The Knowledge Graph Track at OAEI

Since different ontologies are mostly developed independently, establishing meaningful links between their entities, so-called ontology matching, is critical to ensure their communication. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, since none of the similarity measure can distinguish all the identical ontology entities in any context, ontology alignments generated by the automatic matching tools should be validated by the users to ensure their qualities. To improve the quality of the ontology alignment, in this work, a similarity measure is first proposed to calculate the similarity value of two ontology entities; then, an optimal model for ontology matching problem is constructed; after that, an EA-based automatic matcher is presented to solve the ontology matching problem, which can also adaptively determine the timing of getting user involved; and finally, the ontology concept hierarchy graph based reasoning approaches are proposed to tradeoff the user's workload and his work's effect. The experiment is conducted on the Interactive track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of our proposal.

To bridge the semantic gap between different offered ontologies, Ontology Engineers have proposed and implemented various matching techniques during the last decade. Still, these techniques require more efforts and investigations to achieve better precision and recall for the set of alignments. The ontology alignment process consists of mainly three different stages; namely, String based, Linguistic based and Structure based. This paper presents the evaluation and analysis of various linguistic based ontology alignment techniques and their performance during the overall alignment process. For this purpose, we have included those linguistic matching tools which are widely used in the alignment systems whose performance is relatively better in terms of precision and recall during the tests conducted for Ontology Alignment Evaluation Initiative (OAEI). The results presented in this paper were obtained using benchmark ontologies used in OAEI conference. The results and analysis shows the significance of linguistic tools in the overall alignment process. The analysis presented in this paper can also be used as future guidelines for the research community who utilizes the linguistic matchers for finding the semantically equal entities from different ontologies.

BackgroundOne of the longest running tracks in the Ontology Alignment Evaluation Initiative is the Anatomy track which focuses on aligning two anatomy ontologies. The Anatomy track was started in 2005. In 2005 and 2006 the task in this track was to align the Foundational Model of Anatomy and the OpenGalen Anatomy Model. Since 2007 the ontologies used in the track are the Adult Mouse Anatomy and a part of the NCI Thesaurus. Since 2015 the data in the Anatomy track is also used in the Interactive track of the Ontology Alignment Evaluation Initiative.ResultsIn this paper we focus on the Anatomy track in the years 2007–2016 and the Anatomy part of the Interactive track in 2015–2016. We describe the data set and the changes it went through during the years as well as the challenges it poses for ontology alignment systems. Further, we give an overview of all systems that participated in the track and the techniques they have used. We discuss the performance results of the systems and summarize the general trends.ConclusionsAbout 50 systems have participated in the Anatomy track. Many different techniques were used. The most popular matching techniques are string-based strategies and structure-based techniques. Many systems also use auxiliary information. The quality of the alignment has increased for the best performing systems since the beginning of the track and more and more systems check the coherence of the proposed alignment and implement a repair strategy. Further, interacting with an oracle is beneficial.

Nowadays, most real-world decision problems consist of two or more incommensurable or conflicting objectives to be optimized simultaneously, so-called multiobjective optimization problems (MOPs). Usually, a decision maker (DM) prefers only a single optimum solution in the Pareto front (PF), and the PF’s knee solution is logically the one if there are no user-specific or problem-specific preferences. In this context, the biomedical ontology matching problem in the Semantic Web (SW) domain is investigated, which can be of help to integrate the biomedical knowledge and facilitate the translational discoveries. Since biomedical ontologies often own large-scale concepts with rich semantic meanings, it is difficult to find a perfect alignment that could meet all DM’s requirements, and usually, the matching process needs to trade-off two conflict objectives, i.e., the alignment’s recall and precision. To this end, in this work, the biomedical ontology matching problem is first defined as a MOP, and then a compact multiobjective particle swarm optimization algorithm driven by knee solution (CMPSO-K) is proposed to address it. In particular, a compact evolutionary mechanism is proposed to efficiently optimize the alignment’s quality, and a max-min approach is used to determine the PF’s knee solution. In the experiment, three biomedical tracks provided by Ontology Alignment Evaluation Initiative (OAEI) are used to test CMPSO-K’s performance. The comparisons with OAEI’s participants and PSO-based matching technique show that CMPSO-K is both effective and efficient.

This paper presents a novel background knowledge approach which selects and combines existing mappings from a given biomedical ontology repository to improve ontology alignment. Current background knowledge approaches usually select either manually or automatically a limited number of different ontologies and use them as a whole for background knowledge. Whereas in our approach, we propose to pick up only relevant concepts and relevant existing mappings linking these concepts all together in a specific and customized background knowledge graph. Paths within this graph will help to discover new mappings. We have implemented and evaluated our approach using the content of the NCBO BioPortal repository and the Anatomy benchmark from the Ontology Alignment Evaluation Initiative. We used the mapping gain measure to assess how much our final background knowledge graph improves results of state-of-the-art alignment systems. Furthermore, the evaluation shows that our approach produces a high quality alignment and discovers mappings that have not been found by state-of-the-art systems.

As a key representation model of knowledge, ontology has been widely used in a lot of NLP related tasks, such as semantic parsing, information extraction and text mining etc. In this paper, we study the task of ontology matching, which concentrates on finding semantically related entities between different ontologies that describe the same domain, to solve the semantic heterogeneity problem. Previous works exploit different kinds of descriptions of an entity in ontology directly and separately to find the correspondences without considering the higher level correlations between the descriptions. Besides, the structural information of ontology haven’t been utilized adequately for ontology matching. We propose in this paper an ontology matching approach, named ERSOM, which mainly includes an unsupervised representation learning method based on the deep neural networks to learn the general representation of the entities and an iterative similarity propagation method that takes advantage of more abundant structure information of the ontology to discover more mappings. The experimental results on the datasets from Ontology Alignment Evaluation Initiative (OAEI1) show that ERSOM achieves a competitive performance compared to the state-of-the-art ontology matching systems. The OAEI is an international initiative organizing annual campaigns for evaluating ontology matching systems. All of the ontologies provided by OAEI are described in OWL-DL language, and like most of the other participates our ERSOM also manages the OWL ontology in its current version. OAEI: http://oaei.ontologymatching.org/

Ontology matching is usually performed to find semantic correspondences between the entity elements of different ontologies to enable interoperability. Current research on ontology matching has largely focused on representation learning. However, there still exist two limitations. Firstly, they are only used in the element level matching phase, ignoring relations of the entity. Secondly, the final alignment threshold is usually determined manually within these methods. It is difficult for an expert to adjust the threshold value and even more for non-expert user. To address these issues, we propose an alternative ontology matching framework, which models the matching process by embedding techniques with jointly encoding ontology terminological description and network structure. We further improve our iterative final alignment method by introducing an automatic adjustment of threshold method. Finally, we perform an experimental evaluation and compare it with state-of-the-art ontology matching systems on four Ontology Alignment Evaluation Initiative (OAEI) datasets. Our approach performs better than most of the systems and achieves a competitive performance. Moreover, we obtained F-measure values of 93.8% and 90.8% on the OAEI Large Biomedical Ontologies FMA-NCI and FMA-SNOMED subtasks.

Ontology matching is an effective technique to solve the ontology heterogeneous problem in Semantic Web. Since different ontology matchers do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of entities in order to increase evidence towards a potential match or mismatch. How to select, combine and tune various ontology matchers to obtain the high quality ontology alignment is one of the main challenges in ontology matching domain. Recently, Evolutionary Algorithms (EA) has become the most suitable methodology to face this challenge, however, the huge memory consumption, slow convergence and premature convergence limit its application and reduce the solution’s quality. To this end, in this paper, we propose a Hybrid Population-based Incremental Learning algorithm (HPBIL) to automatically select, combine and tune different ontology matchers, which can overcome three drawbacks of EA based ontology matching techniques and improve the ontology alignment’s quality. In one hand, HPBIL makes use of a probabilistic representation of the population to perform the optimization process, which can significantly reduce EA’s the memory consumption and the possibility of the premature convergence. In the other, we introduce the local search strategy into PBIL’s evolving process to trade off its exploration and exploitation, and this marriage between global search and local search is helpful to reduce the runtime. In the experiment, we utilize different scale testing cases provided by the Ontology Alignment Evaluation Initiative (OAEI 2016) to test HPBIL’s performance, and the experimental results show that HPBIL’s results significantly outperform other EA based ontology matching techniques and top-performers of the OAEI competitions.

With the explosive growth in generating data in the hydrographical domain, many hydrography ontologies have been developed and maintained to describe hydrographical features and the relationships between them. However, the existing hydrography ontologies are developed with varying project perspectives and objectives, which inevitably results in the differences in terms of knowledge representation. Determining various relationships between two entities in different ontologies offers the opportunity to link hydrographical data for multiple purposes, though the research on this topic is in its infancy. Different from the traditional ontology alignment whose cardinality is 1:1, i.e. one source ontology entity is mapping with one target ontology entity and vice versa, and the relationship is the equivalence, matching hydrography ontologies is a more complex task, whose cardinality could be 1:1, 1:n or m:n and the relationships could be equivalence or subsumption. To efficiently optimize the ontology alignment, in this paper, a discrete optimal model is first constructed for the ontology matching problem, and then a Compact Particle Swarm Optimization algorithm (CPSO) based matching technique is proposed to efficiently solve it. CPSO utilizes the compact real-value encoding and decoding mechanism and the objective-decomposing strategy to approximate the PSO’s evolving process, which can dramatically reduce PSO’s memory consumption and runtime while at the same time ensure the solution’s quality. The experiment exploits the Hydrography dataset in Complex track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CPSO-based approach can effectively reduce PSO’s runtime and memory consumption, and determine high-quality hydrography ontology alignments.

Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontology Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable with the entity-level matchers in a SOTA system.

Over the recent years, ontologies are widely used in the biomedical domains. However, biomedical ontology heterogeneity problem hamper the cooperation between intelligent applications based on biomedical ontologies. It is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Approaches based on Multi-Objective Evolutionary Algorithm (MOEA), such as NSGA-II, are emerging as a new methodology to solve the ontology matching problem. In this paper, to further improve the quality of biomedical ontology alignments, a hybrid NSGA-II is proposed, which modifies the knee solutions in the Pareto front by using a local search method. Experiment utilizes two biomedical ontology matching tracks provided by Ontology Alignment Evaluation Initiative (OAEI 2017). The experimental results show that our approach outperforms the participants of OAEI 2017 and NSGA-II based ontology matching technique.

The heterogeneity problem among different sensor ontologies hinders the interaction of information. Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts. Since none of the similarity measures can ensure its effectiveness in any context, usually, several measures are combined together to enhance the result’s confidence. How to find suitable aggregating weights for various similarity measures, i.e., ontology metamatching problem, is an open challenge. This paper proposes a novel ontology metamatching approach to improve the sensor ontology alignment’s quality, which utilizes the heterogeneity features on two ontologies to tune the aggregating weight set. In particular, three ontology heterogeneity measures are firstly proposed to, respectively, evaluate the heterogeneity values in terms of syntax, linguistics, and structure, and then, a semiautomatically learning approach is presented to construct the conversion functions that map any two ontologies’ heterogeneity values to the weights for aggregating the similarity measures. To the best of our knowledge, this is the first time that heterogeneity features are proposed and used to solve the sensor ontology metamatching problem. The effectiveness of the proposal is verified by comparing with using state-of-the-art ontology matching techniques on Ontology Alignment Evaluation Initiative (OAEI)’s testing cases and two pairs of real sensor ontologies.

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.

Ontology as the base of semantic web is used in many applications. Different ontologies in the same domain lead some heterogeneities and ontology matching systems are developed for resolved them. Heterogeneities have arisen owing to the fact that these ontologies have been created by various people through diverse methods. Nowadays, using large-scale ontologies in some applications such as medical fields seems inevitable. By using large-scale ontologies, some problems like the shortage of memory consumption and long duration of execution appeared in ontology matching systems. In this paper, large-scale ontology matching systems are studied and proposed a general architecture for them. Then large-scale ontology matching systems classified based on the partitioning large ontologies into several sub-ontologies, as known as the modularization, decomposition, summarization, clustering, and divide and conquer categories. This new classification will be useful for future research works in this field. In order to find out the efficiency of the ontology matching systems the results of OAEI (Ontology Alignment Evaluation Initiative) for the period 2011 to 2015 are compared. In spite of great progress, increasing accuracy is required in some section such as conference and benchmark sections.

Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto, a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more “Pythonic” manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).