Ontology Alignment Evaluation Initiative Research Articles

Automatic Knowledge Graph matching via Self-adaptive Designed Genetic Programming

Knowledge Graph (KG) provides a structured representation of domain knowledge by formally defining entities and their relationships. However, distinct communities tend to employ different terminologies and granularity levels to describe the same entity, leading to the KG heterogeneity issue that hampers their communications. KG matching can identify semantically similar entities in two KGs, which is an effective solution to this problem. Similarity Measures (SMs) are the foundation of the KG matching technique, and due to the complexity of entity heterogeneity, it is necessary to construct a high-level SM by selecting and combining the basic SMs. However, the large number of SMs and their intricate relationships make SM construction an open challenge. Inspired by the success of Evolutionary Algorithms (EA) in addressing the entity matching problem, this work further proposes a novel Self-adaptive Designed Genetic Programming (SDGP) to automatically construct the SM for KG matching. To overcome the drawbacks of the classic EA-based matching methods, a new individual representation and a novel fitness function are proposed to enable SDGP automatically explore the SM selection and combination. Then, a new Adaptive Automatic Design (AAD) method is introduced to adaptively trade off SDGP’s exploration and exploitation, which can determine the timing of AAD and efficiently determine the suitable breeding operators and control parameters for SDGP. The experiment uses the Ontology Alignment Evaluation Initiative’s Knowledge Graph (KG) data set to test the performance of SDGP. The experimental results show that SDGP can effectively determine high-quality KG alignments, which significantly outperform state-of-the-art KG matching methods.

Large Scale Ontology Matching System (LSMatch)

Background: Ontology matching provides a solution to the semantic heterogeneity problem by finding semantic relationships between entities of ontologies. Over the last two decades, there has been considerable development and improvement in the ontology matching paradigm. More than 50 ontology matching systems have been developed, and some of them are performing really well. However, the initial rate of improvement was measurably high, which now is slowing down. However, there still is room for improvement, which we as a community can work towards to achieve. Method: In this light, we have developed a Large Scale Ontology Matching System (LSMatch), which uses different matchers to find similarities between concepts of two ontologies. LSMatch mainly uses two modules for matching. These modules perform string similarity and synonyms matching on the concepts of the ontologies. Results: For the evaluation of LSMatch, we have tested it in Ontology Alignment Evaluation Initiative (OAEI) 2021. The performance results show that LSMatch can perform matching operations on large ontologies. LSMatch was evaluated on anatomy, disease and phenotype, conference, Knowledge graph, and Common Knowledge Graphs (KG) track. In all of these tracks, LSMatch’s performance was at par with other systems. Conclusion: Being LSMatch’s first participation, the system showed potential and has room for improvement.

Ontology alignment with semantic and structural embeddings

Ontology alignment is essential for data integration and interoperability across multiple applications across diverse disciplines. In recent decades, significant advancements have been made in the development of advanced methods and systems for ontology alignment. Empirical results have suggested that ontological semantics can be effectively employed to enhance the alignment process. Besides, structural information is crucial for ontology alignment as it reflects the relations among adjacent concepts in the ontology. Previous works are mainly based on external lexicon and predefined rules based on ontological structure. Recently, deep learning has imposed positive impacts on ontology alignment and obtained substantial improvement.This paper proposes a new method based on ontology embedding incorporating the semantic and structural features. It utilizes the distance between the embedding of two ontological concepts to be aligned as the criterion for alignment. The proposed method is used to align two widely used food ontologies and three Chinese food classification ontologies. The experimental results show that our method enhances the performance compared to several state-of-the-art alignment systems, demonstrating the importance of learning semantic representation and structural representation. Furthermore, the proposed method is evaluated on several different tracks of the Ontology Alignment Evaluation Initiative (OAEI), and experimental results show that our method outperforms other baselines in effectiveness. The data and code can be obtained from: https://github.com/haozhigang1111/Ontology-Alignment.git.

AgreementMakerLight

Ontology matching establishes correspondences between entities of related ontologies, with applications ranging from enabling semantic interoperability to supporting ontology and knowledge graph development. Its demand within the Semantic Web community is on the rise, as the popularity of knowledge graph supporting information systems or artificial intelligence applications continues to increase. In this article, we showcase AgreementMakerLight (AML), an ontology matching system in continuous development since 2013, with demonstrated performance over nine editions of the Ontology Alignment Evaluation Initiative (OAEI), and a history of real-world applications across a variety of domains. We overview AML’s architecture and algorithms, its user interfaces and functionalities, its performance, and its impact. AML has participated in more OAEI tracks since 2013 than any other matching system, has a median rank by F-measure between 1 and 2 across all tracks in every year since 2014, and a rank by run time between 3 and 4. Thus, it offers a combination of range, quality and efficiency that few matching systems can rival. Moreover, AML’s impact can be gauged by the 263 (non-self) publications that cite one or more of its papers, among which we count 34 real-world applications.

Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm.

Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor devices is hindered. Sensor ontology matching achieves data integration and sharing between sensors by establishing semantic relationships between sensor devices. Therefore, a niching multi-objective particle swarm optimization algorithm (NMOPSO) is proposed to effectively solve the sensor ontology matching problem. As the sensor ontology meta-matching problem is essentially a multi-modal optimization problem (MMOP), a niching strategy is introduced into MOPSO to enable the algorithm to find more global optimal solutions that meet the needs of different decision makers. In addition, a diversity-enhancing strategy and an opposition-based learning (OBL) strategy are introduced into the evolution process of NMOPSO to improve the quality of sensor ontology matching and ensure the solutions converge to the real Pareto fronts (PFs). The experimental results demonstrate the effectiveness of NMOPSO in comparison to MOPSO-based matching techniques and participants of the Ontology Alignment Evaluation Initiative (OAEI).

Two-stage Detection of Semantic Redundancies in RDF Data

With the enrichment of the RDF (resource description framework), integrating diverse data sources may result in RDF data duplication. Failure to effectively detect the duplicates brings redundancies into the integrated RDF datasets. This not only increases unnecessarily the size of the datasets, but also reduces the dataset quality. Traditionally a similarity calculation is applied to detect if a pair of candidates contains duplicates. For massive RDF data, a simple similarity calculation may lead to extremely low efficiency. To detect duplicates in the massive RDF data, in this paper we propose a detection approach based on RDF data clustering and similarity measurements. We first propose a clustering method based on locality sensitive hashing (LSH), which can efficiently select candidate pairs in RDF data. Then, a similarity calculation is performed on the selected candidate pairs. We finally obtain the duplicate candidates. We show through experiments that our approach can quickly extract the duplicate candidates in RDF datasets. Our approach had the highest F score and time performance in the OAEI (Ontology Alignment Evaluation Initiative) 2019 competition.

Similarity Feature Construction for Matching Ontologies through Adaptively Aggregating Artificial Neural Networks

Ontology is the kernel technique of Semantic Web (SW), which enables the interaction and cooperation among different intelligent applications. However, with the rapid development of ontologies, their heterogeneity issue becomes more and more serious, which hampers communications among those intelligent systems built upon them. Finding the heterogeneous entities between two ontologies, i.e., ontology matching, is an effective method of solving ontology heterogeneity problems. When matching two ontologies, it is critical to construct the entity pair’s similarity feature by comprehensively taking into consideration various similarity features, so that the identical entities can be distinguished. Due to the ability of learning complex calculating model, recently, Artificial Neural Network (ANN) is a popular method of constructing similarity features for matching ontologies. The existing ANNs construct the similarity feature in a single perspective, which could not ensure its effectiveness under diverse heterogeneous contexts. To construct an accurate similarity feature for each entity pair, in this work, we propose an adaptive aggregating method of combining different ANNs. In particular, we first propose a context-based ANN and syntax-based ANN to respectively construct two similarity feature matrices, which are then adaptively integrated to obtain a final similarity feature matrix through the Ordered Weighted Averaging (OWA) and Analytic hierarchy process (AHP). Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track are used to verify the effectiveness of our method. The experimental results show that our approach’s results are better than single ANN-based ontology matching techniques and state-of-the-art ontology matching techniques.

INTEGRATING LARGE-SCALE ONTOLOGIES FOR ECONOMIC AND FINANCIAL SYSTEMS VIA ADAPTIVE CO-EVOLUTIONARY NSGA-II

The identification, prediction, management, and control of economic and financial systems render extremely challenging tasks, which require comprehensively integrating the knowledge of different expert systems. Ontology, as a state-of-the-art knowledge modeling technique, has been extensively applied in the domain of economics and finance. However, due to ontology engineers’ subjectivity, ontology suffers from the heterogeneity issue, which hampers the co-operation among the intelligent expert system based on them. To address this issue, ontology matching for finding heterogeneous concept pairs between two ontologies has been rapidly developed. It is difficult to find the perfect ontology alignment that satisfies the needs of all decision-makers. Therefore, Multi-Objective Evolutionary Algorithm, such as Non-dominated Sorting Genetic Algorithm (NSGA-II), attracts many researchers’ attention. However, when facing large-scale ontology matching problems, NSGA-II tends to fall into local optimal solutions due to the large search space. To effectively address this drawback, we model the large-scale ontology problem as a nonlinear optimization problem, and propose an Adaptive Co-Evolutionary NSGA-II (ACE-NSGA-II) to deal with it. Compared with NSGA-II, ACE-NSGA-II introduces a co-evolutionary mechanism to increase the diversity of populations in order to decrease the probability of premature convergence. In particular, ACE-NSGA-II uses an adaptive population maintenance strategy to assign more resources toward the dominant ones in order to improve the solution efficiency for solving large-scale ontology matching. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track to test the effectiveness of ACE-NSGA-II, and the resulting experiment demonstrated that compared to NSGA-II and OAEI’s participants, ACE-NSGA-II is able to find better alignment.

Interactive complex ontology matching with local and global similarity deviations

<abstract><p>Ontology serves as a central technique in the semantic web to elucidate domain knowledge. The challenge of dealing with the heterogeneity introduced by diverse domain ontologies necessitates ontology matching, a process designed to identify semantically interconnected entities within these ontologies. This task is inherently complex due to the broad, diverse entities and the rich semantics inherent in vocabularies. To tackle this challenge, we bring forth a new interactive ontology matching method with local and global similarity deviations (IOM-LGSD) for ontology matching, which consists of three novel components. First, a local and global similarity deviation (LGSD) metrics are presented to measure the consistency of similarity measures (SMs) and single out the less consistent SMs for user validation. Second, we present a genetic algorithm (GA) based SM selector to evolve the SM subsets. Lastly, a problem-specific induced ordered weighting aggregating (IOWA) operator based SM aggregator is proposed to assess the quality of selected SMs. The experiment evaluates IOM-LGSD with the ontology alignment evaluation initiative (OAEI) Benchmark and three real-world sensor ontologies. The evaluation underscores the effectiveness of IOM-LGSD in efficiently identifying high-quality ontology alignments, which consistently outperforms comparative methods in terms of effectiveness and efficiency.</p></abstract>

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

The amount of multilingual data on the Web proliferates; therefore, developing ontologies in various natural languages is attracting considerable attention. In order to achieve semantic interoperability for the multilingual Web, cross-lingual ontology matching techniques are highly required. This paper proposes a Multilingual Ontology Matching (MoMatch) approach for matching ontologies in different natural languages. MoMatch uses machine translation and various string similarity techniques to identify correspondences across different ontologies. Furthermore, we propose a Quality Assessment Suite for Ontologies (QASO) that comprises 14 metrics, out of which seven metrics are used to assess the quality of the matching process and seven metrics are used to evaluate the quality of the ontology. We present an in-depth comparison of different string similarity techniques across various languages to get the most effective similarity measure(s) between multilingual terms. To illustrate the applicability of our approach and how it can be used in different domains, we present two use cases. MoMatch has been implemented using Scala and Apache Spark under an open-source license. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2020). MoMatch has achieved significantly high precision, recall, and F-measure compared to five state-of-the-art matching approaches.

Solving Ontology Metamatching Problem through Improved Multiobjective Particle Swarm Optimization Algorithm

In recent years, knowledge representation in the Artificial Intelligence (AI) domain is able to help people understand the semantics of data and improve the interoperability between diverse knowledge-based applications. Semantic Web (SW), as one of the methods of knowledge representation, is the new generation of World Wide Web (WWW), which integrates AI with web techniques and dedicates to implementing the automatic cooperations among different intelligent applications. Ontology, as an information exchange model that defines concepts and formally describes the relationships between two concepts, is the core technique of SW, implementing semantic information sharing and data interoperability in the Internet of Things (IoT) domain. However, the heterogeneity issue hampers the communications among different ontologies and stops the cooperations among ontology-based intelligent applications. To solve this problem, it is vital to establish semantic relationships between heterogeneous ontologies, which is the so-called ontology matching. Ontology metamatching problem is commonly a complex optimization problem with many local optima. To this end, the ontology metamatching problem is defined as a multiobjective optimization model in this work, and a multiobjective particle swarm optimization (MOPSO) with diversity enhancing (DE) (MOPSO-DE) strategy is proposed to better trade off the convergence and diversity of the population. The well-known benchmark of the Ontology Alignment Evaluation Initiative (OAEI) is used in the experiment to test MOPSO-DE’s performance. Experimental results prove that MOPSO-DE can obtain the high-quality alignment and reduce the MOPSO’s memory consumption.

Efficient Ontology Meta-Matching Based on Interpolation Model Assisted Evolutionary Algorithm

Ontology is the kernel technique of the Semantic Web (SW), which models the domain knowledge in a formal and machine-understandable way. To ensure different ontologies’ communications, the cutting-edge technology is to determine the heterogeneous entity mappings through the ontology matching process. During this procedure, it is of utmost importance to integrate different similarity measures to distinguish heterogeneous entity correspondence. The way to find the most appropriate aggregating weights to enhance the ontology alignment’s quality is called ontology meta-matching problem, and recently, Evolutionary Algorithm (EA) has become a great methodology of addressing it. Classic EA-based meta-matching technique evaluates each individual through traversing the reference alignment, which increases the computational complexity and the algorithm’s running time. For overcoming this drawback, an Interpolation Model assisted EA (EA-IM) is proposed, which introduces the IM to predict the fitness value of each newly generated individual. In particular, we first divide the feasible region into several uniform sub-regions using lattice design method, and then precisely evaluate the Interpolating Individuals (INIDs). On this basis, an IM is constructed for each new individual to forecast its fitness value, with the help of its neighborhood. For testing EA-IM’s performance, we use the Ontology Alignment Evaluation Initiative (OAEI) Benchmark in the experiment and the final results show that EA-IM is capable of improving EA’s searching efficiency without sacrificing the solution’s quality, and the alignment’s f-measure values of EA-IM are better than OAEI’s participants.

Alignment Conservativity Under the Ontology Change

Recently, many methods have appeared to solve the problem of the evolution of alignment under the change of ontologies. The main challenge for them is to maintain consistency of alignment after applying the change. An alignment is consistent if and only if the ontologies remain consistent even when used in conjunction with the alignment. The objective of this work is to take a step forward by considering the alignment evolution according to the conservativity principle under the change of ontologies. In this context, an alignment is conservative if the ontological change should not introduce new semantic relationships between concepts from one of the input ontologies. The authors give methods for the conservativity violation detection and repair under the change of ontologies and they carry out an experiment on a dataset adapted from the Ontology Alignment Evaluation Initiative. The experiment demonstrates both the practical applicability of the proposed approach and shows the limits of the alignment evolution methods compared to the alignment conservativity under the change of ontologies.

Matching Cybersecurity Ontologies on Internet of Everything through Coevolutionary Multiobjective Evolutionary Algorithm

Since Internet of Everything (IoE) makes all the connections that come online more relevant and valuable, they are subject to numerous security and privacy concerns. Cybersecurity ontology is a shared knowledge model for tackling the security information heterogeneity issue on IoE, which has been widely used in the IoE domain. However, the existing CSOs are developed and maintained independently, yielding the CSO heterogeneity problem. To address this issue, we need to use the similarity measure (SM) to calculate two entities’ similarity value in two CSOs and, on this basis, determine the entity correspondences, i.e., CSO alignment. Usually, it is necessary to integrate various SMs to enhance the result’s correctness, but how to combine and tune these SMs to improve the alignment’s quality is still a challenge. To face this challenge, this work first models CSO matching problem as a Constrained Multiobjective Optimization Problem (CMOOP) and then proposes a Coevolutionary Multiobjective Evolutionary Algorithm (CE-MOEA) to effectively address it. In particular, CE-MOEA uses the multiobjective evolutionary paradigm to avoid the solutions’ bias improvement and introduces the coevolutionary mechanism to trade off Pareto Front’s (PF’s) diversity and convergence. The experiment uses Ontology Alignment Evaluation Initiative’s (OAEI’s) bibliographic track and conference track and five real CSO matching tasks to test CE-MOEA’s performance. Comparisons between OAEI’s participants and EA- and MOEA-based matching techniques show that CE-MOEA is able to effectively address various heterogeneous ontology matching problems and determine high-quality CSO alignments.

A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm (D^{2}CMOPSO). The sophistication of the D^{2}CMOPSO based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.

Matching Knowledge Graphs with Compact Niching Evolutionary Algorithm

To address the Knowledge Graph (KG) heterogeneity issue, we need to determine a set of entity correspondences, which requires aggregating several complementary similarity measures to improve the confidence of the results. How to determine the suitable aggregating weights for the similarity measures to improve the KG alignment’s quality is called the KG meta-matching problem, whose challenge of scalability remains significant in the Semantic Web (SW) domain. To face this challenge, this work proposes a Compact Niching Evolutionary Algorithm (CNEA) based matching technique. We first propose an approximate evaluation metric on the alignment’s quality, and on this basis, a multi-modal optimization model is constructed to formally define the KG meta-matching problem. Then, a niching strategy is combined with EA’s evolutionary paradigm to address it, which is able to effectively locate and maintain multiple global optimal solutions. Moreover, a serial matching framework and the compact evolutionary mechanism are presented to improve CNEA’s efficiency. In particular, the former reduces the algorithm’s search space of the instance matching phase with schema-level alignments, and the latter uses the probability representation on the population to reduce the algorithm’s memory consumption and run time. The experiment utilizes Ontology Alignment Evaluation Initiative (OAEI)’s KG track to test our proposal’s performance, and experimental results show that CNEA-based KG matching technique is both effective and efficient.

Semi-Automatic Ontology Matching Based on Interactive Compact Genetic Algorithm

Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffer from the limitation of similarity measure, whose alignment’s quality cannot be ensured. To overcome this drawback, in this work, an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique is proposed, which utilizes both the compact encoding mechanism and expert interacting mechanism to improve the algorithm’s performance and the alignment’s quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed, which is able to reduce the expert’s workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test our proposal’s performance. The experimental results show that our approach is able to make use of the expert knowledge to improve the alignment’s quality, and it also outperforms OAEI’s participants.

Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm

To implement co-operation among applications on the Internet of Things (IoT), we need to describe the meaning of diverse sensor data with the sensor ontology. However, there exists a heterogeneity issue among different sensor ontologies, which hampers their communications. Sensor ontology matching is a feasible solution to this problem, which is able to map the identical ontology entity pairs. This work investigates the sensor ontology meta-matching problem, which indirectly optimizes the sensor ontology alignment’s quality by tuning the weights to aggregate different ontology matchers. Due to the largescale entity and their complex semantic relationships, swarm intelligence (SI) based techniques are emerging as a popular approach to optimize the sensor ontology alignment. Inspired by the success of the flower pollination algorithm (FPA) in the IoT domain, this work further proposes a compact FPA (CFPA), which introduces the compact encoding mechanism to improve the algorithm’s efficiency, and on this basis, the compact exploration and exploitation operators are proposed, and an adaptive switching probability is presented to trade-off these two searching strategies. The experiment uses the ontology alignment evaluation initiative (OAEI)’s benchmark and the real sensor ontologies to test CFPA’s performance. The statistical comparisons show that CFPA significantly outperforms other state-of-the-art sensor ontology matching techniques.

A Schema-Free Instance Matching Algorithm Based on Virtual Document Similarity

With the continuous development of semantic web, especially of the web of data, several knowledge bases expressed by ontologies are independently created and added to the Linked Open Data (LOD) cloud, on a daily basis. A major challenge for the LOD paradigm is to discover resources that refer to the same real-world object, in order to interlink web resources and hold large scale data integration and sharing. In this context, instance matching is a promising solution. It aims to link co-referent instances belonging to heterogeneous knowledge bases with owl:sameAs links. Several state-of-the-art existing approaches addressing this issue are based on the prior schema-level matchings, which does not avoid the limitation of heterogeneity at the property-level. In this paper, we propose a schema-free, scalable and efficient instance matching approach that is independent from matching results at the schema-level. We transform the instance matching problem to a document similarity problem and we solve it by a Clustering technique that uses an Ascendant Hierarchical Clustering algorithm to group similar instances in the same clusters. Furthermore, we design multiple validating patterns that use some structural information to validate obtained mappings and eliminate wrong ones. Experiments on instance matching track from Ontology Alignment Evaluation Initiative (OAEI) show that our approach gets prominent results compared to several participating systems in OAEI’2019, OAEI’2020 and OAEI’2021.

Matching Network of Ontologies: A Random Walk and Frequent Itemsets Approach

A System of Systems (SoS) is a complex set of IS (Information Systems) created by the aggregation and interconnection of ISs. SoS brings unexpected behavior and functionality during its construction to the benefit of its users and the SoS itself. The integration of SoSs is a matter of time. However, as SoSs can behave as an organization, it may be inappropriate to try to integrate individual IS members separately. On the other hand, manually integrating the SoS as a whole can be unfeasible due to its complexity. If an SoS has ontologies modeling the knowledge, the integration of SoSs can be translated into a problem of a network of ontologies alignment. However, it creates another challenge: computing each possible pair of entities inside each network’s ontology can have unfeasible execution time, even using the best matchers available. In this article, we propose to mine the data from the networks using random walks and frequent item sets algorithm and discover relevant nodes elected as candidate entities. Next, the networks are pruned by an algebraic method eliminating identical entities. The relevant nodes are reinserted in the network to avoid losing essential correspondences. After the pre-processing step, data is sent to two matchers to obtain metrics and compare the results with the pairwise brute force approach and previous work. We identified relevant nodes with recall up to 0.75. The results are promising since precision and recall are closer to the force brute, and execution time is shorter, even more, when the size of the networks and the number of ontologies to be compared increases. We validate our approach using ontologies created from the OAEI (Ontology Alignment Evaluation Initiative).