Learning Non-taxonomic Relationships of Financial Ontology
Omar El Idrissi Esserhrouchni
1
, Bouchra Frikh
2
and Brahim Ouhbi
1
1
Lab LM2I, ENSAM, Moulay Ismaïl University, Marjane II, B.P. 4024, Meknès, Morocco
2
Lab LTTI, ESTF, Sidi Mohamed Ben Abdellah University, B.P. 2427, Route d’imouzer, Fès, Morocco
Keywords: Ontology Learning, Financial Ontology, Non-taxonomic Relationships Extraction, Knowledge Acquisition,
Open Information Extraction.
Abstract: Finance ontology is, in most cases, manually addressed. This results in a tedious development process and
error prone that delay their applicability. This is why there is a need of domain ontology learning methods
that built the ontology automatically and without human intervention. However, in this learning process, the
discovery of non-taxonomic relationships has been recognized as one of the most difficult problems. In this
paper, we propose a new methodology for learning non-taxonomic relationships and building financial
ontology from scratch. Our new technique is based on using and adapting Open Information Extraction
algorithms to extract and label domain relations between concepts. To evaluate our new method
effectiveness, we compare the extracted non-taxonomic relations of our algorithm with related works in the
same finance corpus. The results showed that our system is more accurate and more effective.
1 INTRODUCTION
Financial ontologies are created to solve widespread
financial problems such as support of financial
decision making, market research and analysis,
investment recommendations or assessing the
financial health of a company.
Financial ontology engineering is typically
performed manually, requiring the intervention of
(1) financial domain expert who provide the
financial knowledge, (2) and knowledge engineers
who are able to formalize that knowledge. However,
the field of finance is a conceptually rich domain
where information is diverse, huge in volume and
obtained from a variety of heterogeneous sources. A
massive amount of valuable information is produced
worldwide every day in the web. In fact, the manual
development of these ontologies is costly, time
consuming, tedious and error prone task, which
delay the applicability of the resulting ontologies
(Shamsfard and Barforoush, 2003; Sanchez and
Moreno, 2008).
Due to these reasons, nowadays, there is a need
for methods and processes that can build finance
ontology automatically and without human
intervention. In this sense, domain ontology learning
was identified as the process of building domain
ontology from scratch, enriching, or adapting an
existing ontology in an unsupervised way (Maedche
and Staab, 2001). This process reduces the time and
effort needed in the ontology development process.
Domain ontology learning is still an emerging field,
which aims at assisting knowledge engineers in
ontology construction.
In the literature several approaches have been
proposed for learning domain ontology.
Nevertheless, most of these approaches address only
the way to learn the taxonomic part of domain
ontologies. The phase of extraction of non-
taxonomic relationships has been recognized as one
of the most difficult and least tackled problems
(Villaverde et al., 2009). This phase includes two
different problems: (1) discovering the existence of
relevant relationships between concepts and (2)
labeling these relationships according to their
semantic meaning. Moreover, in the ontology
learning process, non-taxonomic relationships
identification layer uses background knowledge
from domain taxonomy in order to extract non-
taxonomic relations from domain corpus (Buitelaar
et al. 2005). However, the precision and the quality
of the extracted ontology relations will highly
depend on the accuracy of its domain taxonomy
given as input.
In previous works (El Idrissi Esserhrouchni et
al., 2014; Frikh et al., 2011), we covered the
Esserhrouchni, O., Frikh, B. and Ouhbi, B..
Learning Non-taxonomic Relationships of Financial Ontology.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 2: KEOD, pages 479-489
ISBN: 978-989-758-158-8
Copyright
c
2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
479
learning of taxonomic relationships for domain
ontology and introduced an efficient system that
builds financial taxonomy more accurately than
other benchmark algorithms. In this paper, we
present a new methodology for learning non-
taxonomic relationships and building financial
ontology from scratch. Our new technique is based
on integrating and adapting Open Information
Extraction (Open IE) algorithms to extract and label
domain relations between concepts. Indeed, Open IE
algorithms, extract automatically and without human
intervention all existing relations from a large text
corpus. Due to its open-domain and open-relation
nature, gross use of Open IE algorithms is unable to
relate the extracted relations to domain ontology.
However, an adaptation of Open IE algorithms is
necessary to overcome this limitation.
Open IE approaches were introduced recently by
Banko et al., (2007). So there is only a small amount
of projects using it. To our knowledge, they were
never used as a part of the process of learning
domain ontology from scratch. Our work will be the
first one that adapts these types of algorithms to
learn domain ontology, especially for finance
domain.
The rest of the paper is organized as follows.
Section 2 presents related works. Section 3 describes
the general steps for learning domain ontology. Then
in Section 4, our proposed algorithm is described. In
Section 5, the architecture of our system is
presented. In Section 6, an evaluation of our
proposed algorithm is carried out in term of
precision. We integrate our system with three well-
known Open IE tools: Reverb (Fader et al., 2011),
Ollie (Schmitz et al., 2012) and ClausIE (Del Corro
and Gemulla, 2013). Then we compare it with the
algorithm of Sanchez and Moreno (2008), in finance
corpus, to evaluate the relevance of the resulting
non-taxonomic relationships of each algorithm.
2 RELATED WORKS
Some techniques have already been proposed for
learning non-taxonomic relationships of domain
ontology. Most of them combine different levels of
machine learning and linguistic analysis.
(Maedche and Staab, 2000) propose a semi-
automatic method for learning domain ontology. It
uses generalized association rules to identify
relationships between pairs of words and propose the
best possible level in the hierarchy where to add the
relationships. Nevertheless, this technique does not
address the problem of labeling relationships. As
consequence, users have to complete this task
manually and without any assistance. In the same
context, (Villaverde et al., 2009) use the strength of
the association between concepts and verb given by
POS-tagging rules to suggest multiple labels
relationship between concepts. They identify non-
taxonomic relationships by the presence of two
concepts of the taxonomy in the same sentence with
a verb between them at maximum of N terms. One
limitation of this method is that the authors refer to
the verbs and the concepts as single words when in
fact, in most cases, they appear in the form of verb
phrases. However, the accuracy will be reduced and
the recall will be low.
Alternatively, Jiang and Tan (2005) attempt to
acquire non-taxonomic relationships between
concepts using regular expressions and natural
language processing. Their algorithm performs a full
parsing of the entire corpus using the Berkeley
Parser (Petrov et al., 2006) and inspects the entire
corpus to identify instances of patterns that indicate
non-taxonomic relationships. However, the use of
regular expressions limits the identification of the
relations in the corpus. It may lack relevant
relationships that are not recognized by the used
patterns. An other important element to note is that
their method involves a full parsing from text, which
allows better extraction of concepts, but it is very
expensive in time.
Other works combine natural language
processing and statistical measure to extract and
select relevant non-taxonomic relationships for
domain ontology. OntoGain (Drymonas et al., 2010)
selects verbs frequently occurring in the context of
pairs of concepts for labeling semantic relationships.
In that work, the association between concepts is
assessed using a measure of the conditional
frequency of a pair of concepts given a verb. In
Sanchez and Moreno (2008) paper, non-taxonomic
relationships extraction is based on (1)
morphological and syntactic analysis to extract verbs
and concepts that have a relationship with the
domain keyword, (2) web scale statistics to refine
the extracted relations. To avoid the natural
language complexity, they apply some restriction,
for example: only sentences with present tenses are
used, verb phrases containing modifiers in the form
of adverbs are rejected. Thus, to learn non-
taxonomic relationships, for each sentence that
contain the domain keyword, relationships
represented by tuples in the form (np1, vp, np2) are
extracted and evaluated for every verb phrase (vp)
with the first noun phrase to its left (np1) and the
second noun phrase to its right (np2).
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480
Figure 1: The main steps for learning non-taxonomic relationships for domain ontology.
In a more recent work, (Serra et al., 2013) use on
their work Natural Language Processing (NLP) and
statistics to extract non-taxonomic relationships of
domain ontology. The system provides three types
of extraction rules: the Sentence Rule (SR), the
Sentence Rule with Verb Phrase (SRVP) and the
Apostrophe Rule (AR). The importance of the
extracted relations is measured using co-occurrence
frequency.
In term of financial ontologies, following the
research that we have conducted, we found that most
financial ontologies were built manually (Mellouli et
al., 2010; Wang et al., 2011). Rarely are the
researches topics that address the automatic
construction of this domain ontology. The OntoPlus
methodology introduced by Novalija et al., (2011) is
one of the few works that deals with automating the
extraction of finance ontology. However, OntoPlus
relates to the automatic extension of an existing
ontology and not to learn it from scratch. It
introduces a new methodology for semi-automatic
ontology extension for analyzing business and
financial news. OntoPlus used mining techniques to
automatically identify candidate concepts in the
ontology to relate to the new knowledge from the
domain. However, general non-taxonomic
relationships were not included in the process of
ontology extension.
In the present work, we propose a new method to
overcome all the limitations listed below and to
learn financial ontology from scratch. We
incorporate, for the first time in the literature, Open
IE algorithms in the domain ontology extraction
process. Thus, we benefit from the performance and
the experience of these algorithms in the area of
non-taxonomic relationships extraction.
3 OVERVIEW OF THE STEPS OF
LEARNING NON-TAXONOMIC
RELATIONSHIPS FOR
DOMAIN ONTOLOGY
From the ontology-learning point of view (Buitelaar
et al., 2005), the main steps of learning non-
taxonomic relationships for domain ontology are the
following (see Figure.1):
Step 1 - Extraction of Terms that Represent the
Domain: It’s needed to identify terms that are likely
related to the studied domain. This step constitutes a
principal prerequisite for unsupervised concept
acquisition. In our work, the term extraction is based
on the neighborhood of an initial keyword that
characterizes the input corpus. In our finance
domain example (Figure 1), the initial keyword
given as input for building the ontology is
“Finance” and the terms “bank, loan, report,
finance company” are examples of the extracted
terms in this phase of the ontology learning process.
Step 2 - Selection of Domain Concepts: This step
aims to select the most relevant terms from those
previously extracted. The filtered terms constitute
the concepts of the studied domain. Concepts
selection can be performed using statistic-based
techniques (Maedche and Volz, 2001; Makrehchi
and Kamel, 2007), linguistic-based techniques or a
hybrid one (Meijer et al., 2014).
In our work, we use a statistical measure that
combine Chir statistic and Conditional Mutual
Information measure to select the most relevant
domain concepts (see section 4). In our example of
the finance ontology, the word “report”, extracted
in the first step, does not belong to the finance
Learning Non-taxonomic Relationships of Financial Ontology
481
domain. Using our combined method, previously
cited, that noisy word will be excluded in this
second stage of the learning process of ontology.
Therefore, only the terms “bank, loan, finance
company” will be retained as relevant concepts of
the field of finance.
Step 3 - Construction of the Taxonomy: Learning or
extracting taxonomic relations means finding
hyponyms between the selected concepts with the
goal of constructing a concept hierarchy. It can be
performed in various ways such as using predefined
relations from existing background knowledge (Lee
et al., 2008), using hierarchical clustering (Maedche
and Volz, 2001), relying on semantic relatedness
between concepts (Pekar and Staab, 2002), or using
linguistic and logical rules or patterns (Jiang and
Tan, 2005).
Our proposed algorithm uses the term structure
through string matching of the immediate posterior
and anterior words of a keyword concept to extract
their hierarchies. In our example, we extract the
hyponym relation between the two concepts “bank”
and “finance company”.
Step 4 - Extraction of Non-taxonomic Relationships:
Discovering and labeling non-taxonomic relations
are mainly reliant on the analysis of the structure and
dependencies of candidate sentences. In this phase,
verbs are good indicators for non-taxonomic
relations and are used to label such relations.
Our main contribution in this work is related to this
step of the ontology construction process by
introducing a novel technique based on Open IE
algorithms to extract and label non-taxonomic
relations of domain ontology. In our finance domain
example, the relation between “bank” and “credit”
was identified and labeled with the verb “offer”.
4 OUR METHODOLOGY
4.1 Terms Extraction
Our algorithm is based on the analysis of a large
number of web corpus files in order to find relevant
terms of a domain. In the English language, the
immediate posterior and anterior words of a
keyword express a semantic specialization between
them (Grefenstette, 1997). From this point of view,
the method used to extract relevant terms
(candidates concepts) is based on the neighborhood
of an introduced keyword (initial keyword) that is
enough representative for the studied domain. The
algorithm selects it anterior and posterior words as
relevant terms for the next step of concepts
selection.
4.2 Concepts Selection
To identify domain concepts from the extracted
terms, we used the hybrid measurement (Conditional
S-Measure) based on the Chir-statistic and the
conditional information similarity already defined in
our previous work (El idrissi esserhrouchni et al.,
2014). As we have shown in that paper, Conditional
S-Measure statistics is more efficient than other
benchmark algorithms for selecting domain
concepts.
However in the following sections we remind the
various components of the Conditional S-Measure.
4.2.1 Chir Statistic
The Chir statistic proposed by Li et al., (2008) is an
extended variant of the χ2 statistic to measure the
degree of dependency between a term w and a
category c of documents. They showed that their
method could improve the performance of text
clustering by selecting the words that help to
distinguish documents in different groups. Indeed,
when the χ2 statistic measure the lack of
independence between the terms in the category
(Saengsiri et al., 2010), the Chir statistic selects only
relevant terms that have strong positive dependency
on certain categories in the corpus and remove the
irrelevant and redundant terms. To define the term
goodness of a term w in a corpus with m classes Li
et al., (2008) use a combining formula of χ2 and a
category dependency measure R(w,c) defined by:
,
,
,
(1)
Where O(w,c) is the number of documents that are
in the category c and contain the term w, and E(w,c)
is the expected frequency of the category c to
contain the term w. If there is a positive dependency,
then R(w,c) should be larger than 1. If there is
negative dependency, R(w,c) should be smaller than
1. In the case of the no-dependency between the
term w and the category c, the term category
dependency measure R(w,c) should be close to 1. In
summary, When R(w,c) is larger than 1, the
dependency between w and c is positive, otherwise,
the dependency is negative.
The final formula of the Chir statistic was
defined by:
,
,
With
,
>
1
(2)
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482
Where
,
,
∑
,
With
,
>1
(3)
is the weight of
,
in the corpus in terms of Rw,c
j
.
A bigger
value indicates that the term is
more relevant. When there is a positive dependency
between the term w and the category c
j
.
4.2.2 Conditional Information Measure
In our ontology learning process, conditional mutual
information is used to measure dependency between
two terms w and w’ conditioned by the occurrence of
a parent term w
p
. On the basis of Brun et al., (2002)
work, two terms are considered similar if their
mutual information with all terms in the vocabulary
is nearly the same. Thus, we defined the conditional
similarity by (El idrissi esserhrouchni et al., 2014):
,
⁄
1
2
|
|
min,
,
⁄
,
⁄
max,
,
⁄
,
⁄
|
|
min,
,
⁄
,
⁄
max,
,
⁄
,
⁄
(4)
where V is the vocabulary, and I(z
i
,w/w
p
) is the
Conditional Mutual Information of terms z
i
and w
conditioned by the presence of the term w
p
. The
Conditional Mutual Information formula is given by
(Zhang et al., 2012):
,
⁄
,,
,,
,
,
(5)
Where d is the withdrawal, P(w
p
) is the probability
of the term w
p
. P
d
(z
i
,w
p
) and P
d
(w,w
p
) are the
probability of succession of the terms z
i
and w
p
, w
and wp respectively in the window observation.
P
d
(z
i
,w,w
p
) is the probability of succession of the
terms z
i
, w and w
p
in the window observation. This
probability can be estimated by ratio of the number
of times that the term z
i
is followed by the terms w
and w
p
within the window, with the cardinal of the
vocabulary:
,,
,,
||
(6)
Where f
d
(z
i
,w,w
p
) is the number of times that the
term z
i
is followed by the terms w and w
p
.
4.2.3 Conditional S-Measure
To identify the relevant concepts from those
extracted, we have defined a hybrid measure based
on the weighting model combining a component
estimated from the Chir-statistic and another one
from the similarity measure using conditional
information. This new scoring measure was defined
as:
⁄
∗
1
∗
,
⁄
(7)
Where λ is a weighting parameter between 0 and 1.
Since relevant concepts have strong dependency
with the studied domain and convey semantically
similar information with respect to their parent
concept and to the initial keyword, the candidate
concepts having strong score are likely to be
relevant and should be integrated into the extracted
taxonomy.
4.3 Taxonomy Construction
The taxonomy extraction process is summarized in
the following steps:
a) Perform a k-means clustering algorithm on the
set of all documents and get initial clusters.
b) Start with a keyword that has to be representative
enough for the domain and a set of parameters
that constrain the search and the concepts
selection.
c) Extract all the candidate concepts by analyzing
the neighborhood of the initial keyword; select
the anterior words and posterior words as
candidate concepts.
d) For each candidate concept, calculate its score
S(w/w
p
) measure by using (7).
e) Sort the terms in descending order of their
S(w/w
p
) measure.
f) Select the top l terms from the list.
g) The l extracted concepts are incorporated as
classes or instances in the taxonomy.
h) For each concept incorporated in the taxonomy, a
new keyword is constructed joining the new
concept and the initial one. This process is
repeated recursively until a selected depth level
is achieved or no more results are found.
i) Finally, a refinement process is performed in
order to obtain a more compact taxonomy and to
avoid redundancy.
4.4 Non Taxonomic Relations
Extraction
The main contribution of our methodology in this
area is the integration of Open IE in the process of
learning non-taxonomic relationships for domain
ontology. Thus, we benefit from the performance
Learning Non-taxonomic Relationships of Financial Ontology
483
Figure 2: System architecture.
and the experience of these algorithms in the area of
relations extraction.
Open IE approaches are relatively a recent
paradigm for extracting relations form unstructured
documents. They were introduced by Banko et al.,
(2007). Open IE systems facilitates domain
independent discovery of relations. They extract all
possible relations without any prerequisite or
restriction. In the recent years, many systems for
Open IE have been developed. For our non-
taxonomic learning process, we integrate the most
recent ones, mainly: Reverb (Fader et al., 2011),
Ollie (Schmitz et al., 2012) and ClausIE (Del Corro
and Gemulla, 2013).
Reverb uses shallow syntactic parsing to identify
relations expressed by verbs. The system takes a
sentence as input, identifies a candidate pair of noun
phrase arguments (arg1, arg2) from the sentence,
and then uses the learned extractor to label each
word between the two arguments as part of the
relation phrase or not.
In Ollie system, the authors use context analysis
to extract relations in a given sentence. It extracts
not only relations expressed via verb phrases, but
also relations mediated by adjectives and nouns.
ClausIE is the most recent Open IE system. It
differs from Reverb and Ollie approaches in that it
separates the detection of useful information
expressed in a sentence from their representation in
terms of extractions. ClausIE exploits linguistic
knowledge to first detect clauses in an input
sentence and to subsequently identify the type of
each clause according to the grammatical function of
its constituents.
However, in our domain extraction context, due
to its open-domain and open-relation, gross use of
Open IE algorithms is unable to relate the extracted
relations to domain ontology. Subsequently, an
adaptation of the used Open IE algorithms is
necessary to overcome this limitation.
To address this limitation, we have implemented
a solution based on the concepts of the taxonomy
already extracted in the previous stage. The
proposed process for learning non-taxonomic
domain relationships with Open IE tools is
performed in three steps:
Step1: For each concept of the taxonomy, we extract
from the corpus all the sentences where the concept
c is found.
Step2: For each extracted sentence, we discover all
possible relations using one of the proposed Open IE
algorithms. As an output, we obtain a set of
relational tuples <Arg1, Rel, Arg2> that describe the
sentence verb relation (Rel) and its arguments (Arg1
and Arg2).
Step3: Finally, we judged each tuple as related to the
studied domain or not, based on whether it contains
the concept c in one of the extracted arguments. The
selected relations are incorporated into the result
ontology.
This process is repeated until all concepts of the
taxonomy are processed.
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484
5 SYSTEM ARCHTECTURE
The proposed ontology learning system consists of
five basic components, namely: Corpus Pre-
processing Component (CPC), Data Pre-processing
Component (DPC), Algorithm Library Component
(ALC), Open IE Component (OIEC) and Ontology
Generation Component (OGC) (see Figure 2).
5.1 Corpus Pre-processing Component
This component aims to import the corpus into the
system and prepare it for processing. In a first stage,
k-means clustering algorithm (Jain et al., 1999) is
applied. It partitions the corpus into k clusters so that
two documents within the same cluster are more
closely related than two documents from two
different clusters. In a second step, an indexing
process is executed to index the full contents of the
clustered documents. Based on the neighborhood of
an initial keyword to select domain concepts and to
extract their taxonomic and non-taxonomic
relationships. The indexation allows an efficient and
fast retrieval of this information.
5.2 Data Pre-processing Component
In the Data Preprocessing Component, text
information is filtered and cleaned. The
preprocessing includes the following elements:
- Tokenization: Splitting strings into their
component words based on delimiters.
- Splitting compound words.
- Normalization: Elimination of stylistic
differences due to capitalization, punctuation,
word order, and characters not in the Latin
alphabet.
- Lemmatization: Elimination of grammatical
differences due to verb tense, plurals, etc. It’s
used to improve the recall of the domain
ontology concepts in the corpus.
- Stop word removal: Remove of very common
words. The Glasgow stop word list is used in this
work.
5.3 Algorithm Library Component
The Algorithm Library consists of a statistical
algorithm that (1) extracts candidate concepts from a
collection of documents and stores the sentences
where they are located; (2) evaluates the taxonomic
dependency between key concepts; (3) selects the
most relevant ones, (4) discovers non-taxonomic
relationships of the selected concepts (5) and
performs an iterative mining algorithm that
constructs the ontology.
5.4 Open IE Component
The Open IE component proposes three algorithms
to extract non-taxonomic relations: Reverb, Ollie
and ClausIE. It communicates with the previous
component "Algorithm Library" in input/output
mode. According to the Open IE algorithm chosen at
the beginning of the process, and from the sentences
provided by the "Algorithm Library", the Open IE
component identifies non-taxonomic relationships of
each concept of the taxonomy and returns verbs and
concepts that describe the identified relationships.
5.5 Ontology Generation Component
In this final phase, the resulted domain ontology is
generated in an OWL file format by using the Jena
toolkit. The resulting file can be visualized using an
ontology editor such as Protegé.
6 EVALUATION
The evaluation process is an essential step that
should be performed in any ontology learning
approach. It's particularly important in automatic
approaches as the present work. In this section, we
evaluate the performance of our methodology for
extracting non-taxonomic relationships for finance
domain using a gold standard ontology. We
implement our new system using three well-known
Open IE tools in the literature: Reverb, Ollie and
ClausIE. Then we compare it with Sanchez and
Moreno (2008) algorithm, in a finance corpus, to
evaluate the relevance of the resulting non-
taxonomic relationships of each algorithm.
Due to the lack of an evaluation corpora and an
ontology officially released and fully covering this
area, we decided to build our own corpus and gold
standard ontology. The corpus was automatically
constructed from the financial news Website
“Yahoo! Finance” and the gold standard ontology
was developed in a collaboration with a domain
specialist.
It should be noted that the comparison of the
finance result taxonomy of our algorithm with that
of Sanchez and Moreno and others related works
was already presented in a previous work (El idrissi
esserhrouchni et al., 2014). This comparison showed
that our algorithm was more efficient in building
Learning Non-taxonomic Relationships of Financial Ontology
485
finance concept hierarchies.
6.1 Gold Standard Ontology
The gold standard ontology was developed in two
steps. It was designed to give equal chances to all
tested algorithms. In the first step, the four candidate
algorithms (our three algorithms based on Open IE
tools and the Sanchez and Moreno algorithm) were
launched on the same constructed finance corpus.
Then, the resulting ontologies were automatically
merged into a single one. Redundant concepts and
relations were exported only once. Our aim is to
build a domain ontology based on the previous four
ontologies. In the second step, the constructed
ontology was presented to a domain specialist for
validation. He performs a cleanup of the constructed
ontology and removes erroneous concepts and
invalid taxonomic and non-taxonomic relationships.
The final ontology was used as a reference ontology
for our evaluation process.
The number of the extracted non-taxonomic
relationships are shown in Table 1. Table 2 shows
the number of non-taxonomic relationships in the
Gold standard ontology after the specialist validation
and the refinement of the merged ontology.
6.2 Finance Domain Corpus
We built the test corpus from the financial news
Website “Yahoo! Finance”. A Java program was
developed to retrieve financial articles from it. We
based on a period of 100 days since January 1, 2015
to retrieve randomly new articles. 7213 documents
were retrieved for that period, which corresponds to
over 11 million words. One of the extracted
document is shown in Figure 3.
6.3 Algorithms Implementation
We programmed our new algorithm using Java 8.
Regarding the integration of Open IE algorithms in
the domain ontology learning process, we used the
original Java source code published by their owners
and we adapted them to extract domain relations.
In the absence of the source code of the
algorithm of Sanchez and Moreno, and to perform
the comparison with our algorithm, we developed
their algorithm from scratch in Java 8 by referring to
its description in their paper.
6.4 Evaluation
To evaluate the relevance of the extracted non-
taxonomic relationships by the studied algorithms
we have used the Lexical Precision measure (LP). It
measures the number of relevant relations extracted
e
relevant
divided by the total number of relations
extracted e
all
. LP is defined as (Sabou et al., 2005):
(8)
In the interest of a fair comparison, all algorithms
were executed under the same conditions. The test
was performed on a 64-bit windows machine, with 8
GB of RAM.
The results given by the different algorithms are
summarized in Table 3.
Table 1: Number of generated non-taxonomic
relationships.
Source
Our
method
using
Reverb
Our
method
using
Ollie
Our
method
using
ClausIE
Sanchez
&
Moreno
Merged
ontology
Number of
extracted
non-
taxonomic
relations
17 20 17 13 42
Table 2: Number of non-taxonomic relationships in the
gold standard ontology.
Source Gold standard ontology
Number of non-taxonomic relations 29
According to those results, we observe that our
method, used with the three Open IE algorithms, is
more precise than that proposed by Sanchez and
Moreno with over 30% of difference.
However, using the open IE algorithm reverb,
our method reaches its best performance and
achieved an accuracy of 82.35%. It outperforms
Ollie and ClausIE in term of precision of the
extracted non-taxonomic relationships. Table 4
shows an extract of the learned non-taxonomic
relationships using reverb with our proposed
algorithm.
Table 3: Precision evaluation of non-taxonomic
relationships performed on a financial corpus.
Algorithm
Number of
rejected non-
taxonomic
relations
Number of
accepted non-
taxonomic
relations
Precision
Our method using
Reverb Open IE
3 14 82,35%
Our method using
Ollie Open IE
4 16 80,00%
Our method using
ClausIE Open IE
4 13 76,47%
Sanchez & Moreno 5 8 61,54%
SSEO 2015 - Special Session on Enterprise Ontology
486
Car loan: green light
Even if you can afford to pay for a car with cash, you may want to consider paying it out over time. If your
credit is good, you may qualify for a very low-interest loan that could help your credit mix and eventually
boost your credit score even higher.
You can get car loans from several different sources. Most dealers are affiliated with finance companies
that offer loans. You can also get a loan through your bank or credit union. The better your credit score,
the lower the interest rate you'll be offered.
If your score is less than stellar, consider using a credit union. Interest rates at federal credit unions are
limited by law to 18 percent, and may be more reasonable than rates at buy-and-drive car lots or finance
companies, says Rex Johnson, owner and founder of Lending Solutions Consulting Inc., a credit-union
consulting firm in Elgin, Ill.
Figure 3: A partial example of an extracted document from Yahoo! Finance web site.
Table 4: An extract of the learned non-taxonomic
relationships using reverb with our proposed algorithm.
The extracted relations
Subject (NP) Verb (VP) Object (NP)
finance company
provide loan
credit to be get from finance company
mortgage finance
solution
include loan
company will pay finance debt
corporate bond help company financing
dealer Be affiliate with Finance institution
broker Be affiliate with Finance institution
7 CONCLUSION
This paper presented a new methodology for
learning non-taxonomic relationships and building
financial ontology from scratch. One of the main
difficulties of domain ontology learning is the
identification of relevant non-taxonomic relations.
The novelty of our approach in this field is that it is
based on adjusting Open IE algorithms to extract
and label domain relations between concepts. The
obtained results show that using Open IE tools is an
interesting way to extract ontological relationships
with a higher degree of precision.
For the implementation of our methodology, we
integrated three well-known Open IE algorithms in
our process of extracting non-taxonomic domain
relationships, namely: Reverb, Ollie and ClausIE.
The approach has been evaluated in two steps: (i)
using a gold standard ontology. In this case, the
obtained results show that Reverb enables to achieve
best performance and outstrips Ollie and ClausIE in
terms of accuracy of the extracted non-taxonomic
relationships. (ii) By comparing the discovered non-
taxonomic relationships by our approach to those
extracted by Sanchez and Moreno algorithm on the
same finance corpus. In this latter test, our method
was more precise and obtained the best results.
However, the number of the extracted relationships
by the different methods remains limited. This is due
to the complexity of the financial domain and the
number of documents in the corpus. Indeed, the size
and the quality of the corpus are important criteria
for the success of an ontology learning tool. Also,
changing the corpus might be interesting to test if
the performance of our approach remains the best
from one domain to another.
In terms of perspective, our future work will
consist of increasing the size of the finance corpus in
order to build a richer ontology for finance domain.
We plan its validation by using it in a decision
making application in the financial sector. Also, our
current work consist in the online publication of our
algorithm as a web application on the address
www.ontologyline.com. This will allow the
ontological research community to test the algorithm
and evaluate it in various domains.
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