=Paper=
{{Paper
|id=Vol-2536/oaei19_paper10
|storemode=property
|title=Lily Results for OAEI 2019
|pdfUrl=https://ceur-ws.org/Vol-2536/oaei19_paper10.pdf
|volume=Vol-2536
|authors=Jiangheng Wu,Zhe Pan,Ce Zhang,Peng Wang
|dblpUrl=https://dblp.org/rec/conf/semweb/WuPZW19
}}
==Lily Results for OAEI 2019==
https://ceur-ws.org/Vol-2536/oaei19_paper10.pdf
Lily Results for OAEI 2019?
Jiangheng Wu, Zhe Pan, Ce Zhang, Peng Wang
School of Computer Science and Engineering, Southeast University, China
{jiangh_wu, pwang} @ seu.edu.cn
Abstract. This paper presents the results of Lily in the ontology align-
ment contest OAEI 2019. As a comprehensive ontology matching system,
Lily is intended to participate in three tracks of the contest: anatomy,
conference, and spimbench. The specific techniques used by Lily will be
introduced briefly. The strengths and weaknesses of Lily will also be
discussed.
1 Presentation of the system
With the use of hybrid matching strategies, Lily, as an ontology matching sys-
tem, is capable of solving some issues related to heterogeneous ontologies. It can
process normal ontologies, weak informative ontologies [1], ontology mapping de-
bugging [2], and ontology matching tunning [3], in both normal and large scales.
In previous OAEI contests [4–10], Lily has achieved preferable performances in
some tasks, which indicated its effectiveness and wideness of availability.
1.1 State, purpose, general statement
The core principle of matching strategies of Lily is utilizing the useful information
correctly and effectively. Lily combines several effective and efficient matching
techniques to facilitate alignments. There are five main matching strategies: (1)
Generic Ontology Matching (GOM) is used for common matching tasks with
normal size ontologies. (2) Large scale Ontology Matching (LOM) is used for
the matching tasks with large size ontologies. (3) Instance Ontology Matching
(IOM) is used for instance matching tasks. (4) Ontology mapping debugging is
used to verify and improve the alignment results. (5) Ontology matching tuning
is used to enhance overall performance.
The matching process mainly contains three steps: (1) Pre-processing, when
Lily parses ontologies and prepares the necessary information for subsequent
steps. Meanwhile, the ontologies will be generally analyzed, whose characteris-
tics, along with studied datasets, will be utilized to determine parameters and
strategies. (2) Similarity computing, when Lily uses special methods to calculate
?
This work is supported by National Key R&D Program of China (2018YFD1100302).
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 Jiangheng Wu, Zhe Pan, Ce Zhang, Peng Wang
the similarities between elements from different ontologies. (3) Post-processing,
when alignments are extracted and refined by mapping debugging.
In this year, some algorithms and matching strategies of Lily have been
modified for higher efficiency, and adjusted for brand-new matching tasks like
Author Recognition and Author Disambiguation in the Instance Matching track.
1.2 Specific techniques used
Lily aims to provide high quality 1:1 concept pair or property pair alignments.
The main specific techniques used by Lily are as follows.
Semantic subgraph An element may have heterogeneous semantic interpre-
tations in different ontologies. Therefore, understanding the real local meanings
of elements is very useful for similarity computation, which are the foundations
for many applications including ontology matching. Therefore, before similarity
computation, Lily first describes the meaning for each entity accurately. However,
since different ontologies have different preferences to describe their elements, ob-
taining the semantic context of an element is an open problem. The semantic
subgraph was proposed to capture the real meanings of ontology elements [11].
To extract the semantic subgraphs, a hybrid ontology graph is used to repre-
sent the semantic relations between elements. An extracting algorithm based on
an electrical circuit model is then used with new conductivity calculation rules
to improve the quality of the semantic subgraphs. It has been shown that the
semantic subgraphs can properly capture the local meanings of elements [11].
Based on the extracted semantic subgraphs, more credible matching clues can
be discovered, which help reduce the negative effects of the matching uncertainty.
Generic ontology matching method The similarity computation is based
on the semantic subgraphs, which means all the information used in the simi-
larity computation comes from the semantic subgraphs. Lily combines the text
matching and structure matching techniques.
Semantic Description Document (SDD) matcher measures the literal similar-
ity between ontologies. A semantic description document of a concept contains
the information about class hierarchies, related properties and instances. A se-
mantic description document of a property contains the information about hier-
archies, domains, ranges, restrictions and related instances. For the descriptions
from different entities, the similarities of the corresponding parts will be calcu-
lated. Finally, all separated similarities will be combined with the experiential
weights.
Matching weak informative ontologies Most existing ontology matching
methods are based on the linguistic information. However, some ontologies may
lack in regular linguistic information such as natural words and comments. Con-
sequently the linguistic-based methods will not work. Structure-based methods
Lily Results for OAEI 2019 3
are more practical for such situations. Similarity propagation is a feasible idea
to realize the structure-based matching. But traditional propagation strategies
do not take into consideration the ontology features and will be faced with ef-
fectiveness and performance problems. Having analyzed the classical similarity
propagation algorithm, Similarity Flood, we proposed a new structure-based on-
tology matching method [1]. This method has two features: (1) It has more strict
but reasonable propagation conditions which lead to more efficient matching pro-
cesses and better alignments. (2) A series of propagation strategies are used to
improve the matching quality. We have demonstrated that this method performs
well on the OAEI benchmark dataset [1].
However, the similarity propagation is not always perfect. When more align-
ments are discovered, more incorrect alignments would also be introduced by
the similarity propagation. So Lily also uses a strategy to determine when to use
the similarity propagation.
Large scale ontology matching Matching large ontologies is a challenge due
to its significant time complexity. We proposed a new matching method for large
ontologies based on reduction anchors [12]. This method has a distinct advantage
over the divide-and-conquer methods because it does not need to partition large
ontologies. In particular, two kinds of reduction anchors, positive and negative
reduction anchors, are proposed to reduce the time complexity in matching.
Positive reduction anchors use the concept hierarchy to predict the ignorable
similarity calculations. Negative reduction anchors use the locality of matching
to predict the ignorable similarity calculations. Our experimental results on the
real world datasets show that the proposed methods are efficient in matching
large ontologies [12].
Ontology mapping debugging Lily utilizes a technique named ontology map-
ping debugging to improve the alignment results [2]. Different from existing meth-
ods that focus on finding efficient and effective solutions for the ontology mapping
problems, mapping debugging emphasizes on analyzing the mapping results to
detect or diagnose the mapping defects. During debugging, some types of map-
ping errors, such as redundant and inconsistent mappings, can be detected. Some
warnings, including imprecise mappings or abnormal mappings, are also locked
by analyzing the features of mapping result. More importantly, some errors and
warnings can be repaired automatically or can be presented to users with revising
suggestions.
Ontology matching tuning Lily adopted ontology matching tuning this year.
By performing parameter optimization on training datasets [3], Lily is able to
determine the best parameters for similar tasks. Those data will be stored. When
it comes to real matching tasks, Lily will perform statistical calculations on the
new ontologies to acquire their features that help it find the most suitable con-
figurations, based on previous training data. In this way, the overall performance
can be improved.
4 Jiangheng Wu, Zhe Pan, Ce Zhang, Peng Wang
Currently, ontology matching tuning is not totally automatic. It is difficult
to find out typical statistical parameters that distinguish each task from oth-
ers. Meanwhile, learning from test datasets can be really time-consuming. Our
experiment is just a beginning.
1.3 Adaptations made for the evaluation
For anatomy and conference tasks, Lily is totally automatic, which means Lily
can be invoked directly from the SEALS client. It will also determine which strat-
egy to use and the corresponding parameters. For a specific instance matching
task, Lily needs to be configured and started up manually, so only matching
results were submitted.
2 Results
2.1 Anatomy track
The anatomy matching task consists of two real large-scale biological ontologies.
Table 1 shows the performance of Lily in the Anatomy track on a server with
one 3.46 GHz, 6-core CPU and 8GB RAM allocated. The time unit is second
(s).
Table 1. The performance in the Anatomy track
Matcher Runtime Precision Recall Recall+ F-Measure
Lily 281 0.873 0.796 0.52 0.833
Compared with the result in OAEI 2018 [4], there is a sight improvement in
Precision, Recall and F-Measure. However, as can be seen in the overall result,
Lily lies in the middle position of the rank, which indicates it is still possible
to make further progress. External knowledge will be leveraged in the future for
the better results. Additionally, to futher reduce the time consumption, some
key algorithms will be parallelized.
2.2 Conference track
In this track, there are 7 independent ontologies that can be matched with one
another. The 21 subtasks are based on given reference alignments. As a result of
heterogeneous characters, it is a challenge to generate high-quality alignments
for all ontology pairs in this track.
Lily adopted ontology matching tuning for the Conference track this year.
Table 2 shows its latest performance.
Lily Results for OAEI 2019 5
Table 2. The performance in the Conference track
Test Case ID Precision Recall F.5-Measure F1-measure F2-measure
ra1-M1 0.59 0.6 0.61 0.62 0.63
ra1-M3 0.59 0.58 0.56 0.54 0.53
ra2-M1 0.58 0.58 0.57 0.56 0.56
ra2-M3 0.58 0.56 0.53 0.50 0.48
rar2-M1 0.60 0.59 0.57 0.55 0.44
rar2-M3 0.54 0.53 0.52 0.51 0.50
Average 0.58 0.57 0.56 0.55 0.52
Compared with the result in OAEI 2018 [4], there is no obvious progress
in mean Precision, Recall and F-Measure. All the tasks share the same config-
urations, so it is possible to generate better alignments by assigning the most
suitable parameters for each task. The performance of Lily was even worse than
StringEquiv in some tasks. ‘We will further analyze this task and our system to
find out the reason later.
2.3 Spimbench track
This tack is an instance-mactching tack which aims to match instances of cre-
ative works between two boxes. And ontology instances are described through
22 classes, 31 DatatypeProperty and 85 ObjectProperty properties.
There are about 380 instances and 10000 triples in sandbox, and about 1800
CWs and 50000 triples in mainbox.
Table 3. Performance in the spimbench task
Track Matcher Precision Recall F-Measure Time
AML 0.8349 0.8963 0.8645 6223
FTRL-IM 0.8543 1.000 0.9214 1474
SANDBOX
LogMap 0.9383 0.7625 0.8413 6919
Lily 0.8494 1.000 0.9186 2032
AML 0.8386 0.8835 0.8605 39515
FTRL-IM 0.8558 0.9980 0.9215 2155
MAINBOX
LogMap 0.8926 0.7095 0.7906 26920
Lily 0.8546 1.000 0.9216 3667
Lily utilized almost the same startegy to handle these two different size tasks.
We found that creative works in this task was rich in text information such as
titles, descriptions and so on. However, garbled texts and messy codes were mixed
up with normal texts. And Lily relied too much on text similarity calculation
and set a low threshold in this task, which accounted for the low precision.
6 Jiangheng Wu, Zhe Pan, Ce Zhang, Peng Wang
As is shown in Table 3, Lily outperforms the others in mainbox. And we
suppose that Lily and FTRL-IM share similar strategies in this track as their
results are close. Meanwhile, experiments shows that simple ensemble methods
and a low threshold contribute to increase of matching efficiency. Nevertheless,
compared with FTRL-IM, there is still potential for Lily to speed up in process
of matching.
3 General comments
In this year, a lot of modifications were done to Lily for both effectiveness and ef-
ficiency. The performance has been improved as we have expected. The strategies
for new tasks have been proved to be useful.
On the whole, Lily is a comprehensive ontology matching system with the
ability to handle multiple types of ontology matching tasks, of which the results
are generally competitive. However, Lily still lacks in strategies for some newly
developed matching tasks. The relatively high time and memory consumption
also prevent Lily from finishing some challenging tasks.
4 Conclusion
In this paper, we briefly introduced our ontology matching system Lily. The
matching process and the special techniques used by Lily were presented, and
the alignment results were carefully analyzed.
There is still so much to do to make further progress. Lily needs more opti-
mization to handle large ontologies with limited time and memory. Thus, tech-
niques like parallelization will be applied more. Also, we have just tried out
ontology matching tuning. With further research on that, Lily will not only
produce better alignments for tracks it was intended for, but also be able to
participate in the interactive track.
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