=Paper=
{{Paper
|id=Vol-3063/oaei21_paper3
|storemode=property
|title=AgreementMakerDeep results for OAEI 2021
|pdfUrl=https://ceur-ws.org/Vol-3063/oaei21_paper3.pdf
|volume=Vol-3063
|authors=Zhu Wang,Isabel Cruz
|dblpUrl=https://dblp.org/rec/conf/semweb/WangC21
}}
==AgreementMakerDeep results for OAEI 2021==
https://ceur-ws.org/Vol-3063/oaei21_paper3.pdf
AgreementMakerDeep Results for OAEI2021
Zhu Wang1 and Isabel F. Cruz1 ?
ADVIS Lab, Dept of Computer Science
University of Illinois at Chicago, Chicago IL 60607, USA
zwang260@uic.com
isabelcfcruz@gmail.com
Abstract. AgreementMakerDeep (AMD) is a new flexible and exten-
sible ontology matching system with knowledge graph embedding tech-
niques. AMD learns from classes and their relations between classes by
constructing vector representations into the low dimensional embedding
space with knowledge graph embedding methods. The results demon-
strate that AMD achieves a competitive performance in a few OAEI
tracks, but AMD has limitations for property and instance matching. 1
1 Presentation of the system
AgreementMakerDeep (AMD) is a new ontology matching system inspired by
AgreementMaker [2, 3], AgreementMakerLight (AML) [7] and BootEA [19]. This
year is the first time that AMD participates in OAEI. It is designed with the main
goal of higher efficiency for ontology matching problems by applying knowledge
graph embedding methods.
1.1 State,purpose, general statement
Ontology matching aims to establish semantic correspondences or relationships
between concepts or properties of different ontologies [6]. There is a wide range
of algorithms developed for ontology matching, such as those that use lexical
similarity with linguistic techniques [12], partition large ontology sets based on
structural proximity [10], or detect graph similarity [5, 14]. However, such strate-
gies may be time consuming [9], may use sparse and a high-dimensional training
space [17], and may vary with the domains [1].
AMD mainly utilizes string-based techniques [4] and lexical matching al-
gorithms [15], but adopts the representative learning models [8] to capture the
relations as structural information with a translation vector between two classes.
?
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
1
This paper is dedicated to the memory of Isabel F. Cruz.
2 Zhu Wang and Isabel F. Cruz
Fig. 1. The framework of AMD.
2 Specific Techniques Used
The architecture of AMD is shown in figure 1, including ontology parsing, string
and lexical matching, knowledge graph embedding, model learning and candidate
selection.
Ontology parsing owlready2 [11] is used to extract meta information of
classes from the source and target ontology, such as super/sub-classes, labels,
annotations, partof and disjointwith. BeautifulSoup [16] is used to extract syn-
onyms.
String and lexical matching We apply several text per-processing tech-
niques like stop-words removal and tokenization on class labels and annotations.
AMD uses the Base Similarity Matcher (BSM) [5] and lexical matching algo-
rithms to obtain a baseline class alignment.
Knowledge graph embedding and model learning We characterize
the structure information of ontologies by relations translated from one class
to another class using a modified TransR [13] model into relational embedding
spaces.
Problem Formulation Given two ontologies O and O’, we construct knowledge
graph X and Y, and define the correspondence between two concepts as following
triplets Tc,c0 = < c, r, c0 >, where r is the relation between c and c’. The problem
is to find mapping set M = {(cx , cy )X × Y |cx ≡ cy }. In this study, we focus on
one-to-one alignment and the relation between concepts is equality.
Let ~v (cx )= {v1 , v2 , ...vm } and ~v (cy )= {v10 , v20 , ...vn0 } be two d-dimensional vec-
tors sets of size m and n, we compute their distance with simple cosine similarity
by d(~v (cx ), ~v (cy )) = 1-sim(~v (cx ), ~v (cy )) as follows:
X
sim(~v (cx ), ~v (cy )) = arg max cos(~v (cx ), ~v (cy )) (1)
j
i=1
We define the probability of the aligned labels between concepts cx and cy by
p(cy |cx ) as follows:
p(cy |cx ) = σsim(~v (cx ), ~v (cy )) (2)
AgreementMakerDeep Results for OAEI2021 3
where σ is the sigmoid function.
Knowledge graph embedding In AMD, we apply a modified TransR method
which translates concepts and relations into concept space and relation-specify
concept spaces, since there are multiple relations in the ontologies e.g subclassof
and disjointwith. In the original TransR, the projected vectors are defined as cr =
cMr , c0r = c0 Mr , and the score function as fr (c, c0 ) = kcr + r − c0r k22 [13]. Inspired
by Sun et al. [19], the absolute scores of positive triples are lower than the
negative ones, so we modify the loss function by using two γ hyper-parameters
as follows:
X X
L= max(0, (fr (cx , c0x ) − γ1 ) − µ(f (cy , c0y ) + γ2 ) (3)
(cx ,r,c0x )∈S1 (cy ,r,c0y )∈S2
where γ1 , γ2 , µ > 0 and γ2 > γ1 , S1 is the positive triples set and S2 is the
negative triples set. We set different γ values to ensure absolutely low margin
loss scores in the positive triples for reducing the drift of the embedding and
also keep the function of the margin-based ranking loss.
During the process that computes vectors, we need to generate negative
triples. Following the work of Sun et al. [19] and Li et al. [12], we refine the
uniform negative sampling by choosing from the k-nearest neighbors in the em-
bedding space, and setting constraints of select candidates excluding from the
subclassOf or disjointWith related concepts. In this way, we can avoid vector
sparsity and obtain better quality of vector representations for the concepts.
Candidate selection We select candidates based on a threshold of the
classes knowledge graph embedding vectors similarity, and then compare the
similarity with baseline if the pairs are in baseline result sets.
2.1 Parameter settings
In AMD, we use stochastic gradient descent as the optimizer and configure hyper-
parameters as listed: dimensions are set to 200 for the vectors. The learning rate
is among {0.01,0.02,0.001}, and mini-batch is {5,10}. γ1 = {0.01,0.05,0.1}, γ2 =
{0.5,1.0}. The number of nearest neighbors for negative sampling is {5,10,20}.
From the local evaluation results on the Anatomy track, the best parameter
set is as follows: the learning rate is 0.01, mini-batch is 10, γ1 is 0.01, γ2 is 0.5
and 10 nearest neighbors for the negative sampling.
2.2 Adaptations made for the evaluation
Our framework uses Python with Tenserflow2 and RDFLib 3 , and is packed for
SEALS using MELT. We use the best parameter set in local alignments for the
OAEI submission, see section 2.1.
2
https://www.tensorflow.org/
3
https://github.com/RDFLib
4 Zhu Wang and Isabel F. Cruz
3 Results
3.1 Anatomy
The Anatomy track results of AMD are shown in Table 1. AMD returns 1167
correspondences in 3 seconds. The result shows that AMD can be competi-
tive among the top promising matching systems, especially in terms of runtime
and precision. AMD is the second fastest system in this track and a slightly
higher(0.004) precision than AML.
Table 1. AgreementMakerDeep results in the Anatomy track.
Runtime Precision Recall F-measure
AMD 3 0.96 0.739 0.835
3.2 Conference
The Conference track results of AMD are shown in Table 2. As expected, the
performance of AMD in the conference track is not good, with the F-measure
only slightly higher when comparing baseline method(StringEquiv). AMD shows
a lack of ability to extract and match the properties in M2 and M3 evaluation
variants. However, AMD has higher values in term of Precision in most tasks.
Table 2. AgreementMakerDeep results in the Conference track.
Precision Recall F-measure
ra1-M1 0.87 0.51 0.64
ra1-M3 0.87 0.43 0.58
ra2-M1 0.82 0.48 0.59
ra2-M3 0.82 0.39 0.53
rar2-M1 0.81 0.48 0.6
rar2-M3 0.81 0.41 0.54
3.3 Largebio
Table 3 shows results of AMD in the Large BioMed track. Our workstation
was able to complete one task and finished the other larger tasks with an out of
memory exception. In FMA-NCI small fragment task, AMD achieves a promising
performance with a F-measure of 0.906.
3.4 Knowledge Graph
AMD is able to complete two of the five tasks with a runtime of 37 minutes, and
AMD only returns class correspondences with a precision of 1.0.
AgreementMakerDeep Results for OAEI2021 5
Table 3. AgreementMakerDeep results in the Largebio track.
Precision Recall F-measure
FMA-NCI small 0.973 0.848 0.906
4 General comments
4.1 Comments on the result
2021 is the first time that AMD participates in OAEI, and performs promising
results. Overall, the results show that AMD is able to complete several tasks in
different domains on class-level matching in a timely manner. It is a fast system in
most of tracks. Hence, we have shown that knowledge graph embedding is helpful
to decrease computation time and that it leads to a competitive performance
in term of F-measure in Anatomy and LargeBio tracks. AMD has consistently
had higher precision than AML in a few tracks. However, AMD is still under
development that it is only able to return class correspondences. Moreover, AMD
has memory issues for large scale datasets and is not able to match properties
and instances in the current stage.
4.2 Improvements
The current development of AMD touches on several aspects. Besides considering
properties and instances matching, we will utilize joint embedding to combine
contextualized knowledge graph embeddings like coKE and BERT and additional
knowledge resources such as WebIsA [18] as a lexicon database. Moreover, we
will adapt AMD with different data types parsing and parameters selections for
different tracks.
5 Conclusions
In this paper, we have introduced a new ontology matching system called AMD.
We adapted a modified transR model to fit the ontology matching problem: thus,
we learn low-dimensional embeddings for each class and relation to capture the
hidden semantics of ontologies, rather than measuring the similarities between
classes directly, as in other traditional systems. AMD makes full use of the
textual and structure knowledge of ontologies. The results demonstrate the high
efficiency and the promising performance of our proposed matching method as
compared to other systems results in several tracks.
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