A Complex Alignment Benchmark: GeoLink Dataset

Zhou, Lu; Cheatham, Michelle; Krisnadhi, Adila; Hitzler, Pascal

doi:10.1007/978-3-030-00668-6_17

Cited by 14 publications

(14 citation statements)

References 6 publications

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“…However, to the best of our knowledge, none of these were applied to match ocean science dataset schemas, neither pair-wise nor to mediated schemas or ontologies. Zhou et al (2018) proposed a complex real-world ontology alignment benchmark made on two separate GeoLink dataset ontologies. However, even this unique example attempts to automate ontology alignment and not automatically match dataset schemas against these ontologies.…”

Section: Matchmentioning

confidence: 99%

Artificial intelligence for ocean science data integration: current state, gaps, and way forward

Sagi

Lehahn²,

Bar

2020

Elem Sci Anth

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The study of the ocean is one of the biggest scientific challenges of the 21st century. It has a direct impact on our understanding of Earth's climate (Stocker et al., 2013) and biogeochemical cycling (Field et al., 1998), as well as on our ability to provide human society with food, chemicals, and energy (Lehahn et al., 2016). Oceanographic research strongly relies on in-situ and remotely-sensed observations, which describe physical, chemical, and biological seawater properties at a given time and place. These observations are collected from various crewed and autonomous platforms, including research vessels, floats (Roemmich et al., 2009), drifters (Lumpkin et al., 2017), autonomous vehicles (Eriksen et al., 2001), and satellites (Lehahn et al., 2018), providing an abundance of interdisciplinary information on processes occurring over a wide range of spatial (from micrometers to thousands of kilometers) and temporal (from seconds to decades) scales. Over the last century, numerous in-situ and remotelysensed measurements have been taken, resulting in the creation of an increasingly large amount of oceanic data. In recent years, with the enhanced utilization of satellites and autonomous observation platforms, these data are collected at a blistering rate. Improving the scientific community's ability to integrate, share, and explore this vast amount of data is an urgent task that will contribute substantially to our understanding of the ocean and its role in the Earth system. Several public data repositories have emerged to enable the archiving and sharing of data collected between researchers. For example, PANGEA (2020), a data repository for publishing and distributing georeferenced data

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Section: Matchmentioning

confidence: 99%

Artificial intelligence for ocean science data integration: current state, gaps, and way forward

Sagi

Lehahn²,

Bar

2020

Elem Sci Anth

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“…There are two different types of correspondences, which are simple correspondence and complex correspondence [27]. Simple correspondence refers to basic 1:1 simple alignment between two ontologies, such as 1:1 class equivalence, property equivalence, and 1:1 class subsumption, property subsumption.…”

Section: Simple and Complex Correspondencesmentioning

confidence: 99%

“…It may comprise more than one class or property in both ontologies, such as 1:n equivalence, m:n equivalence, and m:n arbitrary relationship. With respect to the correspondence patterns, Zhou et al list roughly 12 different types of simple and complex correspondence patterns [27]. In the Enslaved benchmark, there are three different types that emerge most frequently in ontology matching tasks, which are listed in Table 2.…”

Section: Simple and Complex Correspondencesmentioning

confidence: 99%

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The Enslaved Dataset

Zhou

Shimizu

Hitzler

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Ontology alignment has taken a critical place for helping heterogeneous resources to interoperate. It has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers to find simple 1:1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. Even if the complex alignment systems are developed, the performance of finding complex relations still has a lot of room for improvement. One reason for this limitation may be that there are still few applicable alignment benchmarks that contain such complex relationships that can raise researchers' interests. In this paper, we propose a real-world dataset from the Enslaved project as a potential complex alignment benchmark. The benchmark consists of two resources, the Enslaved Ontology along with a Wikibase repository holding a large number of instance data from the Enslaved project, as well as a manually created reference alignment between them. The alignment was developed in consultation with domain experts in the digital humanities. The alignment not only includes simple 1:1 equivalence correspondences, but also more complex m:n equivalence and subsumption correspondences and are provided in both Expressive and Declarative Ontology Alignment Language (EDOAL) format and rule syntax. The Enslaved benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) 2020 and is completely free for public use to assist the researchers in developing and evaluating their complex alignment algorithms.

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“…a “Wetlands” in one ontology is equivalent to a “Swamp Or Marsh” in another ontology. However, 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 [ 19 ]. Therefore, the traditional ontology matchers is not able to determine the high-quality ontology alignment, and the complex ontology matching problem is one of the challenges in the ontology matching domain [ 11 ], e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Hydrography Ontology Alignment Through Compact Particle Swarm Optimization Algorithm

Wang

Yao

Wan

et al. 2020

Lecture Notes in Computer Science

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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.

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A Complex Alignment Benchmark: GeoLink Dataset

Cited by 14 publications

References 6 publications

Artificial intelligence for ocean science data integration: current state, gaps, and way forward

Artificial intelligence for ocean science data integration: current state, gaps, and way forward

The Enslaved Dataset

Optimizing Hydrography Ontology Alignment Through Compact Particle Swarm Optimization Algorithm

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