Inode

Abstract

A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such systems are a real opportunity for our community to cater to users with different domain and data science expertise. We introduce INODE - an end-to-end data exploration system - that leverages, on the one hand, Machine Learning and, on the other hand, semantics for the purpose of Data Management (DM). Our vision is to develop a classic unified, comprehensive platform that provides extensive access to open datasets, and we demonstrate it in three significant use cases in the fields of Cancer Biomarker Research, Research and Innovation Policy Making, and Astrophysics. INODE offers sustainable services in (a) data modeling and linking, (b) integrated query processing using natural language, (c) guidance, and (d) data exploration through visualization, thus facilitating the user in discovering new insights. We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we briefly illustrate how this work paves the way for new research opportunities in DM.

Lessons learned to boost a bioinformatics knowledge base reusability, the Bgee experience

“…This facilitates the integration with other cancer biomarker-related data such as the differential expression dataset that OncoMX integrates. In addition, work toward a federated and automatic interoperability between Bgee and OncoMX has been done in the context of the Intelligent Open Data Exploration (INODE) project [ 57 ]. By applying this federated approach, we address most of the issues mentioned in the first paragraph of subsection File-based data exchange.…”

“…To address drawbacks of the file-based interoperability between Bgee and OncoMX as discussed in subsection File-based data exchange, we federate both KBs in the context of the INODE project [ 57 ] by applying data virtualization and one-side interoperability approaches. To implement this federation, we use Teiid [ 92 ], a real-time integration engine that supports a high query volume and transactions.…”

“…Finally, in the context of the INODE project [ 57 ], we provide an ontology-based data access to the OncoMX-Bgee federation with the Ontop tool. This is done mainly to achieve two aims: to improve the semantics of the data sources by applying ontologies to reduce ambiguities and to provide the possibility of performing federated SPARQL queries, hence the capacity to interoperate with other knowledge graphs through its SPARQL endpoint [ 95 ].…”

Interactive network visualization of opioid crisis research: a tool for reinforcing data linkage skills for public health policy researchers

“…Furthermore, recent work on data integration and federation demonstrates advances in ontology and knowledge graph-based approaches allowing for integration, querying, analysis, and visualization across heterogeneous data sources (Sima et al, 2019;SN SciGraph, 2019;Cox et al, 2020;Amer-Yahia et al, 2021;Morris et al, 2023). For example, SPOKE (Morris et al, 2023) and Springer Nature SciGraph (SN SciGraph, 2019) use a knowledge graph (KG) to interlink and query different datasets.…”