Many have strived to make computers ‘think’, ‘learn’ and therefore ‘act’ like humans, all with varying levels of success to date; from machine learning and deep neural networks to knowledge graphs with semantic reasoning.
Artificial intelligence (AI) is the “scientific field of study which aims to understand and build intelligent entities by automating human intellectual tasks”. This trending term is frequently seen in reports, papers, articles and news stories, as interest in AI continues to increase.
There are many parallels between the process of learning in humans, and the work undertaken by computer scientists while creating AI applications. One of the most fundamental human processes is our ability to store information in our memory, and how we then use that information to navigate through the world around us. But our brains do not store this information in tables with rows and columns, with foreign and primary keys like a SQL database… so why should computers? …
With the increasing popularity of smartphones, personal assistants and applications, comes the need for better on-device recommendation engines.
Recommendation engines are used by many well-known companies such as Amazon, Netflix and Spotify. Currently they tend to be based on four technologies: Collaborative filtering; Content based filtering; Demographic filtering; and Knowledge-based filtering. Each of these models has specific performance weaknesses which often result in poor user rating for relevance.
Mobile phones contain contextual data which could improve recommendation quality; for example, user data in the form of calendars, SMS messages, emails, web search history and app usage logs. …
Following our earlier blog post on ‘Determining Compatbility’, we have teamed up with our partners at metaphacts to create an innovative knowledge graph-based application. The application is built on top of metaphactory, a knowledge graph management, visualisation and interaction platform, and RDFox, our knowledge graph and semantic reasoning engine.
Together, metaphactory and RDFox deliver unprecedented results in compatibility determination scenarios by allowing users to quickly and efficiently gain access to actionable and meaningful insights. This blog will demonstrate the functionality of the metaphactory & RDFox joint solution, using an industrial configuration use case example.
Part One demonstrated how OST Music, a hypothetical music streaming service, was able to link and enrich large datasets of music industry data into a unified knowledge graph.
This article will explain how the same music service was able to validate and query their knowledge graph using RDFox, without compromising speed or correctness.
You can read Part One here.
With RDFox, the music platform can validate the music industry data integrated from the various sources. During the data integration process outlined in Part One, inconsistencies can be highlighted, for example, by flagging data which doesn’t corroborate between the three datasets. By doing so, the correctness of information stored within the knowledge graph can be verified. …
The music industry is a dynamic space, with daily new releases, artists, bands and albums. Information on the industry is vast, presenting music platform providers with a great challenge, if their aim is to provide a complete, up to date service for their users.
This two-part article will demonstrate how RDFox can be used within a music streaming service, to link, enrich, validate and query large datasets, with record accuracy and speed. The provider can operate a responsive application, which obtains real value from their data. …
October 2020 marks the release of RDFox Version 4. This comes with several developments, for example, full support for named graphs, full text indexing, performance enhancements, docker containerisation & high availability setup, as well as an improved data explorer. These improvements provide our users with a wider scope for applications of RDFox and we are excited to see what our users do with this feature.
“One of the most important features of Version 4 is the complete support for named graphs. This is important for many of our clients” Founder
RDFox’s functionality has been improved in Version 4 through the integration of Full Text Index support. …
The Two Strands of Artificial Intelligence
Artificial intelligence (AI) is a widely used term that conjures notions of fantasy, the future, or even threat. This is not surprising considering the multitude of movies which dramatise the role of artificial intelligence and what it may become.
In reality, artificial intelligence is a branch of computer science which aims to “understand and build intelligent entities by automating human intellectual tasks”.
These processes have contributed to numerous technological advances across various industries, for example. self-driving cars, technology for diagnosing cancer, revealing fraud in financial services and new data processing techniques. …
This article aims to provide a basic introduction to Datalog with RDFox. It will explain what Datalog is, why RDFox uses Datalog, how to write Datalog rules, how rules can enhance SPARQL query performance, and touch upon RDFox’s Datalog extensions.
Datalog is a rule language for knowledge representation. Rule languages have been in use since the 1980s in the fields of data management and artificial intelligence.
A Datalog rule is a logical implication, where both the “if” part of the implication (the rule body) and the “then” part of the implication (the rule head) consist of a conjunction of conditions. In the context of RDF, a Datalog rule conveys the idea that, from certain combinations of triples in the input RDF graph, we can logically deduce that some other triples must also be part of the graph. …
Part 4: Adam Parr
In the fourth and final article of this series, Adam Parr joins us to discuss his journey to become a founder of Oxford Semantic Technologies and his insights into the semantics world. If you missed the previous articles, you can find them here: Professor Ian Horrocks, Professor Boris Motik and Professor Bernardo Cuenca Grau.
So Adam, how did you end up in semantic technologies and become a founder of Oxford Semantic Technologies?
“I moved to Oxford in 2006 to join the Williams Formula One team where I worked for five years, before getting into venture capital. I was a founding investor of a company called Mind Foundry. In 2016, through the Mind Foundry Professors, Steve Roberts and Mike Osborne, I met Ian Horrocks. At this time, I hadn’t heard of a knowledge graph. Ian told me about the spin-out he was about to launch with two colleagues Bernardo and Boris. I loved the technology and the Professors. …
Part 3: Professor Bernardo Cuenca Grau
Bernardo is well known for his work in the area of semantic technology, knowledge representation, reasoning, and applications for data management and the web. This article will discuss his academic and professional journey and insights into the semantics world.
So Bernardo, how did you end up in semantics, Resource Description Framework and a founder of Oxford Semantic Technologies?
“My journey with Resource Description Framework, known as RDF, began by chance in 2002. I got a scholarship for a PhD from the Spanish Government. My supervisor at the time had just joined the World Wide Web Consortium (W3C), which is an international community for developing open standards for the web. They gave me a draft of an RDF primer and that was my first contact with logical reasoning. …