Combining semantic rule-based methods with machine learning are the best hybrid methods for optimal contextualized results. A pure machine learning solution is rarely going to understand semantics of data, and will always return some form of confidence score as an approximation with no exact results. A pure semantic method will provide exact results based on inference and reasoning constraints. A semantic solution is also logically testable through standard programmatic methods. A machine learning solution can at best be evaluated on approximates for which formal explainability and interpretability methods are required in the process for compliance. Semantics can take the form of ontological representations like knowledge graphs and commonsense reasoning methods. When both approaches are combined there is an increase in connected context and semantics which is beneficial for formal artificial intelligence driven systems. It also provides for better transfer learning and a way of managing governance. Such methods when combined add significant accessibility value to business in form of natural language interpretability, integration, feature engineering from standards compliance, and for human-computer interaction interfaces. In essence, data is transformed into knowledge and information through the enrichment of machine-interpretable context that can grow through the mechanisms of self-learning, self-experience, and inevitably develop self-awareness about the environment. The semantic aspects act as a simulated form of associated memories that are available for forming new data associations and relationships. Such memories are formed through aspects of persistence in a knowledge graph that could be treated as in-memory cache for short-term processing and storage for long-term processing. In process, the machine learning and semantic methods feed of each other to increase in learnability and comprehension about the domain targets within the aspects of an open-world. The entire world wide web is based on the aspects of semantic resources making the internet accessible for browsing, searching, and findability. Metadata is in every technological software and hardware in use today. However, such metadata requires semantic enrichment to enable machine-interpretability which can be achieved through semantic standards. In fact, many programming languages are built using similar theoretical underpinnings of compilers, interpreters, parsers, semantics, and syntax. Many of these methods for decades have shown to be sufficiently plausible in industrial business use cases. In many respects, these are similar aspects, albeit in simpler abstractions, to human intelligence processes that are far more intricate and complex in nature. Rarely, do humans think in statistics for pattern matching and recognition where many of such processes are driven through semantic associations and are reinforced through experience. A pure statistical machine is always going to be fairly unsure about the world if based on approximations. The semantics will give it an edge to formulate meaningful associations from the world through domain relevant experiences which it is then able to interpret and analyze in context.
