Hate Wasting Good Data? Reclaim Predictive Information with Knowledge Graphs
People & Blogs
Introduction
In today’s data-driven world, the quest for better predictions often leads us to lament, "If only I had better data!" However, the reality is that we already possess more data than we might realize; the challenge often lies in accessing and utilizing this data effectively within our machine learning (ML) pipelines. The key to enhancing our predictive capabilities may be as simple as improving our data or effectively encoding knowledge into our systems.
To improve our models, we can either enhance the quality of our data, a concept closely associated with data-centric AI, or we can embed our domain knowledge into the model itself. Common methods for improving data quality include data cleansing and sampling, while knowledge encoding might take the form of feature engineering, manual labeling, weak supervision, or the generation of synthetic data.
However, the first step in creating a predictive model often leads to a detrimental practice. As we create a feature matrix from our raw data, we inadvertently flatten crucial structures, losing valuable insights derived from our organizational data. This process strips away hierarchy, meaning, and the important relationships between various data points. Consequently, we risk misrepresenting the dependencies between entities, which are vital for accurate predictions.
Consider an example from the retail space: if we want to recommend bicycle components to an online shopper, it is essential to understand how the purchase relates to previous acquisitions—be it a high-end bike, basic frame, or associated services. Any loss of relational context can hinder our prediction models. In machine learning, we strive to create representations of real-world systems and make informed predictions; therefore, stripping away components can severely limit the model's depth and accuracy.
Many organizations have vast reserves of corporate data that can be harnessed for machine learning. However, successfully tapping into this data requires integrating business logic and leveraging different machine learning techniques and breakthroughs, particularly those seen in image and text processing. By valuing and incorporating relationships within the data, such as those illustrating buying behaviors, we can optimize our predictive models.
Recent developments in semantic layers and models can help map data to its business meaning, capturing the relationships and logic surrounding the data. Organizations can streamline application development and improve intelligence within data applications by utilizing these mappings. By leveraging relational knowledge graphs, we can enhance our predictive analytics by feeding encoded knowledge into platforms like Snorkel.
A relational knowledge graph serves as a model of concepts, their relationships, and associated logic, all framed in an executable manner. A notable illustration of this approach comes from a Google case study involving Snorkel. The integration of a knowledge graph as an organizational resource allowed for the effective capture of information, improving the outcomes of their machine learning initiatives.
Using relational knowledge graphs extends beyond mere relationship mapping; they allow for the incorporation of heuristics, business rules, and semantic organization. At Relational AI, we strive to unify rich corporate data with domain knowledge in a way that is executable, permitting complex analytics, graph analytics, reasoning, and knowledge sharing.
The marriage of relational knowledge graphs with modern data stacks illustrates our path toward enhancing intelligence in our work. If you find these concepts intriguing, don’t hesitate to reach out. I’m always excited to discuss graphs, knowledge graphs, or anything related to these innovative technologies. For those eager to explore relational knowledge graphs further, sign up for our newsletter at relational.ai to gain early access to this cutting-edge technology.
Keyword
- Predictive data
- Knowledge graphs
- Data-centric AI
- Feature engineering
- Machine learning
- Corporate data
- Relationships
- Semantic models
- Analytics
FAQ
Q: What is a knowledge graph?
A: A knowledge graph is a model that represents concepts, their relationships, and associated logic in a structured and executable way.
Q: How can relational knowledge graphs improve machine learning outcomes?
A: They capture important relationships and domain knowledge that can enhance predictive analytics and result in more accurate predictions.
Q: What is data-centric AI?
A: Data-centric AI focuses on improving the quality and richness of data to enhance the effectiveness of machine learning models.
Q: Why is preserving relationships in data important for predictive models?
A: Relationships provide context and dependencies that are often critical for accurate predictions, reflecting the interconnectedness of entities in the real world.
Q: What tools can be used with relational knowledge graphs?
A: Platforms like Snorkel can be utilized to integrate relational knowledge graphs into machine learning workflows for improved results.