Identifying, understanding, and organizing data around entities is crucial for businesses to navigate and leverage their unstructured data. Entity extraction and entity linking act as the foundation for what we at Agolo describe as entity intelligence.
Traditionally, companies have only extracted named entities of standard popular types, such as people, organizations, locations, and products. However, entities in the business world encompass much more than that. Here’s how entity intelligence can enable businesses to extract and link all of the entities relevant to their day-to-day operations.
Companies have used many libraries and tools to extract a variety of default entity types with decent accuracy. Named Entity Recognition (NER) has been well-explored and refined over the years.
However, the real challenge lies not in extracting common entity types but in the nuanced, specific needs of businesses. These require more than what out-of-the-box NER solutions offer.
Moreover, NER alone is only useful when discovering which entities exist in your data. It can’t help you build more intelligence around these entities.
Many businesses today operate in specialized domains or have unique analytical needs that generic NER tools can’t fully satisfy. These organizations often deal with custom or domain-specific entity types, such as industry-specific jargon, product names, technical terms, etc. Traditional NER systems often overlook or misclassify these.
The inability to accurately identify and utilize these custom entities has historically limited the potential of downstream analytics. That constrains businesses to surface-level insights and prevents them from unlocking the full value of their data.
Thanks to state-of-the-art Large Language Models (LLMs), we can now extract a wide variety of custom entities specific to any business or domain, resolve them accurately, and link each mention to its actual identity.
For example, a mention of a Galaxy Smartphone in a support call transcript could be extracted as an entity of type “Smartphone.” Based on the call’s context, the model can link it to the identity "Galaxy S24.”
The true innovation of this approach lies not just in the extraction and linking of entities. It lies in how we can use these entities to generate real intelligence. In reality, the essence of valuable insights lies in understanding the entities in their context. This includes how they relate to one another and other elements within the data, such as topics, issues, problems, etc.
With this approach, you can extract not only entities, but also:
This comprehensive extraction forms the foundation of Agolo’s entity graph. An entity graph is a sophisticated form of a knowledge graph that, while centered around entities, is not limited to them.
This entity graph is a dynamic, interconnected web of information that becomes a source of intelligence. By offering a high-level view of the data, it enables a wide range of data analytics.
Enter the advent of entity intelligence: a paradigm shift in how organizations approach data analysis and decision-making. By integrating advanced techniques such as entity extraction, disambiguation, linking, and the construction of comprehensive entity graphs, businesses can now unlock a level of insight previously unimaginable.
This shift isn’t just technical. It fundamentally alters the landscape of strategic decision-making. It enables moving from reactive to proactive stances in various operational and market challenges.
Entity intelligence facilitates a more nuanced understanding of data by capturing the complex web of relationships and attributes surrounding each entity. This understanding turns basic data into a detailed network of connected information. In this network, each link is a clue that could lead to new discoveries.
The dynamic nature of entity graphs, enriched with real-time data, empowers businesses to not only analyze the past and the present but also anticipate the future. These graphs constantly update with new data from customer interactions or industry reports This keeps them evolving and offers a current model of the entities they represent.
This capability allows businesses to query their data in near real-time. They can pose complex questions and receive insights that inform strategic decisions, from product development to market positioning and beyond.
Entity intelligence doesn’t just change how we analyze data. It revolutionizes the very questions we can ask of it, enabling a depth of analysis that was previously impossible.
Entity intelligence can enable the instant identification of recurring issues, their associated products, and even the sentiment of customer discussions around them. All organizations need to do is ask the entity graph the right questions (or more accurately, perform the right query).
This immediate access to organized, actionable information significantly reduces time to resolution. That, in turn, allows companies to address issues before they escalate. This enhances customer satisfaction and fosters loyalty.
Auto warranty claims involve a dense array of parts, problems, and customer interactions. Entity intelligence can streamline the claims process, identify patterns in faults or failures, and even predict future areas of concern based on historical data trends.
Traditional data analysis methods can only scratch the surface of the wealth of information hidden in unstructured documents such as technical support call transcripts. By contrast, Agolo's technology can revolutionize this process.
Agolo accomplishes this through a three-step process:
This approach enables the identification of specific issues, and the context in which problems arise. This transforms raw transcripts into a rich, navigable map of interconnected data points.
Conventional methods can show you the frequency of certain issues. Agolo goes a step further, showing you the nuanced conditions under which they occur.
Additionally, Agolo offers the capability of seeding structured data into its entity graphs. Agolo’s entity intelligence capabilities can create an entity graph from scratch out of unstructured text. However, organizations likely don’t want to ignore the years’ worth of intelligence they already have in the form of structured data.
The integration of structured data into Agolo's entity graphs offers companies a formidable tool to amplify their analytical capabilities and insights. Using Agolo, they can seed their existing knowledge - product details, customer databases, market research - into the entity graph.
Using this approach, organizations can ensure that every piece of extracted information finds its right place within a comprehensive, interconnected framework. This melding of structured and unstructured data improves the entity-linking process, enhancing the accuracy and depth of the insights generated. Furthermore, it helps bridge any gaps in the information, ensuring a holistic view of the data landscape.
For technical support teams - especially in high-volume, high-velocity environments like those handling inquiries and issues for popular products - this integration between unstructured and structured business intelligence can be transformative.
For instance, by seeding product specifications, user guides, and FAQs into the entity graph, every customer interaction can be immediately contextualized. This means that, when a call transcript mentions a specific feature or issue, the entity graph identifies and links this mention to the relevant part of the product's knowledge base. However, it also highlights related issues or solutions that might not have been immediately apparent.
Leveraging this knowledge. Agolo can pinpoint , such as specific features causing user confusion or glitches under unique usage scenarios. This approach allows customer service representatives to offer more informed, accurate, and comprehensive assistance, improving resolution times and customer satisfaction.
This depth of insight enables support centers to address current concerns more effectively. It also enables them to anticipate future inquiries. This improves customer service proactively, enhancing overall user satisfaction.
In the realm of automotive warranty claims, the stakes are even higher. They include not just customer satisfaction but also safety and regulatory compliance. Here, the benefits of integrating structured data into entity graphs are even more pronounced.
Consider the scenario of a new car model experiencing safety-critical issues under specific conditions, such as cold weather or heavy load. Using Agolo, manufacturers can incorporate detailed product designs, historical warranty claim data, and environmental condition parameters into the entity graph. With this integration, they can quickly identify not only the emerging pattern of claims but also correlate them with specific vehicle components and operating conditions.
This integrated analysis can uncover latent defects or design oversights that might have remained obscured, enabling manufacturers to take corrective action more swiftly. Moreover, it allows for targeted communication with vehicle owners, potentially averting safety incidents and reinforcing brand trust.
In other words, Agolo’s approach doesn't just solve problems - it anticipates them. It offers businesses a proactive strategy for enhancing customer satisfaction and ensuring product integrity.
The strategic incorporation of structured data into entity graphs unlocks a new dimension of analytical power for businesses. It ensures that the vast amounts of unstructured data generated through customer interactions and warranty claims are not only accurately interpreted but are also enriched with the context and depth provided by existing knowledge bases.
This synergy between structured and unstructured data within the entity graph elevates the precision of insights. Furthermore, it empowers companies to act with greater confidence and foresight, addressing issues proactively and enhancing their customer and product strategies.
Entity Intelligence is the missing link in AI