A Proposal for a Method of Graph Ontology by Automatically Extracting
Relationships between Captions and X- and Y-axis Titles
Sarunya Kanjanawattana
and Masaomi Kimura
Functional Control Systems, Shibaura Institute of Technology, 3-5-7 Koto-ku Toyosu, Tokyo 135-8548, Japan
Information Science and Engineering, Shibaura Institute of Technology, 3-5-7 Koto-ku Toyosu, Tokyo 135-8548, Japan
Relationship, OCR, Ontology, Triple, Edit Distance, Dependency Parser.
A two dimensional graph is a powerful method for representing a set of objects that usually appears in many
sources of literature. Numerous efforts have been made to discover image semantics based on contents of
literature. However, conventional methods have not been fully able to satisfy users because a wide variety of
techniques are being developed, and each is very useful for enhancing system capabilities in their own way. In
this paper, we have developed a method to automatically extract relationships from graphs on the basic of their
captions and image content, particularly from graph titles. Furthermore, we improved our idea by applying
several technologies such as ontology and a dependency parser. The relationships discovered in a graph are
presented in the form of a triple (subject, predicate, object). Our objectives are to find implicit and explicit
information in the graph and reduce the semantic gap between an image and literature context. Accuracy was
manually estimated to identify the most reliable triple. Based on our results, we concluded that the accuracy
via our method was acceptable. Therefore, our method is dependable and worthy of future development.
Image ontologies are a challenge for several areas of
research, particularly in image processing and ontol-
ogy. Images are very useful resources that contain
specific information about their characteristics, such
as shape and color. The most powerful tool for repre-
senting data objects is a graph, which is often used
to summarize data and present results in academic
literature. As humans, we can interpret information
from graphs with ease because we are intelligent. In
contrast, it is a difficult task for a computer to auto-
matically analyze and extract any information from
graphs. Thus, this topic is interesting because the
information contained in graphs can provide positive
contributions to enhance users understanding. More-
over, a system that can support this automation will
surely provide benefits to users. However, there is a
major problem which we need to tackle in this study,
which is the semantic gap which occurs when we at-
tempt to identity semantics of figures. This is always a
critical problem for many previous studies which per-
forms with visual features or image contents (Zhao
and Grosky, 2002).
In recent years, pattern recognition has become a
critical topic in image processing. It is used to rec-
ognize patterns and regularities in data. This tech-
nique is often utilized in several areas such as medi-
cal science and computer-aided diagnosis systems. A
system can remember a human face from a photo by
repeatedly learning the patterns of the images (Hsu
et al., 2002). Moreover, pattern recognition has been
applied as a classic method for recognizing text-based
characters in images, i.e., optical character recogni-
tion (OCR). This method is applied in many appli-
cations such as medical application (Alday and Pa-
gayon, 2013).
This pattern recognition is a necessary part of ex-
tracting a graph’s text-based components, including
titles of axes. However, we considered that pattern
recognition by itself was inadequate for making our
study successful. Therefore, we applied another sig-
nificant technique, called ontology, which is an essen-
tial part of our work. The common definition of ontol-
ogy is the specification to describe concepts and their
relationships that enables the sharing and reusing of
knowledge (Gruber, 1993). The results of our study
are presented in the basic form of a triple, which com-
prises subject, predicate and object.
In this study, we propose the method to generate
the ontology of graphs with their captions and graph-
ical contents. The content of the graph used in this
Kanjanawattana, S. and Kimura, M..
A Proposal for a Method of Graph Ontology by Automatically Extracting Relationships between Captions and X- and Y-axis Titles.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 2: KEOD, pages 231-238
ISBN: 978-989-758-158-8
2015 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
study was 2-dimensional graph including the X- and
Y-axis titles. The main objectives of our study are
to extract the explicit and implicit relationships from
the content of graphs and captions and to reduce the
semantic gap. The system not only created explicit
triples but also generated implicit triples. Explicit
triples are triples containing tokens (words in a title)
that match the apparent word(s) in the first sentence of
caption. Implicit triples have tokens whose relations
cannot explicitly be obtained by finding the shared
words. Our system produces implicit triples using the
fact that the titles of axes are strongly related. If our
system creates a explicit triple by finding a keyword
shared by the caption with its Y-axis title in a graph,
though it could not find a keyword shared with its X-
axis title, it generates implicit triples by replacement
of the shared keyword to the X-axis title.
In this section, we summarize existing studies that
have inspired and motivated our study. The method
we developed in this study combines several ap-
proaches to enhance the overall abilities of the sys-
tem to describe significant image semantics. Note
that our dataset was collected from the publications of
PubMed, which contains huge dataset and databases
related to biology.
Although existing studies have proposed numer-
ous techniques related to image ontologies, they con-
tinue to only utilize the content of the associated lit-
erature. Soo et al. proposed a framework that facil-
itated image retrieval based on sharable domain on-
tology and thesaurus. It used users’ keywords and
retrieved images which matched with their annota-
tion. Moreover, Fan et al. presented a hybrid re-
trieval method based on the keyword-based annota-
tion structure, combining ontology-guided reasoning
and probabilistic ranking. Their image search systems
provided results matching end-user queries. However,
the ontologies of them applied semantic annotations
to existing image resources, but it did not identify im-
portant contents residing within images. To use the
contents within images, a method to detect compo-
nents of a graph was necessary. Based on this existing
image processing technique, Kataria et al. presented
a method to automatically extract data and text from
data plots inside graphs based on the spatial area of an
image. It only extracted text from graph components
(e.g., data plots, a legend and axes titles), but it did
not study about an image semantic that was different
from our study. Still, it provided a good idea to extract
data components from graphs.
Semantic gap occurs between an image and con-
text, and it is a serious problem. This semantic
gap separates the high-level understanding and inter-
pretation available via human cognitive capabilities
from the low-level pixel-based analysis of computers,
which depend on mathematical processing and artifi-
cial intelligence methods. Building ontology is a way
to reduce the discontinuity between human and ma-
chine understanding. Xu et al. developed a search
engine to find specific images based on text indexes
inside biological images. It proved that not only can
the content of the literature be utilized, but also can
the content within images. Therefore, our system can
use contents from both caption and image to identify
semantic relationships.
Fortunately, image processing techniques suggest
a possible solution as detecting and recognizing text
in complex images and video frames has long been a
well-known topic in image processing, and it contin-
ues to rapidly grow (Chen et al., 2004). Furthermore,
the mentioned abilities of image processing is essen-
tial for our study, specially, a suitable tool for our
study is OCR. Some previous studies have proposed
several efficient methods for extracting elements from
within an image or graph such as (Kataria et al., 2008)
and (Huang et al., 2005). A basic idea was to individ-
ually recognize text and graphics in an input image
and combine the information components to achieve
a full understanding of the input image. Based on
these previous studies, we observed that the extracted
graph components can be a critical part of ontology
for addressing the semantic gap between images and
The ontology is designed to be used by many sys-
tems that analyze the content of information instead
of just presenting the information to human (McGuin-
ness et al., 2004). Moreover, the ontology is a promis-
ing technology for enriching the semantics of images.
Several methods and tools have been proposed for de-
scribing the concepts between other sources and have
used the ontology to improve their systems in many
areas, such as routine clinical medicine and medi-
cal research. Although some previous studies de-
veloped a method for combining images and seman-
tic, a semantic gap remained. Some studies have fo-
cused on addressing this problem using low-level fea-
tures extraction and keyword-based approaches be-
cause they believed that the use of the image fea-
tures could narrow the semantic gap between images
and context ((Deserno et al., 2009), (Mezaris et al.,
2003)). In our study, we realized that features, such
as color or texture, are trivial matters for analyzing
the graph; instead, we considered alphabet characters
in the graph. Results of our study are presented in the
KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development
form of triples, which are the simple form of ontology.
Rusu et al. presented an approach for extracting the
subject-predicate-object form of triples from English
sentences using four parsers. One of them was the
Stanford Parser, which is also applied in this study. It
is a tool to parse a grammar of sentence. It can detect
types of words in a sentence such as nouns or prepo-
3.1 Dataset and Database Design
The dataset applied in this study is a collection of
graphs related to biology; specially we collected our
data from PubMed. Graph types are differentiated
into two distinct types: line graphs (Figure 1(a) and
1(b)) and bar graphs (Figure 1(c)). Observing the
characteristics of line graphs, we concluded that the
titles of both axes can be applied to our system’s in-
put. However, characteristics of bar graphs have an
important difference from those of line graphs, as pre-
sented in Figure 1(b), because the bar graph only has
a title associated with the Y-axis. For the X-axis of
bar graph, only some categories exist, a title is not
present. Therefore, our system only uses Y-axis titles
to generate explicit triples for bar graphs.
3.2 Method
In this study, we propose the innovative method to ex-
tract implicit and explicit relationships of graphs de-
pended on graph contents and their captions. We un-
derstood that every graph contains necessary informa-
tion and concept relationships. There were two kinds
of relationships that we consider in this study: explicit
relationships and implicit relationships. The explicit
relationship is represented by a triple describing the
concept relationships explicitly contained in the cor-
responding graphs. For our current study, we also
create the implicit relationship by use of the explicit
relationship and the relationship that always appears
between X- and Y-axis of graphs. This implicit rela-
tionship should be extracted particularly when faced
with a specific situation. For example, we imagined a
keyword only shared between tokens from a caption
and those from Y-axis titles, but not in those from X-
Our automatic system was separated into three
main steps as illustrated in Figure 4. The first step was
to load images and apply functions of the image pro-
cessing library, shown as in Step (a) and (b) of Figure
4. The key of this step was a preparation of the input
Figure 1: An illustration of both types of images employed
in this study, including (a) and (b) line graphs containing
titles on both the X and Y axes and (c) a bar graph contain-
ing a title only on the Y axis. (a) refers to (Delorme et al.,
2012), (b) refers to (Nekooeian et al., 2014), and (c) refers
to (Sun et al., 2014).
data. We adjusted the original images by converting
them to gray scale images to improve the accuracy of
OCR (Rice et al., 1995). However, we found a critical
problem during OCR, wherein the converted text from
an image was placed in a disorderly manner. Thus, the
resulting input data comprised graphs that contained
text distributed in random positions.
To overcome this difficulty, we performed image
segmentation via horizontal and vertical partitioning.
The number of partitions was a constant value de-
fined by the user. This process is the third step of
Step (b) in Figure 4. Since we considered only the X-
and Y- axes titles, our system selected suitable a piece
of sliced image that commonly contained titles such
as the first vertical slice, which comprised the Y-axis
title, and the last horizontal slice, which comprised
the X-axis title. These selected pieces became the in-
put to the OCR process. We applied an OCR library
named tesseract in this study because it is an accurate
A Proposal for a Method of Graph Ontology by Automatically Extracting Relationships between Captions and X- and Y-axis Titles
open source OCR engine available. Even though the
tesseract is a good OCR library, some errors still hap-
pened in a character recognition process. Therefore,
we applied the edit distance to enhance system per-
formance, as illustrated at Step (c) of Figure 4. This
step measured the distance cost of two strings, such
as a string from the X-axis title and a string from the
first sentence of the caption. Moreover, the compared
string was replaced by one that contained the mini-
mum distance. Note that the edit distance is the opti-
mal method for decreasing OCR errors. As a result,
a number of correct tokens from axes title that were
results of OCR is increased, and we have a higher op-
portunity to obtain a right token to form a triple. Thus,
a precision may be increased.
As shown in Step (d) and (e) of Figure 4, the final
step included the generation of the dependency parse
tree using the Stanford dependency parser, and then,
created the triples. Figure 2 shows an example of a de-
pendency parse tree and typed dependency of Inhibi-
tion of Lip-HSL proteins by MmPPOX(Delorme et al.,
2012). Here, the subject was selected from the first
noun discovered in the first sentence of the caption
because the main idea of a paragraph usually exists
in the first sentence. The dependency parse tree pro-
vided the first noun of the sentence as the first word
with the tags, NN and NP. Furthermore, our system
tokenized the titles of both axes and received the to-
kens of titles that matched words in the caption. For
simplicity, we ignored some manually identified irrel-
evant tokens, which will only cause mismatches, be-
cause they would be meaningless and even detrimen-
tal to our study. The matched tokens were the object
of the triple. To complete the triple, we used the first
verb of the sentence as the predicate. If the first verb
did not show up, the system instead used the given
preposition instead. Figure 3 demonstrates an exam-
ple of triple extraction, wherein the first noun of the
first sentence is Inhibition.
Moreover, we established a method to generate
another triples considering these relationships that we
called implicit triples. Note that, the implicit triple
was generated when the system extracted at least a
triple from either X- or Y-axis title. Regarding our
idea, we created the implicit triples by using the prior
explicit triples. We extracted candidates of the ex-
plicit triple and formed them as candidates of new im-
plicit triples, i.e., subject and predicate. About the ob-
ject of implicit triples, we analyzed tokens from a title
mismatching between a title and the first sentence of
caption by choosing proper token(s), e.g., a noun and
compound word. Following by the above mentioned,
the object was selected from tokens of mismatched
title. Finally, the new implicit triples were created.
Figure 2: An example of a dependency parse tree from a
caption in (Delorme et al., 2012). The first noun of this
sentence was Inhibition; hence, it became the subject of the
triple associated with the specific caption.
Figure 3: An example of the triple extraction process.
The main objective of this study was to discover the
relationships in X- axis title, Y-axis title and a cap-
tion. Our dataset consisted of graphs related to biol-
ogy contained in literature published in PubMed. We
conducted an experiment and estimated how well our
method provide correct triples.
We divided our dataset into two groups, i.e., a set
of bar graphs and a set of line graphs. The data was
gathered from 18 different documents, and the num-
ber of input data was 36 instances, which comprised
10 bar graphs and 26 line graphs. Table 1 presents the
number of titles that contained one or more keywords.
KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development
Figure 4: The overall method of implementation: (a) selecting and preparing the image; (b) partitioning the images and
obtaining both titles via OCR; (c) modifying the extracted titles using the edit distance algorithm; (d) parsing the first sentence
of a caption via the Stanford parser and receiving the typed dependency and dependency tree; and (e) identifying the predicates
and storing them in the database.
A Proposal for a Method of Graph Ontology by Automatically Extracting Relationships between Captions and X- and Y-axis Titles
Table 1: A summary of the number of titles that one or more
keywords appeared in.
Keyword location Bar graph Line graph
X axis - 18
Y axis 8 18
Both axes - 14
Neither axis 2 5
Total graphs 10 26
Table 2 depicts the size of the data in which all
keywords appeared in the first sentence of the caption.
Our system ignored keywords that caused an OCR er-
ror, as well as unnecessary keywords such as an arti-
cle, a numeral adjective, a number, a preposition, or a
Table 2: A summary of the number of titles that all key-
words appeared in.
Keyword location Bar graph Line graph
X axis - 9
Y axis 6 12
Both axes - 4
Consequently, we obtained 52 triples. We ob-
served that some captions produced several triples,
whereas other captions did not produce any triples.
We received six triples from four captions of the bar
graphs and 46 triples from 17 captions of the line
graphs. Table 3 presents the number of correct sub-
ject extractions. The method we used to evaluate the
subject was to manually read all sentences of the cap-
tions,i.e., not just the first sentence, to judge whether
the extracted subject was suitable as the subject of a
triple. Note that for the validated subjects in Table 3,
we removed duplicate values of subject and obtained
21 unique subjects comprising four subjects from the
bar graph and 14 subjects from the line graph. More-
over, the accuracy of the first noun in the sentence
chosen as subject was as high as 0.81.
Table 3: Correctness of subject extraction.
Bar graph Line graph
Correct extraction 3 14
From total 4 17
Tables 4 and 5 show the correctness of the triple
extraction. Table 4 shows all obtained triples; the
accuracy in this case was low, 0.36. Investigating
the reason of the low accuracy, we found that we
needed to exclude the effect of mistakes caused by
the Stanford parser, since we obtained some improper
triples in our results because of the words incorrectly
tagged by the parser. Table 5 shows the number of
triples without parser errors; the accuracy of this case
was 0.76. Clearly, the accuracy of extracting triples
without parser error was substantially higher. From
the results shown in Tables 4 and 5, the numbers of
correctly extracted triples were coincidentally equal,
though they may not be equal in general. This shows
correct triples were suitably extracted without the ef-
fects of errors from the tools. The rest of the incor-
rect triples contained a few errors, such as identifying
tokens with incorrect recognition and obtaining an in-
correct predicate due to a parser error.
Table 4: The correctness of triple extraction.
Bar graph Line graph
Correct triples 2 17
All triples 6 46
Table 5: The correctness of triple extraction without parser
Bar graph Line graph
Correct triples 2 17
All triples 3 22
To evaluate our system, we manually measured
its precision and accuracy by counting the number of
triples that can be correctly identified by our system.
Equation (1) indicates the precision measures the ratio
of relevant triples extracted by our system, and Equa-
tion (2) defines the accuracy which we have already
We should notice that implicit triples were ob-
tained only for line graph, since they were created
when either tokens formed by the X- or Y-axis titles
was not detected in the first sentence of the caption.
From our experiments, we obtained 9 implicit triples
from 7 line graphs. We concluded that 6 from 9 im-
plicit triples were precisely implied. Thus, we assert
that the precision of our study was 0.67.
precision =
relevantTriples retrievedTriples
accuracy =
The results revealed that our system provided satis-
factory accuracy and precision. As we mentioned, af-
ter ignoring triples wrongly obtained because of the
incorrect tagging by the parser, we found the accu-
racy to be 0.76. We compared this to the result of the
method proposed by Soo et al. whose accuracy was
KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development
0.6. Comparing the approaches, we consider the dif-
ference might come from whether the keywords were
obtained from graph images themselves or obtained
from user-assigned keywords. It is clear that the for-
mer gives the keyword strongly related to the infor-
mation in the graphs, though the latter might contain
the less related keywords.
The values of precision of our method were 0.67,
which was greater than the previous study, 0.55. From
our opinion, we may obtain the better results, if we
improve the OCR process and adapt the idea to get
tokens from the caption (subject) by selecting other
keywords which have specific names, such as a name
of protein or chemical material.
We also observed limitations during the process.
The first limitation was about the pattern of input
data. We partitioned the input into three distinct pat-
terns: (1) one or more tokens of the title of either the
X- or Y-axis appearing in the first sentence of the cap-
tion; (2) one or more tokens of the title of both axes
appearing in the first sentence of the caption; and (3)
no token appearing in the first sentence of the caption.
Our system supports inputs with patterns (1) and (2).
For inputs with pattern (3), we need to extend our idea
in future studies to find the relationship for all sen-
tences in the captions, instead of only the first as we
did, as tokens in titles may appear in other sentences.
Moreover, it is significant to understand the pattern of
input. Hence, a text mining algorithm may be a can-
didate to solve this problem, because it can discover
patterns from unstructured data.
The second limitation arises when the subject and
object are coincidentally the same word. We found
only a few such cases in our study, and four they only
gave negligible affects on our results.
The third limitation was that our method was ap-
plicable to inputs containing a single graph. Under
this condition, we could clearly understand what the
caption meant. If multiple graphs were present in an
image, it became be difficult to identify which part of
the image the caption intended to explain. A method
for solving this problem is still a question that should
be addressed in future studies.
In this study, we proposed the method to extract
triples from graphs. Our main objective was to ad-
dress the difficulty of finding relationships between
axis titles and a caption.
We applied OCR to extract the text inside the
given graphs, but errors from incorrect recognition
occurred. The edit distance was employed to reduce
these errors by measuring the similarity between to-
kens in titles and a caption. The token with a min-
imum distance was used to replace incorrect outputs
of the OCR process.
Furthermore, we differentiated the dataset into
two groups: one group containing bar graphs and
the other containing line graphs. We observed that
the system could only utilize the Y-axis title in the
bar graphs because the X axis established individ-
ual categories, and not a single title. Unlike bar
graphs, we could use both titles of the axes in the line
graphs. Therefore, the explicit triples extracted from
bar graphs were created from the Y-axis title only. We
then decided to not create implicit triples from the bar
graphs in this study. In addition, we obtained explicit
triples and implicit triples from line graphs.
Overall, each triple comprises a tuple containing
subject, predicate and object. The subject was the
first noun of the first sentence of the caption. The de-
pendency parse tree was the crucial tool for defining
the predicate. The first verb of the first sentence of
the caption represented the predicate. If we could not
detect a verb in the sentence, we instead selected the
nearest preposition. The object came from tokens ex-
tracted from the titles of the axes of the graph. These
tokens also matched the words in the caption.
Finally, the system could create explicit triples.
On the other hand, the generation of implicit triples
was more difficult, occurring when nothing matched
the words of the caption. We believe that the graph
itself had obvious relationships between axes. There-
fore, we created meaningful implicit triples.
Consequently, our developed method was accu-
rate and reliable, because it provided dependable ac-
curacy and precision.
For our future direction, we will be extended our
method to support generic graphs such as pie graphs
and area graphs by investigating new techniques of
detecting types of graphs and extracting semantic in-
formation from graphs.
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