1. Introduction
The evolution of semantic web technologies and the growth of big data volumes maintained by various database models have resulted in many disparate and independent data sources [
1]. However, data growth will pose many issues if we cannot keep pace with these improvements. To succeed, it is crucial to determine how traditional information systems can be transferred into more integrated systems. In this context, ontologies play an essential role in addressing semantic heterogeneity to achieve semantic interoperability among the various web applications and services [
2]. Semantic web languages have a sharp learning curve, and a shift in viewpoint is necessary, particularly in individuals with qualifications in software engineering, object focused programming, or relational databases.
During the early 1990s, researchers in the field of computer science began investigating ontologies. The claim was that ontologies could facilitate information sharing by users and software agents regarding particular topics. The given definition of ontology was a conceptual representation of an entity, its characteristics and correlations within a domain [
3]. Over the past 10 years, ontologies have gained increasing attention in many different fields, including academia, industry, biomedicine, finance, engineering, law, and governmental agencies [
4]. Furthermore, ontologies have gained significant importance as a component of biomedical research investigations because they supply the formalism, objectivity, and common terminology required to report research findings that can enable direct exchange and reuse by scientists and computers [
5]. However, integrating and sharing data are still challenging because ontologies are semantically heterogeneous.
Ontology matching has grown in popularity, particularly in the biomedical, biological, and geographical domains [
6,
7]. From an abstract perspective, ontology matching aims to identify how ontologies relate to one another. The matching process can be completed by detecting any two given entities’ interrelated or comparable elements. Precisely, the two entities must be tallied to yield the appropriate set of correspondences [
3]. It is challenging to match biomedical ontologies because of their huge size, vocabulary complexity, and rising semantic richness, including new forms of interactions between classes making the task computationally challenging [
6]. Several studies have presented alternative approaches to address the ontology matching problem. They differ principally in terms of the type of information that each ontology encodes and how that knowledge is applied in the context of detecting equivalences across features or structures in ontologies [
8,
9,
10,
11,
12]. Furthermore, additional factors, such as matching settings (e.g., weights and cut thresholds) and external BK resources, influence the matching process. However, BK sources must include lexical or structural knowledge that the source and target ontologies do not have, to recognize novel mappings.
2. Preliminaries
The following fundamental terms are used throughout the study:
Ontology: Ontologies are the tools that allow us to formally describe a domain by its objects and the relationships that exist between them. Ontology is defined in this study as a collection of classes, properties, and instances for a specific topic of interest. The set of classes, properties, and instances that make up the given ontology is often referred to as the entity of the ontology.
Matcher: a matcher is a system used to find mappings between ontologies, such as AML [
6], LogMap, and LogMapLt [
24].
Ontology matching system: A standard ontology matching system inputs two ontologies representing the source and the target and attempts to identify similar entities [
3].
Correspondence: Correspondence is defined as the mapping of an entity between the source and the target ontologies. This task may include additional information regarding the mapping (e.g., relation, score, and matcher).
<e, e′, r, s, m>: Represents a basic correspondence. In this context, e represents an entity from the source ontology, and e′ is an entity from the target ontology. r represents the equivalence between the entities. s represents the degree of confidence reflecting the reliability of a correspondence in the range [0, 1], and m denotes a matcher given by a series of single- or multimatcher.
Alignment: The series of correspondences among the pairs of entities represents the alignment for the specific source and target ontologies. According to this definition, the alignment constitutes the standard results of an ontology alignment system.
Aggregation strategy. A satisfactory output alignment is not always achieved with just one ontology entity matcher. Accordingly, multiple matchers are frequently integrated to generate a singular confidence value representing an aggregated value. The quality of the alignments is highly dependent on the suitable aggregation approach. However, determining an effective combination strategy is a complicated task. A complex procedure is manually carried out by an expert or a generic method (e.g., maximum, minimum, average, and vote) [
25].
Biomedical ontology matching: This is concerned with determining an ontology alignment made up of biomedical concept correspondences. In most cases, the matching procedure requires the use of external BK sources.
BK: BK has different definitions in various techniques. BK is defined as the essential information needed to comprehend a scenario or problem in ontology matching. We identify it as a collection of external ontologies that give lexical or semantic information on the domain of the ontologies to align.
Once the final alignments are established, multiple performance scores are generally determined to measure system performance. In this work, a reference alignment encompassing the ground truth of the mappings between specific ontologies is needed. Two measures, typically referred to as recall and precision, are employed to evaluate the alignment. Recall, known as completeness, assesses the proportion of accurate alignments identified to the overall number of available accurate alignments. Meanwhile, precision is known as correctness and assesses the proportion of identified alignments that are indeed accurate. For example, reference alignment, R, and particular alignment, A, are defined as follows:
In most cases, recall and precision are needed for alignment performance comparison. Furthermore, the F-measure can be employed for a trade off between the two measures and is given by:
The collaborative international initiative (OAEI) is designed to assess the increasing number of ontology matching systems. This initiative is primarily geared toward an open and equal comparison of systems and algorithms to ensure that the ideal matching techniques can be determined by everyone [
26]. Furthermore, the initiative includes a range of tracks (e.g., anatomy, conference, and large biomedical ontologies), and the outcomes of the evaluated systems are disclosed for further analysis.
Author Contributions
Conceptualization, S.A.-Y., W.-W.G., E.-X.T., N.Z.J. and P.B.; Methodology, S.A.-Y., W.-W.G., E.-X.T., N.Z.J. and P.B; software, S.A.-Y.; formal analysis, S.A.-Y.; writing—original draft preparation, S.A.-Y.; writing—review and editing, S.A.-Y., W.-W.G., E.-X.T., N.Z.J. and P.B.; supervision, W.-W.G., E.-X.T., N.Z.J. and P.B. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data used to support this study’s findings are available from OAEI and are available online which can be accessed on
http://oaei.ontologymatching.org/2020/ (accessed on 21 November 2021).
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
AMA | Adult Mouse Anatomy |
AML | AgreementMakerLight |
BK | Background Knowledge |
COMA | Combination of Schema Matching Approaches |
DOID | Human Disease Ontology |
FMA | Foundational Model of Anatomy |
GBKOM | A Generic framework for BK Based Ontology Matching |
GOMMA | Generic Ontology Matching and Mapping Management |
LogMap | Logic Based and Scalable Ontology Matching |
LogMapBio | LogMap BioPortal |
LogMapLt | LogMap Lightweight |
MA | Mouse Anatomy |
NCI | National Cancer Institute Thesaurus |
NCBO | The National Center for Biomedical Ontology |
OAEI | Ontology Alignment Evaluation Initiative |
SNOMED CT | SNOMED Clinical Terms |
UBERON | The Uber Anatomy Ontology |
UMLS | The Unified Medical Language System |
YAM++ | Yet Another Matcher for Ontology Matching |
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Figure 1.
Matching utilizing a BK Source.
Figure 2.
BK based matching overview.
Figure 3.
BK ontology matching: a multimatcher model.
Figure 4.
Example of paths that include scores only.
Figure 5.
Example of paths that include scores and matchers.
Figure 6.
Applying several matchers and different aggregation strategies on the Anatomy track.
Figure 7.
Applying several matchers and different aggregation strategies as: (a) Task 1—FMA-NCI (b) Task 2—Whole FMA and NCI (c) Task 3—FMA-SNOMED (d) Task 4—Whole FMA-SNOMED (e) Task 5—SNOMED-NCI (f) Task 6—Whole SNOMED-NCI.
Table 1.
Part of the alignment between MA and Uberon ontologies using the LogMap matcher.
Entity 1 | Entity 2 | Score |
---|
MA_0002215 | UBERON_0007318 | 0.80 |
MA_0002110 | UBERON_0008783 | 0.79 |
MA_0000462 | UBERON_0001528 | 0.89 |
MA_0002358 | UBERON_0001298 | 0.83 |
MA_0002107 | UBERON_0006656 | 0.62 |
MA_0000004 | UBERON_0000468 | 0.50 |
Table 2.
Part of the alignment between MA and Uberon ontologies using the LogMapLt matcher.
Entity 1 | Entity 2 | Score |
---|
MA_0002215 | UBERON_0007318 | 1.0 |
MA_0002110 | UBERON_0008783 | 1.0 |
MA_0000462 | UBERON_0001528 | 1.0 |
MA_0000599 | UBERON_0004268 | 1.0 |
MA_0000744 | UBERON_0009039 | 1.0 |
Table 3.
Part of the alignment between MA and Uberon ontologies using the AML matcher.
Entity 1 | Entity 2 | Score |
---|
MA_0002215 | UBERON_0007318 | 0.99 |
MA_0002110 | UBERON_0008783 | 0.99 |
MA_0000462 | UBERON_0001528 | 0.88 |
MA_0002358 | UBERON_0001298 | 0.99 |
MA_0002107 | UBERON_0006656 | 0.62 |
MA_0000599 | UBERON_0004268 | 0.99 |
MA_0000001 | UBERON_0001062 | 0.99 |
Table 4.
Part of the final alignment between MA and Uberon ontologies using the minimum aggregation strategy.
Entity 1 | Entity 2 | Score | Matcher |
---|
MA_0002215 | UBERON_0007318 | 0.80 | LogMap, LogMapLt, AML |
MA_0002110 | UBERON_0008783 | 0.79 | LogMap, LogMapLt, AML |
MA_0000462 | UBERON_0001528 | 0.88 | LogMap, LogMapLt, AML |
MA_0002358 | UBERON_0001298 | 0.83 | LogMap, AML |
MA_0002107 | UBERON_0006656 | 0.62 | LogMap, AML |
MA_0000599 | UBERON_0004268 | 0.99 | LogMapLt, AML |
MA_0000004 | UBERON_0000468 | 0.50 | LogMap |
MA_0000744 | UBERON_0009039 | 1.0 | LogMapLt |
MA_0000001 | UBERON_0001062 | 0.99 | AML |
Table 5.
Part of the final alignment between MA and Uberon ontologies using the maximum aggregation strategy.
Entity 1 | Entity 2 | Score | Matcher |
---|
MA_0002215 | UBERON_0007318 | 1.0 | LogMap, LogMapLt, AML |
MA_0002110 | UBERON_0008783 | 1.0 | LogMap, LogMapLt, AML |
MA_0000462 | UBERON_0001528 | 1.0 | LogMap, LogMapLt, AML |
MA_0002358 | UBERON_0001298 | 0.99 | LogMap, AML |
MA_0002107 | UBERON_0006656 | 0.62 | LogMap, AML |
MA_0000599 | UBERON_0004268 | 1.0 | LogMapLt, AML |
MA_0000004 | UBERON_0000468 | 0.50 | LogMap |
MA_0000744 | UBERON_0009039 | 1.0 | LogMapLt |
MA_0000001 | UBERON_0001062 | 0.99 | AML |
Table 6.
Part of the final alignment between MA and Uberon ontologies using the average aggregation strategy.
Entity 1 | Entity 2 | Score | Matcher |
---|
MA_0002215 | UBERON_0007318 | 0.93 | LogMap, LogMapLt, AML |
MA_0002110 | UBERON_0008783 | 0.93 | LogMap, LogMapLt, AML |
MA_0000462 | UBERON_0001528 | 0.92 | LogMap, LogMapLt, AML |
MA_0002358 | UBERON_0001298 | 0.91 | LogMap, AML |
MA_0002107 | UBERON_0006656 | 0.62 | LogMap, AML |
MA_0000599 | UBERON_0004268 | 0.99 | LogMapLt, AML |
MA_0000004 | UBERON_0000468 | 0.50 | LogMap |
MA_0000744 | UBERON_0009039 | 1.0 | LogMapLt |
MA_0000001 | UBERON_0001062 | 0.99 | AML |
Table 7.
Part of the final alignment between MA and Uberon ontologies using the vote aggregation strategy.
Entity 1 | Entity 2 | Score | Matcher |
---|
MA_0002215 | UBERON_0007318 | 1.0 | LogMap, LogMapLt, AML |
MA_0002110 | UBERON_0008783 | 1.0 | LogMap, LogMapLt, AML |
MA_0000462 | UBERON_0001528 | 1.0 | LogMap, LogMapLt, AML |
MA_0002358 | UBERON_0001298 | 0.99 | LogMap, AML |
MA_0002107 | UBERON_0006656 | 0.62 | LogMap, AML |
MA_0000599 | UBERON_0004268 | 1.0 | LogMapLt, AML |
Table 8.
List of the model parameters.
Parameter | Value |
---|
Matcher | Single | Yes/No |
| Multiple | Yes/No |
Matchers | LogMap | Yes/No |
| LogMapLt | Yes/No |
| AML | Yes/No |
| YAM ++ | Yes/No |
Aggregation methods | Minimum | Yes/No |
| Maximum | Yes/No |
| Average | Yes/No |
| VOTE | Yes/No |
BK | DOID and UBERON ontologies | Yes |
| Existing Mapping | No |
| Alignment repository | No |
Mapping selection | ML based | No |
| Rule based | Yes |
Maximum path length | 4 |
Internal exploration | Yes/No |
Threshold | 0.0 |
Semantic verification | Yes/No |
Table 9.
Comparison of the correct paths produced by different matchers with the reference alignment.
Track | All Paths | One Matcher | Two Matchers | Three Matchers |
---|
Anatomy | Min | 0.777 | 0.519 | 0.652 | 0.903 |
Max | 0.777 | 0.518 | 0.651 | 0.904 |
Avg | 0.778 | 0.518 | 0.650 | 0.904 |
Vote | 0.933 | - | 0.148 | 0.960 |
Task 1— FMA-NCI | Min | 0.839 | 0.624 | 0.664 | 0.940 |
Max | 0.841 | 0.622 | 0.658 | 0.940 |
Avg | 0.841 | 0.619 | 0.658 | 0.941 |
Vote | 0.959 | 0.50 | 0.861 | 0.976 |
Task 2—Whole FMA and NCI | Min | 0.487 | 0.241 | 0.322 | 0.646 |
Max | 0.485 | 0.241 | 0.321 | 0.638 |
Avg | 0.484 | 0.239 | 0.322 | 0.639 |
Vote | 0.725 | 1 | 0.578 | 0.739 |
Task 3— FMA-SNOMED | Min | 0.839 | 0.738 | 0.851 | 0.904 |
Max | 0.842 | 0.737 | 0.852 | 0.902 |
Avg | 0.842 | 0.738 | 0.852 | 0.902 |
Vote | 0.964 | 1 | 0.959 | 0.970 |
Task 4—Whole FMA-SNOMED | Min | 0.680 | 0.457 | 0.777 | 0.859 |
Max | 0.681 | 0.458 | 0.775 | 0.851 |
Avg | 0.681 | 0.457 | 0.774 | 0.853 |
Vote | 0.935 | 0.785 | 0.928 | 0.952 |
Task 5— SNOMED-NCI | Min | 0.787 | 0.599 | 0.677 | 0.941 |
Max | 0.786 | 0.600 | 0.675 | 0.941 |
Avg | 0.786 | 0.599 | 0.675 | 0.942 |
Vote | 0.946 | 0.833 | 0.876 | 0.965 |
Task 6—Whole SNOMED-NCI | Min | 0.589 | 0.463 | 0.374 | 0.824 |
Max | 0.590 | 0.462 | 0.375 | 0.824 |
Avg | 0.590 | 0.462 | 0.376 | 0.824 |
Vote | 0.843 | 0. | 0.690 | 0.873 |
Table 10.
Compare our model with GBKOM and different direct matchers using the precision measure.
Track | GBKOM (LogMap) | AML | LogMapLt | LogMap | Our Model |
---|
Min | Avg | Max | Vote |
---|
Anatomy | 0.900 | 0.950 | 0.962 | 0.918 | 0.903 | 0.903 | 0.903 | 0.987 |
Task 1—FMA-NCI | 0.945 | 0.958 | 0.967 | 0.945 | 0.967 | 0.968 | 0.970 | 0.995 |
Task 2—Whole FMA and NCI | 0.763 | 0.806 | 0.676 | 0.867 | 0.797 | 0.806 | 0.813 | 0.989 |
Task 3—FMA-SNOMED | 0.924 | 0.923 | 0.968 | 0.947 | 0.954 | 0.954 | 0.954 | 0.988 |
Task 4—Whole FMA-SNOMED | 0.798 | 0.685 | 0.851 | 0.811 | 0.885 | 0.888 | 0.890 | 0.998 |
Task 5—SNOMED-NCI | 0.924 | 0.906 | 0.949 | 0.957 | 0.948 | 0.947 | 0.951 | 0.997 |
Task 6—Whole SNOMED-NCI | 0.795 | 0.862 | 0.798 | 0.874 | 0.823 | 0.827 | 0.830 | 0.995 |
Table 11.
Compare our model with GBKOM and different direct matchers using the recall measure.
Track | GBKOM (LogMap) | AML | LogMapLt | LogMap | Our Model |
---|
Min | Avg | Max | Vote |
---|
Anatomy | 0.947 | 0.936 | 0.728 | 0.846 | 0.962 | 0.963 | 0.963 | 0.922 |
Task 1—FMA-NCI | 0.896 | 0.910 | 0.819 | 0.902 | 0.928 | 0.937 | 0.938 | 0.884 |
Task 2—Whole FMA and NCI | 0.851 | 0.881 | 0.819 | 0.805 | 0.895 | 0.915 | 0.922 | 0.834 |
Task 3—FMA-SNOMED | 0.735 | 0.762 | 0.208 | 0.690 | 0.823 | 0.827 | 0.828 | 0.668 |
Task 4—Whole FMA-SNOMED | 0.695 | 0.710 | 0.208 | 0.642 | 0.787 | 0.791 | 0.792 | 0.561 |
Task 5—SNOMED-NCI | 0.705 | 0.746 | 0.566 | 0.666 | 0.779 | 0.783 | 0.786 | 0.653 |
Task 6—Whole SNOMED-NCI | 0.683 | 0.687 | 0.566 | 0.650 | 0.760 | 0.767 | 0.771 | 0.594 |
Table 12.
Compare our model with GBKOM and different direct matchers using the f-measure measure.
Track | GBKOM (LogMap) | AML | LogMapLt | LogMap | Our Model |
---|
Min | Avg | Max | Vote |
---|
Anatomy | 0.923 | 0.943 | 0.828 | 0.880 | 0.931 | 0.932 | 0.932 | 0.954 |
Task 1—FMA-NCI | 0.920 | 0.933 | 0.887 | 0.923 | 0.947 | 0.952 | 0.954 | 0.937 |
Task 2—Whole FMA and NCI | 0.804 | 0.842 | 0.741 | 0.835 | 0.843 | 0.857 | 0.864 | 0.905 |
Task 3—FMA-SNOMED | 0.819 | 0.835 | 0.342 | 0.798 | 0.884 | 0.886 | 0.886 | 0.797 |
Task 4—Whole FMA-SNOMED | 0.743 | 0.697 | 0.334 | 0.717 | 0.833 | 0.836 | 0.838 | 0.718 |
Task 5—SNOMED-NCI | 0.80 | 0.818 | 0.709 | 0.785 | 0.855 | 0.857 | 0.861 | 0.789 |
Task 6—Whole SNOMED-NCI | 0.735 | 0.765 | 0.662 | 0.746 | 0.791 | 0.796 | 0.799 | 0.744 |
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