Introducing BioGrakn Covid
We’re excited to release an open source knowledge graph to speed up the research into Covid-19. Our goal is to provide a way for researchers to easily analyse and query large amounts of data and papers related to the virus.
BioGrakn Covid makes it easy to quickly trace information sources and identify articles and the information therein. This first release includes entities extracted from Covid-19 papers, and from additional datasets including, proteins, genes, disease-gene associations, coronavirus proteins, protein expression, biological pathways, and drugs.
For example, by querying for the virus SARS-CoV-2, we can find the associated human protein, proteasome subunit…
Exploring common concepts and differences
This is part three of Comparing Semantic Web Technologies to Grakn. In the first two parts, we looked at how RDF, RDFS, and SPARQL compare to Grakn. In this part, we look specifically at OWL and SHACL. If you haven’t read part 1, follow this link, or part 2 on this link.
To learn more, make sure to attend our upcoming webinars via this link.
OWL
OWL and Grakn
OWL is a family of Descriptive Logic (DL) based ontology language which adds ontological constructs on top of RDFS to express conditions and derive new facts. …
Exploring common concepts and differences
This is part two of Comparing Semantic Web Technologies to Grakn. In the first part, we looked at how RDF compares to Grakn. In this part, we look specifically at SPARQL and RDFS. If you haven’t read part 1, follow this link.
To learn more, make sure to attend our upcoming webinars via this link.
SPARQL
What is SPARQL
SPARQL is a W3C-standardised language to query for information from databases that can be mapped to RDF. Similar to SQL, SPARQL allows to insert and query for data. …
Exploring common concepts and differences
This article explores how Grakn compares to Semantic Web Standards, focusing specifically on RDF, XML, RDFS, OWL, SPARQL and SHACL. There are some key similarities between these two sets of technologies — primarily as they are both rooted in the field of symbolic AI, knowledge representation and automated reasoning. These similarities include:
- Both allow developers to represent and query complex and heterogeneous data sets.
- Both give the ability to add semantics to complex sets of data.
- Both enable the user to perform automated deductive reasoning over large bodies of data.
However, there are core…
Exploring common concepts and differences
This is part two of Comparing Graql to SQL. In the first part, we looked at the origin of the relational model and how to go about modelling in both Graql and SQL. In this part, we look at how to read/write data, and how we should model at a higher-level in Graql leveraging the Hypergraph and Automated Reasoning. If you haven’t read part 1, follow this link.
Inserting Data
Let’s look at how we write and read data using relational operators. …
Exploring common concepts and differences
Since the 1970s, SQL has been the de facto language to work with databases. As a declarative language, it’s straightforward to write queries and build powerful applications. However, relational databases struggle when working with interconnected and complex data. When working with such data in SQL, challenges arise especially in the modelling and querying of the data.
Graql is the query language used in Grakn. Just as SQL is the standard query language in relational databases, Graql is Grakn’s query language. Both SQL and Graql are declarative query languages that abstract away lower-level operations. Both are:
- Languages that attempt be readable…
Thank you for being part of this pioneering community
We still can’t believe that on 6–7 February we held our first user conference. Over 200 attendees and 33 speakers came together to celebrate the work of our global community. We’re so incredibly proud of what we’ve achieved together.
We’ve worked hard to make this happen. After seeing our Grakn community grow for many years, it was time they had their own space to come together and celebrate everyone’s achievements.
The work being done on Grakn is so inspiring. We’ve seen our community use Grakn in drug discovery pipelines, to build autonomous vehicles, and to fight cyber crime. Grakn Cosmos…
A knowledge graph of biomedical data for precision medicine, text mining and disease networks.
Building on the previous work done in BioGrakn — DiseaseNetworks, I’m really excited to announce the next release of BioGrakn, which expands the use cases to include precision medicine, text mining and BLAST.
Github: github.com/graknlabs/biograkn
We want to inspire anyone working in life sciences how they can leverage Grakn to better organise their complex networks of data and accelerate their knowledge discovery. We want BioGrakn to be used by anyone, whether you’re in academia, a startup or as part of a team in a large pharmaceutical. We encourage you to extend, modify and play around with this data!
In this…
A knowledge graph of biomedical data for disease networks.
I’m really excited to finally announce the first release of BioGrakn — DiseaseNetworks (DN)!
Download: grakn-core-1.4.2-biograkn_dn-0.1.zip
Github: github.com/graknlabs/biograkn
We want to give biologists and bioinformaticians easier access to work with biomedical data so we built this knowledge graph describing various disease networks. We hope you’ll find it helpful!
As part of this release, we’ve taken care of all the data modelling and data ingestion, so you just need to download, launch Grakn, and start exploring the data.
BioGrakn DN integrates a number of publicly available datasets describing various disease networks. …
Writing 151 SQL lines in 4 lines
Using SQL to query relational databases is easy. As a declarative language, it’s straightforward to write queries and build powerful applications. Having been around for many decades, SQL has developed into a language that is extremely robust, reliable, fast and useful for applications of many sizes.
However, SQL struggles when working with complex data. By complex data, I mean data that contains many entity types and is highly interconnected. When querying such data in SQL, challenges arise in the modelling and querying of the data. For example, due to the large number of necessary JOINs, it forces us to write…
Tomas Sabat
Grakn Warrior @GraknLabs | Machine Learning, Knowledge Graphs and AI.
