DBpedia Member Feature – Over the last year we gave DBpedia members multiple chances to present their work, tools and applications. In this way, our members gave exclusive insights on the DBpedia blog. This time we will continue with Ontotext, who helps enterprises identify meaning across diverse datasets and massive amounts of unstructured information. Jarred McGinnis presents the beauty of data virtualization. Have fun reading!

by Jarred McGinnis, Ontotext

The beauty and power of knowledge graphs is their abstraction away from the fiddly implementation details of our data. The data and information is organized in a way human-users understand it regardless of the physical location of the data, the format and other low-level technical details. This is because the RDF of the knowledge graph enables a schema-less, or a schema-agnostic, approach to facilitate the integration of multiple heterogeneous datasets.

Semantic technology defines how data and information is inter-related. These relationships give context and that context is what gives our data meaning that can be understood by humans AND machines. That’s where the knowledge part of the graph comes from and it is a powerful way of providing a single view on disparate pieces of information.

ETL is Still Your Best Bet

When possible it’s better to pay the initial costs of the ETL process. In a previous blog post, we talked about how knowledge graphs generously repay that investment of time and effort taken in data preparation tasks. However, there are a number of reasons that it is impossible or impractical, such as the size of the dataset or the data exists in a critical legacy system where an ETL process would create more problems than it fixed. In these cases, it is better to take a data virtualization approach.

Ontotext GraphDB provides data virtualization functionality to realize the benefits of real-time access to external data, remove or reduce the need for middle-ware for query processing, reduce support and development time, increase governance and reduce data storage requirements.

Firstly, There’s Federation.

RDF is the language of the semantic web. If you are working with Linked Data, it opens up a world of billions upon billions of factual statements about the world, which is probably why you chose to work with linked data in the first place. Nice work! And that means I don’t have to tell you that DBpedia, a single data set among hundreds, has three billion triples alone. You are no longer limited by the data your organization holds. Queries about internal data can be seamlessly integrated with multiple external data sources.

For example, suppose you want to query well-known people and their birth places for a map application. It’s possible to create a single query that gets the person’s information from DBpedia, which would give you the birthplace and take those results to query another data source like Geonames to provide the geographic coordinates to be able to add them to a mapping application. Since both of these data sources are linked data, it’s relatively straightforward to write a SPARQL query that retrieves the information.

It doesn’t even have to be another instance of GraphDB. It’s part of the reason Ontotext insists on using open standards. With any equally W3C-compliant knowledge graph that supports a SPARQL endpoint, it is possible to retrieve the information you want and add it to your own knowledge graph to do with as you please. A single query could pull information from multiple external data sources to get the data you are after, which is why federation is an incredibly powerful tool to have.

The Business Intelligence Ecosystem Runs on SQL.

Ontotext is committed to lowering the costs of creating and consuming knowledge graphs. Not every app developer or DBA in an organization is going to have the time to work directly with the RDF data models. A previous version of GraphDB 9.4 added the JDBC driver to ensure those who need to think and work in SQL can access the power of the knowledge with SQL.

Knowing the importance and prominence of SQL for many applications, we have a webinar demonstrating how GraphDB does SQL-to-SPARQL transformation and query optimization and how Microsoft’s Power BI and Tableau can be empowered by knowledge graphs. GraphDB provides a SQL interface to ensure those who prefer a SQL view of the world can have it.

Virtualization vs ETL

The most recent GraphDB release has added virtualization functionality beyond simple federation. It is now possible to create a virtual graph by mapping the columns and rows of a table to entities in the graph. It becomes possible to retrieve information from external relational databases and have it play nice with our knowledge graph. We aren’t bound by data that exists in our graph or even in RDF format. Of course it would be easier and certainly quicker to ETL a data source into a single graph and perform the query, but it is not always possible, because either the size of the dataset is too large, it gets updated too frequently or both.

For example, in the basement of your organization is a diesel-powered database that has a geological strata of decades old data and that is critical to the organization. You know and I that database is never going to be ETLed into the graph. Virtualization is your best bet, by creating a virtual graph and mapping that decade old format for client orders by saying,  “When I query about ‘client order’, you need to go to this table and this column in that behemoth server belching black smoke that’s run by Quismodo and return the results”.

There will be an inevitable hit to query performance but there are a number of situations where slow is better than not at all. Such as the over-egged example above. It is important to understand the trade-offs and practicalities between ETL and virtualization. The important thing for Ontotext is to make sure GraphDB is capable of both and provide a combined approach to maximize flexibility. There is also a webinar on this topic, introducing the open-source Ontop.

GraphDB Gives You Data Agility

Data virtualization and federation come with costs as well as benefits. There is no way we are ever going to master where the data you need exists and what format. The days of centralized control are over. It’s about finding the technology that gives your agility and GraphDB’s added virtualization capabilities enables you to create queries that include external open sources and merge it seamlessly with your own knowledge graph. Virtualization of relational databases creates incredible opportunities for applications to provide users with a single coherent view on the complex and diverse reality of your data ecosystem.

Jarred McGinnis

Jarred McGinnis is a managing consultant in Semantic Technologies. Previously he was the Head of Research, Semantic Technologies, at the Press Association, investigating the role of technologies such as natural language processing and Linked Data in the news industry. Dr. McGinnis received his PhD in Informatics from the University of Edinburgh in 2006.

The post Data Virtualization: From Graphs to Tables and Back appeared first on DBpedia Association.

 by Milen Yankulov from Ontotext

GraphDB is a family of highly efficient, robust, and scalable RDF databases. It streamlines the load and use of linked data cloud datasets, as well as your own resources. For easy use and compatibility with the industry standards, GraphDB implements the RDF4J framework interfaces, the W3C SPARQL Protocol specification, and supports all RDF serialization formats. The database offers open source API and it is the preferred choice of both small independent developers and big enterprise organizations because of its community and commercial support, as well as excellent enterprise features such as cluster support and integration with external high-performance search applications – Lucene, Solr, and Elasticsearch. GraphDB is build 100% on Java in order to be OS Platform independent.

GraphDB is one of the few triplestores that can perform semantic inferencing at scale, allowing users to derive new semantic facts from existing facts. It handles massive loads, queries, and inferencing in real-time.

GDB Architecture

GraphDB Workbench

Workbench is the GraphDB web-based administration tool. The user interface is similar to the RDF4J Workbench Web Application, but with more functionality.

GraphDB Engine

The GraphDB Workbench REST API can be used for managing locations and repositories programmatically, as well as managing a GraphDB cluster.  It includes connecting to remote GraphDB instances (locations), activating a location, and different ways for creating a repository.

It includes also connecting workers to masters, connecting masters to each other, as well monitoring the state of a cluster.

GraphQL access via Ontotext Platform 3

GraphDB enables Knowledge Graph access and updates via GraphQL. GraphDB is extended to support the efficient processing of GraphQL queries and mutations to avoid the N+1 translation of nested objects to SPARQL queries.

Ontotext offers three editions of GraphDB: Free, Standard, and Enterprise.

Free – commercial, file-based, sameAs & query optimizations, scales to tens of billions of RDF statements on a single server with a limit of two concurrent queries.

Standard Edition (SE) – commercial, file-based, sameAs & query optimizations, scales to tens of billions of RDF statements on a single server and an unlimited number of concurrent queries.

Enterprise Edition (EE) – high-availability cluster with worker and master database implementation for resilience and high-performance parallel query answering.

Why GraphDB is preferred choice of many data architects and data ops?

3 Reasons:

1. High Availability Cluster Architecture

GraphDB offers you a high-performance cluster proven to scale in production environments. It supports 

• (1) coordinating all read and write operations, 

• (2) ensuring that all worker nodes are synchronized,

• (3) propagating updates (insert and delete tasks) across all workers and checking updates for inconsistencies, 

• (4) load balancing read requests between all available worker nodes
  

Improved resilience

failover, dynamic configuration

Improved query bandwidth

larger cluster means more queries per unit time

Deployable across multiple data centres

Elastic scaling in cloud environments

Integration with search engines

Cluster Management and Monitoring

It supports

(1) automatic cluster reconfiguration in the event of failure of one or more worker nodes, 

(2) a smart client supporting multiple endpoints.

2. Easy Setup

GraphDB is 100% Java based in order to be Platform Independent. It is available through Native Installation Packages or Open Maven. It supports also Puppet and could be Dockerized. GraphDB is Cloud agnostic – It could be deployd on AWS, Azure, Google Cloud, etc.

3. Support

Based on the Edition you are using you could use the Community Support (StackOverFlow monitoring)

Ontotext has its Dedicated Support Team tha could assist through Customized Runbooks, Easy Slack communication, Jira Issue-Tracking System 

A big thank you to Ontotext for providing some insights into their product and database.

Yours,

DBpedia Association

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