Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery

Fernandez, Raul Castro; Mansour, Essam; Qahtan, Abdulhakim; Elmagarmid, Ahmed K.; Ilyas, Ihab F.; Madden, Samuel; Ouzzani, Mourad; Stonebraker, Michael; Tang, Nan

doi:10.1109/icde.2018.00093

Cited by 60 publications

(52 citation statements)

References 27 publications

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“…In effect, existing approaches put the burden on the SPARQL users or systems to find out precisely how to write a conjunctive federated query. An emerging research direction entails automatically discovering links between datasets using Word Embeddings [15]. However, the current focus is mostly on relational data or unstructured data [7].…”

Section: Related Workmentioning

confidence: 99%

“…Based on these virtual links, a set of more than twelve specialized federated query templates over these data stores was defined 14 . These templates are also available through a template-based search engine 15 . Moreover, as an example of facilitating knowledge discovery, we can mention the virtual link sets between OMA and Bgee.…”

Section: Benefits and A Sib Swiss Institute Of Bioinformatics' Applicmentioning

confidence: 99%

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VoIDext: Vocabulary and Patterns for Enhancing Interoperable Datasets with Virtual Links

Farias

Stockinger

Dessimoz

2019

Lecture Notes in Computer Science

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Semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains. Causes for this challenge are context-specific requirements (i.e. no "one model fits all"), different data modelling decisions, domain-specific purposes, and technical constraints. Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is solved, querying and accessing the data of distributed RDF datasets on the Web is not straightforward. This is because of the complex and fastidious process needed to understand how these datasets can be related or linked, and consequently, queried. To address this issue, we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) by introducing new terms such as the Virtual Link Set concept and data model patterns. A virtual link is a connection between resources such as literals and IRIs (Internationalized Resource Identifier) with some commonality where each of these resources is from a different RDF dataset. The links are required in order to understand how to semantically relate datasets. In addition, we describe several benefits of using virtual links to improve interoperability between heterogenous and independent datasets. Finally, we exemplify and apply our approach to multiple world-wide used RDF datasets.Keywords: data interoperability · virtual link · Vocabulary of Interlinked Datasets (VoID) · federated query. 5

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Section: Related Workmentioning

confidence: 99%

Section: Benefits and A Sib Swiss Institute Of Bioinformatics' Applicmentioning

confidence: 99%

VoIDext: Vocabulary and Patterns for Enhancing Interoperable Datasets with Virtual Links

Farias

Stockinger

Dessimoz

2019

Lecture Notes in Computer Science

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“…Thus, the features upon which their model was trained were not the choice of matchers, but rather the structure and various counting statistics of the match result. Recently, word embeddings were used to enhance the effectiveness of schema matchers by Fernandez et al (2018).…”

Section: Machine Learning For Data Integrationmentioning

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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“…To avoid this issue, federated approaches have recently been proposed (24–27), but to the best of our knowledge, none of them proposes a vocabulary and patterns to extensively, explicitly and formally describe how the data sources can be interlinked further than mostly considering ‘same as’-like mappings; in effect, they put the burden on the users to find out precisely how to write a conjunctive federated query. An emerging research direction entails automatically discovering links between datasets using Word Embeddings (28). We did not pursue this approach, given that it is computationally expensive and that for our study writing the relational-to-Resource Description Framework (RDF) mappings proved more straightforward.…”

Section: Introductionmentioning

confidence: 99%

Enabling semantic queries across federated bioinformatics databases

et al. 2019

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Motivation: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases.Results: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface.

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Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery

Cited by 60 publications

References 27 publications

VoIDext: Vocabulary and Patterns for Enhancing Interoperable Datasets with Virtual Links

VoIDext: Vocabulary and Patterns for Enhancing Interoperable Datasets with Virtual Links

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

Enabling semantic queries across federated bioinformatics databases

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