In the fast-paced technological landscape, where terms like AI, LLM, ML, and GenAI are commonplace, businesses are continuously seeking innovative technological solutions to get ahead of the hype. With the advent of large language models like Bard, GPT-4, Grok, and many more, the focus has shifted towards consistently producing the most accurate and reliable answers by any generative AI technology.
One of the solutions that have become increasingly popular to address the hallucination problems of deep learning models, are knowledge graphs. This is no surprise with the capabilities of a knowledge graph to structure data in an intuitive way, offering users to see the connections between their data at a glance. However, to truly maximise the potential of knowledge graphs and make them indispensable tools for decision-makers, we need to go one step further and include semantic reasoning, or what is known as Knowledge Representation and Reasoning.
This article delves into how to automate accurate human-like reasoning within knowledge graphs with Knowledge Representation and Reasoning (KRR).
KRR or Knowledge Representation & Reasoning is a type of artificial intelligence designed to enable machines to reason like humans on an enterprise-wide scale and with absolute precision every time. Its advantage lies in ensuring the answers generated are firmly grounded in facts, eliminating the risk of making up any information. With reasoning embedded in machines, intelligent inferences from information that is explicitly and implicitly stated can be made. This can be carried out by adding a semantic reasoning layer to knowledge graphs.
Semantic reasoning enables systems to infer new facts by applying rules. These rules serve as statements that adds meaning to entities within a database or data lake, allowing for new relationships between datapoints to be formed. This capability means that experts within an organisation can easily integrate their domain expertise into existing databases, facilitating the natural connections and inferences humans would normally make to solve a complex problem. This allows enterprises to start democratising the knowledge they hold.
Imagine an online streaming service that wants to enhance its recommendation system to suggest shows that align more closely with individual user preferences. Traditional approaches solely relying on watch history and crowd behaviour may miss nuanced connections between shows that a user might enjoy. By implementing a rule that states that if a user has consistently shown interest in mystery shows and has positively reviewed shows with complex plots, the streaming platform can more precisely recommend other shows with similar characteristics, giving a more personalised experience.
Looking beyond straightforward watch history and ratings, KRR technology considers the implicit connections between user preferences for mystery shows and an interest in intricate plotlines. An inference is automatically made that the user might also enjoy shows from the psychological thriller genre, even if they haven't explicitly watched or rated such shows before. This takes AI recommendations to the next level.
One major challenge faced with any recommendation system is what happens when the underlying data changes. In the context of streaming services, what if new shows are added or existing shows are no longer available? Traditionally this involves complex and time-consuming updates, such as recompiling the underlying data and often reloading and restarting the entire system. This can be inconvenient to all parties, but this is where incremental reasoning comes into play.
Incremental reasoning allows for changes to be implemented by only applying the change based on the data relevant to a specific rule. This ensures that these changes are reflected instantaneously, without any downtime or service interruption, saving valuable time and resources for businesses looking to maximise their AI applications and customer experience.
In another example, imagine an e-commerce platform that employs a knowledge graph to manage product information. With incremental reasoning, updates to product specifications, pricing, or inventory levels can be seamlessly incorporated without disrupting the entire system. This not only enhances operational efficiency but also ensures that the platform stays up-to-date with the latest market trends.
Backed by decades of research from Oxford University, experts at RDFox, the only enterprise grade AI technology explicitly designed with KRR in mind, have found a way to implement incremental reasoning within a knowledge graph—enabling real-time adaptability and responsiveness to dynamic data changes.
To learn more about KRR in industry-specific applications, read our blog posts on making smarter retail assistants, digital twins, self-driving cars, and more!
To see KRR in action, request a RDFox demo or try out a 30-day free trial of RDFox.
The team behind Oxford Semantic Technologies started working on RDFox in 2011 at the Computer Science Department of the University of Oxford with the conviction that flexible and high-performance reasoning was a possibility for data-intensive applications without jeopardising the correctness of the results. RDFox is the first market-ready knowledge graph designed from the ground up with reasoning in mind. Oxford Semantic Technologies is a spin-out of the University of Oxford and is backed by leading investors including Samsung Venture Investment Corporation (SVIC), Oxford Sciences Enterprises (OSE) and Oxford University Innovation (OUI).
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