The RDFox Blog

Having participated in Samsung’s launch of the Galaxy S25 at the recent Unpacked event, OST co-founder and consultant Prof. Ian Horrocks is interviewed by Cliff Saran, Managing Editor of Computer Weekly.

Professor Irene Tracey CBE FRS FMedSci, Vice-Chancellor of the University of Oxford delivered her annual speech at the Sheldonian Theatre earlier this month.

RDFox v7.2 is out now and brings a diverse array of incredible new additions ranging from a modernistic redesign of the RDFox Console, to delta queries and disk management optimisations. With something for everyone, the newest RDFox user to the seasoned expert, this is certainly not an update to miss!

With the release of RDFox v7.2, the console boasts a fresh new look and feel. We’ve revamped the colors, typography and iconography, together with an updated layout to lay the foundations for future console enhancements. Together, they deliver the benefit of a fresh modern user experience.

Expected to provide ultra-personalized experiences by combining with Samsung's on-device AI technology.

Digital search is fundamental to the way we interact with the modern world and yet it often leaves plenty to be desired, lacking expressiveness and the ability to capture human intention, providing answers that fail to align with the user’s true desires.

The basis for LLMs is just next word prediction. We're all familiar with typing messages into our iPhone and it predicting what the next word is going to be. Well LLMs in essence are just a sort of massively scaled up version of that where the model is so huge that the context can be quite large, even thousands of words, so that instead of just looking at the last two or three words that you typed in it's looking at the last thousand or even 10s of thousands of words of our conversation and then it's predicting what comes next.

Retrieval Augmented Generation, more commonly known as RAG, is widely considered today’s best solution to this problem but it’s less common to hear about how RAG is actually achieved in practice, the details of different methods, and their pros and cons.

I mean obviously, in the medical application, if you get the wrong answer, people may die. You really need to be sure you're getting the right answer. But also in a corporate setting, if you give the wrong answer, customers may come back and ask for you know refunds of extremely large contracts. It's not good. You want to be absolutely sure you're getting the right answer.

I started out personally working in a medical application where that problem was that the team were trying to use the logical reasoner to check the logical consistency of a body of medical knowledge they were capturing. The reasoning system was slow and getting even slower as they added more knowledge and they weren't even sure it was correct.

The latest version of RDFox is out now and packed with yet another round of upgrades to supercharge your applications and make your life easier. Alongside performance enhancements and UX additions to accelerate the development cycle and production systems alike, at the heart of RDFox v7.1 are privacy and security upgrades that build on the significant improvements introduced in v7.0.

By combining both LLMs and rules-based AI we can run our applications faster, reduce development costs, and critically for RAG, extract greater insights from our data, all while maintaining the truth, consistency, and auditability we’ve worked so hard to achieve.

This tutorial is a follow-up to my article about Fully interconnected construction projects that automatically adjust for changes.

Ian is the winner of the 2020 BSc Lovelace Medal for his contributions to the field of Computer Science, so we thought he’d be perfect to tell us all about Knowledge Representation and Reasoning.

Construction projects are getting increasingly more complicated and nobody has the full understanding of the derived consequences of a change. In this article, I suggest an approach to transitioning the industry to the next level by modelling information beyond 3D geometries, considering component properties such as volume flow, temperature, and power output using Linked Data technologies and incremental reasoning made possible by software such as RDFox.

Did you know RDFox has its own VS Code extension? It is designed to make your RDFox workflow easier and more intuitive with features such as auto-complete, syntax highlighting and custom icons for RDF file formats.

With huge upgrades across the board, RDFox v7.0 hails our biggest collection of several production features to date. With a new concurrency model, writes no longer block reads, increasing throughput efficiency and speed while security is being tightened with the encryption of data at rest and a new authentication option for endpoint requests.

A summary of our 2023, from conferences, new releases, and record-breaking benchmark results!

Top companies like Google, Amazon, and Microsoft have leveraged knowledge graphs to combat LLM hallucinations. Beyond intuitive data structuring, the true potential of knowledge graphs lies in enhancing it with human-like reasoning, or in other words, rules-based AI.

From designing and manufacturing to operations management, digital twins have proven indispensable, yet pitfalls loom large. Discover how rules-based AI is poised to future-proof digital twins in the context of operations, ensuring businesses thrive in an era where adaptability is paramount.

Many retailers are racing to harness the full power of generative AI, especially in virtual assistants. However, many current chatbots are plagued by inaccuracies or are often unhelpful — leaving customers dissatisfied. So, how can retailers make their chatbots provide an experience that keeps customers coming back?

Version 6.3 brings with it some exciting new features that extend the wide range of capabilities available to RDFox users.

The Game of Life is an incredible showcase of mathematical ingenuity and creativity, but that’s not really what this article is about. At the heart of the colourful patterns and charming evolution is the technology driving it all—a knowledge graph and reasoning engine capable of powerful analytics and computation, even on a tiny device.

ChatGPT is an incredible tool that has revolutionized the way we interact with technology. With no barrier to entry, remarkable knowledge, and apparent creativity, more and more people are relying on this powerful AI chatbot to answer their most pressing questions. The latest in AI services, ChatGPT demonstrates a phenomenal leap forward in machine learning technology, but it’s not without its flaws — primarily the quality of its answers. To solve this problem, we must turn to the rising second branch of artificial intelligence: semantic reasoning.

Accurately planning a build is one of the biggest challenges facing the construction industry, particularly considering the increasing complexity of buildings and the range of disciplines required in the process (structural, electrical, landscaping, etc.). Project data is often kept in different silos and it is difficult to create an integrated and interoperable information flow across all stages of the process.

Oxford Semantic Technologies Limited (OST), the company behind the cutting-edge knowledge graph and reasoning engine, RDFox®, announces a refreshed board and new commercial leadership. Hema Patel and Ju Won Cha have joined the board while Philip Foster has joined as the company’s first Chief Commercial Officer.

RDFox v6.0 is here and it’s a big one! This release is landmarked by ground-breaking features including the new high availability setup and a total reimagining of the handling of named graphs. These features alone offer the opportunity for truly cutting-edge solutions, but when combined with the myriad of other improvements and the blistering capabilities of RDFox already in place, they allow you to step ahead of the curve.

Oxford Semantic Technologies has secured the patent, US Patent No. 11,409,698, for the application of sideways information passing to SPARQL queries. This new approach brings a significant optimization to graph database querying by drastically reducing the number of intermediate answers considered during query processing, further boosting RDFox’s lightning response.

Have you ever wondered how to find the right recipe for the ingredients you have at home? Have you felt very frustrated when you searched for recipes based on one ingredient but you didn’t have the other ingredients in the recipe at home? Oxford Semantic Technologies found a recipe for a successful semantic search using PoolParty powered by RDFox. They showed us their implementation at SEMANTiCS 2022.

The need for graph technology on the edge has exploded in recent times. Applications are endless and often practically beneficial but due to limitations in hardware and software technology, the reality has been underwhelming. With the latest from RDFox that’s all about to change! With RDFox on the edge, the knowledge you use to power your solutions, to run your business, can be condensed down into a box that fits in your palm. The strength of this little box lies, among other things, in its versatility. Now for the first time, your embedded expertise sits on a portable device, pushing your logic and reasoning to the edge. Not only does this open up mobile applications such as vehicle automation, it also creates the opportunity for reasoning in semi-connected or disconnected devices. Suddenly remote IoT use cases become viable, as do those that require consistent reliability such as medical support, or high security like the handling of private information on a mobile phone. All this and more, now a reality with RDFox.

The two branches of AI, machine learning and semantic reasoning, are often discussed as opposing forces—one a probabilistic black box and the other reliant upon provable logical inferences. In reality, their strengths can be combined to create a solution greater than the sum of its parts. While exploring RDFox, our in-house experts at Volvo Cars set out to do just that, pairing knowledge graph reasoning with object detection.

We’re pleased to announce the release of RDFox Syntax Highlighting for VS Code. This open-source extension is designed to help RDFox users work with their data, queries and rules more efficiently. The extension adds colour to keywords, operators, IRIs and literals in a number of RDF formats (Turtle, TriG, N-Triples and N-Quads), as well as Datalog rules, SPARQL scripts, and RDFox shell scripts.

The era of black-box AI systems is over. Next-generation systems will optimize the explainability and trustworthiness of the overall human-AI system, and knowledge graphs will serve as a key ingredient that makes these systems more explainable, inspectable, auditable and, ultimately, controllable.

With RDFox v5.6 comes a flurry of improvements: enhancing the Web Console to increase your efficiency when handling data; introducing a powerful, versatile way to interact with the RDFox Shell remotely; and an all-new tool to visualise semantic reasoning — the latest essential for data comprehension. Each, in its own way, will change the way you use RDFox from this day on.

AWS (Amazon Web Services) has utilized the strengths of RDFox to help in the fight against financial crime. Combining the advanced performance of the RDFox reasoning engine with the resources available in the AWS cloud, Amazon has created a fraudulent transaction chain detection solution that is both scalable and fast enough to combat financial crime.

Oxford Semantic Technologies has been granted the patent, US Patent No. 11,216,456, for SPARQL query optimization techniques that exploit sideways information passing, extending its patent holdings within the information technology space. Sideways information passing massively reduces the number of intermediate answers that need to be considered during query processing, enabling RDFox to improve query response times by orders of magnitude, from minutes to milliseconds. 

Dow Jones has incorporated a powerful new semantic technology into its product line. RDFox from Oxford Semantic Technologies is the world’s most performant knowledge graph and semantic reasoner, and will enhance the scope and capability of various Dow Jones products, from The Wall Street Journal to competitor listings in the S&P 500 index.

Formula 1 is a sport like no other. Anything can happen and the smallest of errors can mean the difference between finishing first and crashing into a wall of tires. The cost of any mistakes is further amplified by major factors outside the driver’s control, from the ever-changing weather to the quality of the car provided by the constructor. With so many variables in play, it’s no wonder that the greatest driver of all time is so fiercely debated. To make matters worse, F1 rules have changed drastically over the years and it is difficult to make a fair comparison between drivers whose careers happened decades apart. With all of this to consider, how can we give a reasonable, meaningful answer to who the best Formula 1 driver of all time is? Here at OST, we set out to help you find your answer based on whatever criteria are most important to you.

‍All usage of RDFox, whether via its built-in shell, REST API, or Java library, involves an RDFox server. In turn, whenever there is an RDFox server there is an RDFox server directory — a filesystem location containing any settings and data the user wishes to persist to the next session. To help developers, architects and operators working with RDFox to get comfortable with managing server directories, this article will describe how and when they are used, give details on the process for initializing new directories, and briefly touch on some key operational considerations.

The RDFox Web Console has long been utilized and well-loved by users — encompassing easy accessibility and human-centred visualisation. However, up until now it has been an experimental add-on. With RDFox v5.4 this all changes, seeing an end to the Console’s experimental tag. This change comes about with an increase in the Console’s functionality, now a more holistic and faithful portrayal of RDFox.

The Semantic Web represents knowledge as a semantic graph structure. Domain knowledge is represented using controlled vocabularies and ontologies that can be applied to semantically describe data. The combination of these vocabularies, ontologies and the instance data of a domain typically constitute the so-called Knowledge Graph (KG). This form of knowledge representation allows a lot of flexibility for modeling as well as combining data sets by linking graphs, which has the potential to solve enterprise data heterogeneity problems by providing a bottom-up and flexible approach.

First of all, congratulations! To win the Lovelace Medal is an incredible achievement and solidifies your place in computing history next to those who have previously received the award; world wide web inventor Sir Tim Berners-Lee, Linux creator Linus Torvalds, and information retrieval pioneer Karen Spärck Jones, to name just a few.

IT asset management solutions often frustrate users with inflexibility and a lack of scope. In this day and age there is no good reason for such shortcomings, but thankfully, they may soon be a thing of the past. By considering two IT asset management use cases built with RDFox, we step beyond the typical to unlock greater functionality. The first avenue we explore is more meaningful scenario planning, incorporating the IT system’s capacity and availability. The second is real-time warning and failure propagation through the dynamic tracking of components’ dependencies and functioning status. This is by no means all of what RDFox has to offer the sector, and instead serves to inspire further application.

Legislate is a platform that allows small businesses and landlords to easily manage their legal documents. Legislate combines technical and legal expertise to make the tedious procedure of creating and processing legal documents easier as well as skipping over legalese; making documents easy to understand for all parties involved. Legislate’s patent for knowledge graph approach to smart documents is how Legislate uses RDFox to maximise their potential.

The plots and schemes of criminals are forever evolving as they scramble to stay one step ahead of civil society, but the best efforts of the law-abiding populace make this increasingly difficult. The unscrupulous have been forced to broaden their efforts to evade capture, putting more time and energy into keeping their newly acquired wealth beyond just stealing it in the first place. Faced with this predicament, they often turn to money laundering — the process by which illegal ‘dirty’ money is ‘cleaned’, allowing the holder to spend it freely.

This article will introduce one of the world’s most popular RDF data sources and how RDFox can use it to offer a rich and responsive experience for web-based applications by reducing load times to minutes or seconds, and querying in a matter of milliseconds — orders of magnitude faster than the dedicated query service.

Autonomy is a central pillar of the fourth industrial revolution. However, fully autonomous vehicles remain an area of development. Autonomous vehicles operate in a high-risk environment — if something goes wrong, lives could be at risk. To function effectively, the system needs human-like reasoning and decision-making capabilities.

RDFox v5.2.0 is here and while a smaller update, carries no less weight than those that came before. In fact, it marks a significant shift towards the wider adoption of graph technology — something we’d all like to see. On-device projects are now a reality with the new support for Linux on ARM CPUs, creating the opportunity for use in previously inaccessible ventures. Everything from autonomous cars to medical equipment now falls within the purview of RDFox — and that’s only the beginning of what it has to offer. You can watch our animated video ‘Reasoning on the Road’ for a glimpse of what can be achieved, or read our accompanying white paper ‘Reasonable Vehicles Rule the Road’ for further details.

Using the shell is the simplest way to get started with RDFox, but having to manually input each command every time you want to use it can become a hassle. Thankfully, the RDFox shell can process text files with pre-written commands and execute those commands just as if you typed them one by one. This can save you a lot of time and make the process of restarting your work much simpler.

In our last article, we spoke to Vaishali Raghvani, a knowledge engineer from Legislate Technologies, on her experience of getting started with RDFox. Since then, Legislate has kindly shared their ‘cheat sheet’ which stores their tips and tricks for getting to grips with RDFox quickly… We believe this cheat sheet will be useful for other RDFox beginners, so are sharing it with our community.

Getting used to a new product can be a pain. At Oxford Semantic Technologies we know this can put people off moving to something new. That’s why we care about our new users’ experiences. Although RDFox is cutting-edge, we want it to be accessible and easy to use. This short Q&A session tapped into the experiences of Legislate’s knowledge engineer, Vaishali Raghvani, as she began her journey with RDFox at the beginning of 2021. Legislate’s knowledge graph streamlines contract drafting and management for its users. It prefills the contract with domain expertise and aggregates statistics from and between contracts effortlessly.

As engineering companies move towards Industry 4.0, the question of how to leverage the vast amounts of knowledge that they currently store in non-uniform formats must be addressed. In many organisations, critical information is scattered between excel spreadsheets and relational databases (RDBs), which require skilled IT experts to extract the knowledge. Unification of this data is critical for the creation of data-driven production systems and to ensure that maintenance strategies are created with all the relevant data.

In RDFox v5.0.0, we have changed the way the implementation of OWL works, as well as a bunch of other things. In this article, we will outline the difference between the two approaches, and why the new one is better. If you’d like to follow along, you can find the data and ontologies in this GitHub repository.

Every year, credit card fraud causes massive losses for banks, businesses and their customers, and prevention is a constant race between programmers and criminals. With the rise in popularity of online shopping, we have seen a steep increase in so-called “card-not-present” fraud, where credit card data (including security code) is stolen and used without the physical card ever leaving the owner’s wallet.

Along with other exciting additions (like property paths and SHACL support), version 5 of RDFox brings with it a web console refresh that will make working with RDFox even easier than before. With the ability to create data stores and import files (data, axioms, rules) straight from the browser, you can get started with RDFox in no time at all. The purpose of this article is to show you exactly how to achieve that.

It’s been a busy few months at Oxford Semantic Technologies, with the launch of our ‘Reasonable Vehicles’ video, growth of our partner network and now, the release of RDFox Version 5! Along with performance enhancements, the latest version of RDFox brings a variety of new capabilities and updates. RDFox now supports SHACL, property paths and we include a release dedicated to the new Apple M1 ARM chip. These developments mean RDFox now has complete support for SPARQL 1.1. The console has new slick features and our support for OWL and Solr has been updated for increased usability.

When the Food and Drug Administration (FDA) rejected the cancer drug Erbitux in 2001, share price in the pharmaceutical company ImClone dropped significantly. Investors were severely impacted, while those close to Samuel Waksal, the CEO, appeared to benefit. An SEC investigation uncovered that Waksal advised various executives, friends and family to sell their stock, knowing the drug was going to fail the assessments. For this action, Waksal faced seven years in prison and $4.3 million in fines.

We are delighted to announce our partnership with DataLens, and the general availability of RDFox on the AWS Marketplace. DataLens and RDFox have teamed up to provide the world’s fastest RDF Knowledge Graph & Reasoning Engine alongside rapid data pipeline deployment to deliver the most comprehensive knowledge graph inferencing available on the AWS Marketplace. With this service, you can now connect SQL, CSV, XML, JSON, REST and any unstructured data to RDFox and take advantage of the world-class reasoning and performance all in one place. No contracts, no complex setup, just launch and use on-demand.

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.

My research over the last fifteen years has focused on Knowledge Representation and Reasoning (KRR) — an area of Artificial Intelligence and Computer Science concerned with the representation of human knowledge in a symbolic, machine-interpretable way, and the effective manipulation by computer programs of this knowledge in combination with data. For example, KRR studies the way to represent in a format that a computer can understand statements such as ‘every playwright is an author’, and ‘if a person is born in a town located in a given country, then this is the person’s country of birth’. Once such information has been unambiguously represented in a suitable language (usually a kind of formal logic), KRR systems can then be used to process data in a more intelligent way.

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.

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.

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. Inconsistencies are found and fixed, making the knowledge graph more accurate.

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. This use case nicely demonstrates the power of RDFox and its applicability to modern day applications.

One of the many benefits of RDF graph databases is their structure. They overcome the flexibility limitations of relational databases, as data is stored as richly connected entities. Datasets with many relationships between data points can be effectively stored, queried over, and now visualised in Version 4.0.0 of RDFox. This increased functionality is great for RDFox users, as they can now visualise, present and explore their data in a user-friendly way.

Oxford Semantic Technologies Limited, the developers of RDFox, a high-performance knowledge graph and semantic reasoning engine, are excited to announce the successful grant of a United States patent for their advanced semantic reasoning technology. This patent will allow Oxford Semantic Technologies to lead the field, with correct, fast and robust semantic reasoning. RDFox can be applied in various industries and use cases, and its unique capabilities offer clients significant benefits over competitors, a result of the novel design and concepts, which were developed and validated in peer-reviewed research at the University of Oxford.

In 2012, the BBC famously used linked data to support coverage of the London Olympics on its website, app, and interactive video player. They have continued to champion the benefits of semantic technologies to this day. Coincidentally, as I started to write this, the Tokyo 2020 Olympics were planned to be in full swing. Therefore only one topic can so aptly be chosen for this article: The Olympics.

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.

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.

As of today, users have the option to run RDFox in Docker using official images from Oxford Semantic Technologies. In this article I will describe how to deploy these images to a multi-zone Kubernetes cluster to achieve a high-availability, read-only configuration. RDFox is a high-performance knowledge graph and semantic reasoning engine. It is an in-memory solution, which allows flexible incremental addition and retraction of data, and incremental reasoning. It is mathematically validated at the University of Oxford. Since v3, RDFox also offers the ability to incrementally save updates to persistent storage for easier restarts.

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.

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. My co-investor and I decided we would back the initial round and I ended up chairing the company.

This article will show you how to very quickly set up your very own knowledge graph database and semantic reasoning engine. By the end you'll be able to setup RDFox locally, import triples and rules, and run queries. To follow along, request an RDFox license and download the latest release. Once you’ve received your license key, place it in a directory with your RDFox download.

Following discussions with Professor Ian Horrocks and Professor Boris Motik, the third article in the series places Professor Bernardo Cuenca Grau on the hot seat. 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.

Originally from Croatia, Boris undertook an undergraduate and a master’s degree at the University of Zagreb. Inspired by authors such as David Poole, Boris entered the space of semantic technologies fascinated with automated detection.“I was fascinated by two things: that it looked so difficult mathematically; and the idea that you could structure knowledge symbolically” During his academic years in Croatia, Boris was already contemplating ways to improve the systems he was using, for example AltaVista:

I had a slightly non-standard career for an academic. I did a Bachelor degree at the University of Manchester in computer science. I then worked there as a researcher for a few years in desktop publishing, which then got commercialised and I went off in a startup. I worked with the startup for several years before deciding that I wanted to do a PhD. I wanted to really get into some deeper research where I could spend a bit longer thinking about things.

This article aims to provide a basic introduction to SPARQL with RDFox. It will explain what SPARQL is, why RDFox uses SPARQL, how to write SPARQL queries, how to monitor SPARQL queries with RDFox, and other SPARQL related resources.

RDFox harnesses the ability to serve extensions to built-in functionality, and Faceted Search takes full advantage of this. Faceted Search enables the navigation of graph data via a successive refinement approach, which is similar in principle to e-commerce websites, where the available products may be filtered by applying a series of constraints. For example, on a website selling televisions the variety of televisions available for purchase may be refined down to a small set by stipulating the screen-size, then the manufacturer, and then a feature such as 4K resolution.

With RDFox v3 came Datalog constraints and SWRL Support. We have asked Professor Bernardo Cuenca Grau, a founder of Oxford Semantic Technologies and Professor at the University of Oxford in the Computer Science department, about SWRL’s origins and the benefits it will bring to RDFox. Read more about Datalog constraints in our previous article, and don't forget to check out the latest from RDFox v5.6!

Wine Sommeliers at upmarket restaurants provide in-depth knowledge on flavour, notes and the perfect food pairing. Online wine merchants could provide a similar service to their customers with new AI technology. Imagine an online AI Wine Sommelier to help you pick out the perfect bottle… Searching for a great bottle of wine online should be fairly straightforward, but it can be disappointing when the ones you love are out of stock, or simply out of your budget. In these situations, the ‘next best’ wines are recommended, but flavours can range widely, even within a region or grape variety. This can leave the customer disappointed if the alternative recommendations don’t quite meet their expectations.

Scripts can help you save time and avoid duplication of code for setting up data stores, and executing your rules and queries. This article will explain what a script is, how to write scripts, when to use scripts, how to use scripts with persistence, how to structure your workspace, and finally how use scripts within scripts, to set up your data source.

Datalog Constraints leverage RDFox’s unique incremental reasoning capabilities to bring the expressiveness of RDFox’s rule language to the problem of constraining data store content. In so doing, they provide application developers a means to ensure that their data stores remain valid and focused on their application domain, without having to write any external code.

Across various industries and business models, companies seek to assess compatibility; for example, in regard to industrial configuration management, terms in contracts, points of a supply chain, buyers and suppliers, and so on. Whether this process is in relation to assemblies, production or data analysis, it can require checking millions of combinations to assess whether components fit together and if they meet certain requirements. The large volumes of data, complex process and issue of scalability mean that traditional approaches are inefficient.

‍Rules are the conduit for reasoning. They offer an expressive way to process and manipulate knowledge graphs and help you to write queries by bringing the intelligence layer closer to the data. It can be difficult to know where to start when creating your own, but it really doesn’t have to be that way. The power of rules is derived from their simplicity and structured logic, properties that lend themselves to easy use—you just have to get over the initial teething pains. That’s what we hope to do for you here. We aim to provide you with some of the best practices of rule writing, that we’re sure will give you the boost in confidence you need to jumpstart your reasoning.

The search feature of a website requires the creation of an index of the searchable fields. Creating an index requires adding some structure to the fields so that they can be easily categorised. The categories then enable the search results to be filtered. For example, e-commerce websites provide a structure to help customers browse by category. However, the searchable categories tend to be fixed so the search results can’t be filtered with elements beyond the ones initially provided.

Although May 2020 will be widely remembered for the Coronavirus pandemic and resultant lockdown, it also marks the launch of RDFox Version 3. So, to capture the excitement at Oxford Semantic Technologies, we asked a few of our clients, and the team, about their favourite features…

A knowledge graph is composed of a graph database to store the data and a reasoning layer to interpret and manipulate the data. RDFox is an in-memory, Resource Description Framework (RDF) triple store.RDFox stores data in triples. A triple represents three linked data pieces, i.e. subject-predicate-object, often referred to as nodes (data point i.e. subject or object) and edges (the relationship between them).

An application’s logic for processing and manipulating data is typically controlled by an application or logic layer which sits between the database and the presentation. This formulates the requests which must then comply with the database’s structure. The following diagram represents the classic three tier architecture on which much of the world’s IT systems are built.

Machine learning algorithms are now synonymous with finding patterns in data but not all patterns are suitable for statistics based data-driven techniques, for example when these patterns don’t have explicitly labelled targets to learn from. In some cases, these patterns can be expressed precisely as a rule. Reasoning is the process of matching rule-based patterns or verifying that they don’t exist in a graph. Because these patterns are found with deductive logic they can be found more efficiently and interpreted more easily than Machine Learning patterns which are induced from the data.

RDFox ingests data in the open standard RDF-triple format which can be imported, written from scratch, or easily converted to from SQL or CSV sources. Once triples have been imported, they are available for querying and reasoning. Additionally, triples can be scheduled for incremental addition and incremental deletion, which makes them available for incremental reasoning. Thus, triples can be added programmatically, read from files of certain formats, or extracted from an OWL 2 DL ontology.

RDFox is the high-performance in-memory knowledge graph and semantic reasoner developed by Oxford Semantic Technologies. This short article will help you understand the key concepts behind RDFox and when to use them in your applications.

Relational databases, despite their name, are not very good at expressing relationships in data because they impose a storage structure that does not favour connections. Graph databases, despite their name, look nothing like a graph and offer greater flexibility for expressing relationships between data points. These relationships can be of any type and are represented as edges between the data points. Storing relationships in a graph is interesting but not very useful for extracting knowledge and insights. A knowledge graph is therefore composed of a graph database to store the data and a semantic reasoning layer to search and materialise patterns in the data.

Samsung’s investment will enable Oxford Semantics to bring its powerful data integration and reasoning engine to more clients. Samsung Ventures has led an investment round that raised over £3 million for Oxford Semantic Technologies Limited, an Oxford University spinout company founded by pioneers in the fast-growing field of semantic technology. The investment round was also supported by the University of Oxford and Oxford Sciences Innovation PLC.