Unstructured and structured data represents a treasure trove of insights for businesses but most of it is dispersed across the organisation and difficult to access or understand via existing data processing systems.

    Knowledge graphs (KGs) and the maps of ontology (or meaning) they create have emerged as a solution that can easily surface these insights. They offer a fast and easy way to search or query your data, visualising complex data relationships as graph structures and making it easy to see how different pieces of information are interconnected.

    But for all of the advantages of KGs, there are a few (manageable) downsides. Let’s dig in, bust a few myths and figure out how to get the most out of Knowledge Graphs.

    1.  Are Knowledge Graphs actually easy to build and deploy?

    Knowledge Graphs are being increasingly overhyped and oversold as a universal, easy to adopt solution for all Decision Intelligence and automation applications, leading to unrealistic expectations and misapplication.

    Whether created manually by domain experts or generated automatically using semantic modelling algorithms or LLMs, implementing KGs effectively requires significant effort, expertise and a clear understanding of their appropriate use cases within your overall tech stack.

    2.  Once built, they’re also complex and difficult to maintain.

    Maintaining large Knowledge Graphs can be a daunting task. Non-trivial graphs with endless nodes and complex interconnections can become difficult to manage, with their lack of transparency making them difficult to understand and modify over time.

    3.  Outcomes aren’t ready-made: they require interpreting and verifying.

    There are few easy answers in life, and Knowledge Graphs are no different. Understanding the outcomes generated by a Knowledge Graph reasoning engine is challenging without a comprehensive grasp of the underlying schema (or organisational structure) and inner workings of a proprietary reasoning engine. The reasoning engine interprets your KG’s schema to perform knowledge inference, applying complex logic and algorithms to derive conclusions from the data.

    Without knowledge of the schema's detailed organisation and the specific rules and processes employed by the reasoning engine, the rationale behind the inferred results can be opaque, making it difficult for users to interpret or verify the outcomes accurately.

    4.  Knowledge Graphs struggle with complex decision-making tasks.

    KGs are primarily data-centric and do not naturally handle decision-making logic or workflow problems well. For instance, workflows often require a specific sequence of operations and state management, which KGs are not inherently designed to support. Decision trees and other decision-centric models are typically better suited for these tasks because they are explicitly designed to handle sequential and conditional logic, providing a clear structure and defined paths for decision-making.

    5. They won’t solve your optimisation problems.

    Optimisation problems require algorithms that can search for and identify the best solutions from a set of possible options. Knowledge Graphs lack built-in optimisation algorithms and mechanisms for representing and enforcing complex constraints. This makes them unsuited for tasks that involve finding optimal solutions within constrained environments.


    How Composite AI maximizes the value of Knowledge Graphs

    Despite what the hype cycle may suggest, you can’t rely on any single AI technology to solve your decision intelligence issues.

    Transcending the limitations of any single tool, Composite AI offers a different approach, combining the unique strengths of different AI technologies to arrive at a more comprehensive and effective solution.

    Composite AI comprises the integration and deployment of various AI technologies into a single platform, to automate and augment complex real-world decision intelligence and automation problems.

    For tasks involving complex decision-making, workflow management and optimisation, alternative AI technologies can be much more effective than Knowledge Graphs.

    Let’s explore a few examples.

    • Enhanced Decision-Making. By combining the strengths of decision trees, machine learning models and a host of other AI techniques, Composite AI can handle complex decision-making tasks more effectively. Decision trees offer a clear and straightforward way to implement decision-making logic, while machine learning models provide predictive analytics and pattern recognition capabilities.

    • Improved Workflow Management. Composite AI can integrate workflow management tools and techniques, allowing for better handling of sequential and state-dependent tasks. This integration ensures that workflows are executed in the correct order and state transitions are managed efficiently.
    • Optimisation Capabilities. Composite AI incorporates optimisation algorithms that can search for and identify the best solutions from a set of possibilities. This allows Composite AI to solve optimisation problems that involve complex constraints and objective functions.
    • Intelligent Dialogue Systems. While KGs lack the complex behavioral logic to manage intelligent dialogue interactions and responses based on user inputs, Composite AI can support these systems by combining natural language processing, Decision Trees and Constraint-based Reasoning. These systems can manage complex real-time interactions and user inputs, providing more dynamic and responsive user interfaces.
    • Scalability and Flexibility. Composite AI offers greater scalability and flexibility by allowing organisations to integrate various AI technologies as needed. This adaptability ensures that AI solutions can evolve with changing requirements and technological advancements.

    But that’s not all. In a Composite AI infrastructure, KGs can improve your other AI tools, too. For instance, they can enhance machine learning algorithms by providing structured data to improve the accuracy and relevance of AI models, reducing hallucinations and improving natural language understanding by providing the necessary context.

    Too often, AI technologies are pitted against each other in a bid for market supremacy or media hype.

    Composite AI takes a different approach, recognising the strengths of each and bringing them together into one platform.

    We call that platform viabl.ai.

    Get in touch with us to discuss how with viabl.ai you can get the most from your data with Composite AI.