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Extracting meaningful and actionable insights from data is only possible if data is easily and intuitively accessible to and searchable for users. But as data accumulates, finding the right bit of information becomes challenging.
Knowledge Graphs have proven extremely powerful in surfacing previously unknown insights and relations in the data. They enable unprecedented query expressiveness and allow to make all instance data and its related metadata searchable, accessible and shareable.
metaphactory is an excellent example of leveraging Knowledge Graphs to bring together information distributed across siloed sources and departments, ultimately empowering end users to unlock the value of data, especially when it comes to search.
This blog post provides an overview of the metaphactory search components. To make the experience as concrete as possible, we provide examples using the most prominent publicly available knowledge graph: Wikidata. For that purpose, we are hosting an instance of our metaphactory platform on top of Wikidata for you to experience. The specific implementations described in this post are based on examples from the Life Sciences domain, but can be adjusted to match information needs across other usage scenarios or verticals.
Written by Jeen Broekstra on . Posted in RDF / SPARQL
(Reading time: 7 - 13 minutes)
In any Knowledge Graph-based project, keeping track of where data comes from is important. When you know the source of your facts or assertions, you can contextualize those facts: how relevant is assertion X to my current research, is it from a source that I personally trust, and if I have two conflicting views how can I decide which source to go with? Apart from issues of trust and confidence, tracking the source also can serve more mundane goals, such as knowledge graph maintenance: source X has published a new edition of their data set, so we need to replace the relevant data in our own Knowledge Graph, and so on.
Keeping track of the source of data is often referred to as provenance. In this blog post, we will look at provenance tracking in RDF Knowledge Graphs using the Wikidata dataset as an example, and we will look at how RDF-star and SPARQL-star, two new community efforts to extend the RDF model, can make this task easier.
In my previous blog post on building Knowledge Graph-driven, FAIR Data platforms I discussed the importance of data and data-driven decisions, processes and tools in accelerating digital transformation. Knowledge Graphs have revolutionized the way data can be accessed and used, and have helped enterprises overcome the challenges posed by distributed silos where information is available to limited audiences, in heterogeneous formats, and represented according to different models. They have led to great advances in terms of data integration, interoperability and accessibility, and have allowed companies to tap into the full potential of their data assets and transform data into valuable and actionable knowledge.
With metaphactory, our customers have been able to rapidly build Knowledge Graph-based applications enabling them to focus on business outcomes, reduce development efforts and quickly produce results that matter:
Customers in Life Sciences & Pharma have been able to fast-track drug development and drug repurposing.
Customers in Engineering & Manufacturing have established smart manufacturing processes and have sped up research, documentation processes and industrial configuration management.
Customers in Government and Cultural Heritage organizations have streamlined data curation and digital publishing processes, making cultural heritage content intuitively available to the public.
All of these applications utilize a semantic data model to not only describe the domain, but also drive data integration, tie in term vocabularies, or derive UI templates to create a model-driven user interface. Such a semantic data model is called an ontology. According to Gartner, "Ontologies are structural frameworks for organizing information and are used as knowledge representation. Ontology management supports and expands data modeling methodologies to exploit the business value locked up in information silos."
Raul Palma leads the data analytics and semantics department at the Poznan Supercomputing and Networking Center (PSNC), where he coordinates the R&D activities and the center’s participation in various EU projects around these topics. In this guest post for the metaphacts blog, Raul explains how knowledge graph technology can address data integration challenges in the agri-food sector, showcasing it through a few use cases. He describes how he leveraged metaphactory to build a domain-specific application - FOODIE - that delivers intuitive access to distributed, heterogeneous data sources and allows end users to extract meaningful insights.
FOODIE is an agriculture knowledge hub delivered as a Web application built on top of metaphactory Knowledge Graph platform. The application enables an integrated view and access over multiple datasets which have been collected from various and heterogeneous sources relevant to the agriculture sector, transformed, and published as Linked Data / in a Knowledge Graph.
Written by Sebastian Schmidt on . Posted in FAIR Data
(Reading time: 7 - 14 minutes)
At metaphacts we help customers leverage knowledge graphs to unlock the value of their data assets and drive digital transformation. We started out with this mission in 2014 and, since then, we've served a multitude of customers in pharma and life sciences, engineering and manufacturing, finance and insurance, as well as digital humanities and cultural heritage.
This blog post will give you an overview of what we have developed in customer projects over the years as our game plan to build a Knowledge Graph-driven, FAIR Data platform and drive digital transformation with data. The post will show you how our product metaphactory can support you every step of the way, and will highlight examples from the life sciences and pharma domains.
