=Paper=
{{Paper
|id=Vol-2180/paper-77
|storemode=property
|title=An Embedding-based System to Constructing OWL ontologies
|pdfUrl=https://ceur-ws.org/Vol-2180/paper-77.pdf
|volume=Vol-2180
|authors=Lijing Zhang,Xiaowang Zhang,Leyuan Zhao,Jiachen Tian,Shizhan Chen,Hong Wu,Kewen Wang,Zhiyong Feng
|dblpUrl=https://dblp.org/rec/conf/semweb/ZhangZZTCWWF18
}}
==An Embedding-based System to Constructing OWL ontologies==
https://ceur-ws.org/Vol-2180/paper-77.pdf
An Embedding-based Approach to Constructing OWL
ontologies
Lijing Zhang1,3 , Xiaowang Zhang1,3,∗ , Leyuan Zhao1,3 , Jiachen Tian1,3 , Shizhan
Chen1,3 , Hong Wu2,3 , Kewen Wang2,3 , and Zhiyong Feng2,3
1
School of Computer Science and Technology, Tianjin University, Tianjin, China
2
School of Computer Software, Tianjin University, Tianjin, China
3
Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China
∗
Corresponding author.
Abstract. This paper presents a novel system OWLearner for automatically ex-
tracting axioms for OWL ontologies from RDF data using embedding models. In
this system, ontology construction is transformed to the classification problem in
machine learning and thus off-the-shelf tools can be employed to learn axioms in
OWL. There are mainly three modules, namely, embedding, sampling, and train-
ing & learning. Large ontologies DBpedia and YAGO are used to validate the
proposed approach. The experimental results show that OWLearner is able to
learn high-quality expressive OWL axioms automatically and efficiently.
1 Introduction
An ontology is a formal representation of objects and their relationships in a domain
of interest. OWL, with its latest version OWL 2, is the W3C standard for ontology
languages. Formally, an ontology is expressed as a pair of an RDF dataset and a TBox.
Automatic construction of ontologies is an important but challenging task in ontol-
ogy engineering. Specifically, given an RDF data, the task of constructing ontologies
we are interested in is to extract DL axioms. DL-Learner [1] is a leading system for
enriching DL ontologies, which is based on techniques, such as refinement operator, in
inductive logic programming (ILP) [2]. However, ILP-based systems are usually unable
to handle very large ontologies.
We tackle this challenge by providing a scalable method for learning DL axioms
using machine learning techniques. Based on the embedding methods in representation
learning, an RDF dataset is embedded into a continuous vector space and the inherent
structure of the original data is preserved [3]. Thus, the ontology construction can be
accomplished in the vector space via supervised machine learning, which in essence is
to learn a function for each axiom pattern to predict the correctness of input axioms.
To achieve this goal, labeled samples are obtained through SPARQL queries, and are
transformed into vector space using embedding and feature engineering techniques.
In this paper, we have implemented a system prototype OWLearner and com-
pared it with the state of art system DL-Learner on major benchmarks such as DB-
pedia and YAGO for the DL axiom constructing task. Our experimental results show
that OWLearner outperformed DL-Learner in both time efficiency and the quality of
axioms.
2 An Overview of OWLearner
In this section, we introduce OWLearner, a prototype system for learning axioms in
description logics (DL). Specifically, we consider the 12 axiom patterns in SROIQ,
which are popular [4], listed as follows: P1 : C v D; P2 : C ≡ D; P3 : s v
r; P4 : s ≡ r; P5 :≥ 1 r v C; P6 : > v ∀r.C; P7 : r1 ◦ r2 v s; P8 : ∃r.C v
D; P9 : C ≡ D u E; P10 : C ≡ D u ∃r.E; P11 : C v ∃r.(∃s.D); P12 : s ≡ r− .
Fig. 1. Framework and workflow of OWLearner
The framework and workflow of OWLearner are shown in the left and right sub-
figure of Figure 1, respectively. The OWLearner contains five components as follows:
Data preprocessing This component specifies how to retrieve data and how to convert
various formats of RDF data to a unified format (e.g., N-Triples) so that most RDF
serialization formats are supported conveniently.
Embedding This component embeds entities and relations into continuous vector spaces
as their features by employing effective embedding models, such as TransE [5]. The
selection mechanism of embedding models depends on the scores of benchmark
datasets. Then, embeddings of axioms are constructed by applying some feature
engineering methods based on the original embeddings.
Sampling This component generates labeled samples for training, which fall into three
parts: positive samples, negative samples, and unknown samples to be further judged.
This procedure of generating samples consists of three steps: SPARQL querying,
statistical analysis, and OWL reasoning (rule-based and ontology reasoning).
Learning This component trains supervised learning models via positive/negative sam-
ples and then predicts axioms in unknown samples by applying the trained models.
OWLearner supports most of the supervised learning models, and provides many
principal metrics such as accuracy (ACC) and Area Under ROC curve (AUC) to
evaluate these models. Moreover, we define some metrics to evaluate the learned
axioms, including Standard Confidence(SC), Head Coverage(HC) and Partial Com-
pleteness Assumption(PCA) based Confidence(PCAconf ).
Building This part constructs all axioms generated/predicted in our model as an OWL
ontology. OWLearner provides a plugin API which supports most off-the-shelf
ontology editors.
3 Experiments and Evaluation
In this section, we evaluate OWLeaner on three data sets, namely, DBpedia, YAGO1k
(a fragment of YAGO containing all classes with over 1000 entities), and Chinese
Symptom Database (SIC) shown in Table 1. We conducted four sets of experiments
and explain them in detail as follows.
Table 1. Specification of datasets Table 3. Performance of classifiers for
Data DBpedia YAGO1k SIC learning 12 axioms
Entity 6099488 4295827 73812 Axiom rate unknown learned ACC AUC Classifier
Relation 659 38 19 P1 0.99 3049 199 0.82 0.83 GBDT
Fact 18154761 12430700 617486 P2 0.99 5972 817 0.75 0.80 GBDT
Class 14989 4987 16 P3 0.9 5630 827 0.83 0.63 SVM
P4 0.7 5658 524 0.86 0.88 SVM
Table 2. Precision of P1 to P6 P5 0.9 14434 5138 0.82 0.90 GBDT
P6 0.7 25346 4349 0.82 0.88 GBDT
Axiom P1 P2 P 3 P4 P5 P6
P7 0.9 45941 4294 0.81 0.77 GBDT
Precision 0.88 0.73 0.33 0.24 0.78 0.71 P8 0.9 34081 10565 0.84 0.91 GBDT
P9 0.99 59866 7576 0.88 0.92 DT
P10 0.99 1036709 6817 0.95 0.92 DT
P11 0.99 1032741 19237 0.95 0.96 KNN
P12 0.99 3097 79 0.94 0.99 SVM
Set 1. Suitability of Classifiers based on ACC and AUC In this set of experiments,
for each of those 12 axiom patterns, we tested which machine learning model is most
effective based on two metrics. The results are shown in Table 3, which show that no
single learning model is most effective for all axiom patterns. This experiment provides
a guideline for selecting a suitable learning model for a given axiom pattern.
Set 2. Accuracy of learned axioms We used three metrics, namely, Standard Con-
fidence(SC), Head Coverage(HC) and PCA-based Confidence(PCA-conf), to test the
quality (accuracy) of learned axioms by OWLearner. The results are shown in Table 4,
which indicates that OWLearner can learn axioms with high quality.
Set 3. Precision of learned axioms We used DBpedia as the benchmark to evaluate
the precision of OWLearner. As only axioms of patterns P1 , . . . , P6 allows in DBpe-
dia, the precisions for such axioms are obtained. The precision value for each axiom
pattern represents the proportion of the axioms that can be matched. The results are
shown in Table 2. The results show that relatively high precisions are obtained for ax-
iom patterns P1 , P2 , P5 and P6 , but the precisions of P3 and P4 are low. A major reason
for this is that there is little data available for these two axiom patterns.
Set 4. Comparison of OWLearner with DL-Learner In this set of experiments, we
compared the performance of OWLearner with DL-Learner based on four metrics Run-
time, Standard Confidence(SC), Head Coverage(HC) and PCA-based Confidence(PCA-
conf). The results, shown in Table 5, indicate that the quality of learned axioms by
OWLearner are comparable to that by DL-Learner, although OWLearner is superior
to DL-Learner in terms of HC degree. The major advantage of OWLearner is time
efficiency. OWLearner does not need to specify a class name but DL-Learner requires
to specify a target class before axioms can be learned.
Table 4. Accuracy of OWLearner in learning 12 axioms
Axiom P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
SC 0.97 0.86 0.61 0.70 0.70 0.63 0.84 0.88 0.88 0.72 0.63 0.38
HC 0.33 0.77 0.4 0.72 0.47 0.32 0.42 0.31 0.87 0.67 0.5 0.25
PCAconf 0.98 0.88 0.78 0.87 0.89 0.81 0.90 0.9 0.88 0.86 0.83 0.58
4 Conclusions
We have proposed a novel method of learning axioms for OWL/DL axioms. The method
is based on the technique of embedding in representation learning. Based on the pro-
posed method, we have implemented a system OWLearner for automatic axiom ex-
traction in OWL ontologies. Our experiments show that OWLearner is much more
efficient than DL-Learner, state of the art system for ontology axiom learning, and the
quality of learned axioms for these two methods is comparable.
Table 5. Comparision between OWLearner and DL-Learner
OWLearner DL-Learner
Class
Runtime SC HC PCAconf Runtime SC HC PCAconf
Library 1.0 0.89 1.0 8min 1.0 0.51 1.0
Guitarist 1.0 0.87 1.0 9min 1.0 0.66 1.0
15min
Album 1.0 0.8 1.0 20min 1.0 0.43 1.0
(total)
SoccerP layer 1.0 0.78 1.0 10min 1.0 0.64 1.0
RadioStation 1.0 0.89 1.0 9min 1.0 0.73 1.0
Acknowledgments
This work is supported by the National Natural Science Foundation of China (61502336),
the National Key R&D Program of China (2016YFB1000603,2017YFC0908401), and
the Seed Foundation of Tianjin University (2018XZC-0016).
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