group. thus, this approach is more
scalable than a single-node implementation.
PFP is implemented in MLlib on Spark and it
takes three parameters: the minimum support
threshold to identify frequent item sets, the minimum
confidence for generating Association Rules and the
number of shards used to distribute the job.
2.3 Geographical Taxonomy of
Considering a database whose transactions are
documents and items are the cities of the country that
contain the city of the user query. We propose to build
a spatial taxonomy (Fig. 1) of adjacency based on the
PFP algorithm.
The documents that form the input transactional
database are restricted to the Absolute Spatial entities
contained in the documents. Thus, the items
considered are the ASEs.
After the application of the PFP algorithm,
starting from the capital of the country for which we
will built the taxonomy, the fusion of all the generated
FP-trees is forming our geographical taxonomy.
Figure 1: A two-level taxonomy for the ASE0.
Geographical Queries Reformulation using Parallel FP-Growth for Spatial Taxonomies Building
Validation Step. In this contribution, we propose also
a step of validation of each arc of the taxonomy. To
validate each arc in the aggregating step, we verify if
its two parts (the two ASEs that form this arc)
mutually generate each other in the FP-trees. For
example, ASE
involves ASE
and ASE
then the arc is kept and this taxonomy evolves
to a two-level taxonomy as shown in figure 1.
Otherwise, ASE
has involved ASE
, but ASE
not involve ASE
so this arc has not been validated.
Thus, it has been removed from the taxonomy.
In order to reformulate a geographical query, we first
separate the different components of the query based
on the approach of geographic information extraction
(GIE) proposed in (Sallaberry et al., 2007). This
approach utilizes a methodology of semantic
annotation for the detection of geographical markers:
first, the Absolute Spatial entity is detected and
annotated. Then the spatial entity (SE) is constructed
considering this ASE and a lexicon of spatial
relations. The remaining words of the query form the
thematic entity (TE).
In this step also, we proposed a contribution. We
made some modifications in the GIE approach cited
above based on a hypothesis.
Hypothesis. If the Spatial Relation is not present
in the query, the occurrence of an ASE does not mean
that the query has a geographical intent. For example,
a query containing “George Washington”. We can
also consider the example of the query searching for
“Hôtel de Paris”. In this context, the noun “Paris” is
the name of a hotel whose location is in Tangier,
Monte Carlo or Monaco.
After the separation of the different entities of the
user query, we continue applying the proposed
approach by interpreting the spatial relationship
contained in the spatial entity of the query. The
interpretation is done using a lexicon of adjacency
spatial relations. The process of reformulation
depends on the result of this interpretation.
If the spatial relation detected in the query is an
SR of adjacency, we reformulate the spatial part of
the query using the country’s taxonomy (Xueqing et
al., 2012).
Logically, a query that contains a relation of
adjacency means that the intent of the user is to
retrieve places that are around the ASE of his query.
Thus, we propose to eliminate the entire spatial entity,
and to replace by the direct child-nodes items (CNIs)
of the query’s ASE in the geographical taxonomy as
User New Query = TE SR ASE
Reformulated Query = expanded TE + “CNI 1”
or “CNI 2” or …
In the query resulted from the reformulation,
quotes are used to search for the desired place and not
separately search for the words that the place’s name
contains if the ASE is composed many terms (e.g. the
submission of New York unquoted, can lead the
search engine to search for New and York as two
independent terms). Moreover, the boolean operator
‘or’ is used, to ensure that the retrieval returns
documents that include for example "CNI 1" or "CNI
2" or both of them and so on for all the child-nodes
used to reformulate the query.
To apply the proposed approach, we have used a
lexicon of spatial relationships, and a database of
validated ASEs associated with their countries.
In order to test and verify the performance of the
technique of taxonomy building proposed in this
work, we take our country Morocco as an example.
Thus, to be able to use the web pages created by
Moroccans themselves we perform our tests in
French. “Rabat”, the capital of Morocco is the ASE
that we took as a root of our taxonomy. The search
engine used in our experimentations is Google web
We apply our method using transaction database
that is constructed by iterating on Morocco’s ASEs
list (a list of 50 cities and villages of Morocco). For
every ASE, we selected the thirty first web pages
retrieved when submitting a RSE containing the
current ASE.
As a pre-treatment step, we deleted accents from
the extracted documents to minimize the matching
gab between ASEs, due to different manners write
cities names by the persons who wrote the documents
contents. Because, the miss-matching problem arise
particularly in the case of nouns, which contain
Then, we varied the SR of the spatial entities
submitted to verify if the variation of the SR
influences the performance of the proposed approach.
The spatial relations used in this test step are as
IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security
Table 1: Spatial Relations.
Annotation Ex
SR 1 à côté de
SR 2 à la
hérie de
SR 3 à proximité de
SR 4 aux alentours de
SR 5 aux environs de
SR 6 les environs de
SR 7
rès de
First, the five top-ranked documents were
extracted for the ASE Rabat associated with every
spatial relationship of Table 1. A database (DB)
containing 35 transactions is constructed based on
these documents. The parallel FP-growth algorithm is
applied to this DB and then the association rules are
generated between Rabat and every Moroccan ASE
that co-occur with it in the DB.Ater that, we varied
the minsup from 0,2 to 0.8 (as shown in Table 2)
without the validation step for the rules extracted
using 2-frequent item sets. Later we computed the
error rate and the number of rules generated in every
Table 2: The error rate and the number of rules generated
while varying the minimum support threshold and the
spatial relationship used for item sets containing the ASE
Error rate Number of
generated rules
0,2 0,4 0,6 0,8 0,2 0,4 0,6 0,8
RS 1
72,73 28,57 33,33 0 22 7 3 1
RS 2
42 0 0 0 5 2 1 1
RS 3
25 0 0 - 4 1 1 0
RS 4
40 33,33 0 0 10 3 1 1
RS 5
33,33 0 0 0 9 2 2 1
RS 6
40 50 0 0 10 4 2 2
RS 7
0 0 0 - 6 6 1 0
From Table 2, we notice that using the
minsup=0,8 the algorithm does not return any results
in some cases otherwise it gives 1 or 2 answers. The
same for minsup=0,6 that do not exceed 2 correct
Regarding the value 0,2 it generally gives a high
error rate and sometimes returns a very high number
of responses up to 22 resulting ASEs in the case of
RS 1 with 6 correct adjacent ASEs only. Thus, we
favored the value of minimum support equal to 0,4
because it is the one that gives the best ratio between
a minimal error and an acceptable number of answers.
The next step of experimentations is done in order
to compare the cases where we use or not the
validation step for aggregating the generated FP-trees
in order to built the taxonomy of adjacency, based on
a minimum support of 0,4.
Table 3: The error rate and the number of correct rules
generated using the step of validation or not and using the
average of support between the two cases, varying the
spatial relation used for item sets containing the ASE
“Rabat” with a minsup of 0,4.
Error rate Number of correct
SR 1
28,57 0 0 7 2 2
SR 2
0 0 0 2 1 1
SR 3
0 - 0 1 0 1
SR 4
33,33 50 33,33 3 2 3
SR 5
0 0 0 2 2 2
SR 6
50 0 0 4 2 2
SR 7
0 0 0 6 5 6
WV: without validation, UV: Using validation, AS: Average support
Comparing the results using validation with the
results without validation, we note that the error rate
decreases when using the validation step, with the
exception of the SR 4 for which from 3 results
including 2 correct ASEs, validation has eliminated
one of the correct ASEs and kept the erroneous one.
Concerning the SR 3 we notice that the only ASE that
was resulted without validation was eliminated with
the step of validation. In general, we conclude that the
validation step reduces errors sufficiently.
To minimize the error rate while keeping as much
as possible of correct results (eliminate only the
erroneous ASEs by the validation step). We propose
to compute the average of the two supports of the
opposite rules (e.g. ASE1 ASE2 and ASE2
ASE1). Table 4 shows that the result given by the case
of the average support solves the problems mentioned
above for the SR 3 and SR 4.
Comparing the seven spatial relations, we
promote the SR 7 “près de” which gives the most
interesting result with 0% error and six correct ASEs
as child nodes of Rabat’s taxonomy of adjacency.
Figure 2: A one-level taxonomy for Rabat using the spatial
relation “Près de”.
Geographical Queries Reformulation using Parallel FP-Growth for Spatial Taxonomies Building
Using the favorable conditions represented above
we continue the construction of Morocco’s taxonomy
(as shown in figure 3) with 0,4 as a minimum support,
and using the average of support for validating links.
Figure 3: A two-level geographical taxonomy of adjacency
for Morocco.
For building this taxonomy, we had used 50
Moroccans ASEs and we were searching for the 30
first retrieved documents while submitting every
ASEs with the selected relationship. Thus, 1500
documents have been used in this test with a
minimum support of 0,4 and a minimum confident of
0,6. This test have been conducted in 0,85 seconds
due to the use of Spark, using a cluster of two nodes.
In order to evaluate the precision of the results of
our approach and confirm the results of the precedent
tests, we proposed 50 geographical queries that has
been submitted to the Google search services with
and without reformulation using the taxonomy
realized based on the PFP algorithm, and we
compared the values of the P@10 and the Mean
Average Precision of the two cases.
Table 4: The performance rate of the presented technique
according to the original queries.
Baseline P
10 MAP
ueries 10,56% 12,92%
From Table 4, we notice that the approach
presented in this manuscript gives an interesting
improvement in the precision of the geographical
queries used in our experiments.
In this paper, we proposed a new method of
construction of geographical taxonomies of
adjacency using the parallel FP-Growth, and a
technique for reformulating geographical queries that
contain a spatial entity of adjacency. We have
conducted tests on the taxonomy builder method by
forming Morocco’s taxonomy of adjacency. During
our experimentations, we varied the minimum
support threshold and the used spatial relationship in
order to search for the parameters of the approach that
extract the most appropriate frequent item sets. Then
we constructed the Moroccan taxonomy using a
minimum support of 0.4 and a minimum confidence
of 0.6 and the SR number 7, because these conditions
gave the best results during our experiments. The
proposed technique of reformulation has been tested
on 50 queries, which have a geographical intent and
their thematic entities are from different fields. These
queries had been reformulated based on the spatial
taxonomy of adjacency. Finally, we compared the
results retrieved by the search engine before and after
the application of our technique using the evaluation
measures MAP and P@10. The results show that the
reformulation based on our proposed approach and
using a small number of reformulation terms has
improved the value of MAP significantly.
Considering the experimental results, we conclude
that the presented method is an efficient work that
permit to interpret and improve the results of queries
containing a spatial entity of adjacency.
As future work, we intend to propose a new
method of geographical query reformulation, based
on Big Data technologies and an in-depth analysis of
user’s behaviours through a study of a search engine’s
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