From Keywords to Context: Semantic Search for Enhanced Conversational Analytics

Imagine asking a search engine, “I’m trying to protect my customers’ data in the cloud. What are some best practices?” Instead of just showing a bunch of articles with those exact words, the response generated contains articles and reports that perfectly align with the query. This is the promise of semantic search: it goes beyond matching keywords to grasp the context of a query, much like how a human would comprehend and respond. It’s like having a personal assistant who knows the business and can find the most relevant information.

With technology changing so fast and AI-powered applications being at the forefront, simple search engines that just match keywords aren’t good enough anymore. People want meaningful answers that connect with their queries and not just a list of websites with matching words. Semantic search is like having a conversation with a computer. It transforms the experience by focusing on context rather than keywords, which makes it easier for AI to understand and respond to user’s question.

Semantic search analyzes user query or prompt and provides precise results based on the intent. It enhances the capabilities of conversational analytics in a way that keyword search cannot. It is this shift—from words to meaning—that makes semantic search a cornerstone of modern AI applications, especially in domains where getting the right answer matters as much as asking the right question.

Keyword-Based Search vs. Semantic Search

Keyword-based search has been the default method for retrieving information for a long time. It matches the words in a search query with exact or similar words in a database and responds with options that contain those specific terms. As the focus solely relies on the presence of words without understanding the meaning of the question, this approach is inherently limited.

For example, when searching for the term ‘growth in e-commerce’ using keyword-based search, the results are a bunch of articles talking about e-commerce and how it’s growing.

Semantic search, on the other hand, brings precision to this process by understanding relationships between words. It interprets the query, recognizes context and returns results based on the meaning. It can also differentiate between various meanings of the same word and filter results that align more closely with what the user is asking for, instead of just listing documents that contain the same terms.

For the same example as above, the output through semantic search would also highlight the factors that are making e-commerce bigger and better, understanding the “why” component of the query automatically. It ensures that users get answers tailored to their intent, not just a list of links that happens to contain a few matching words.

Below is a comparative table highlighting the difference between these two:

Feature	Keyword-Based Search	Semantic Search
Focus	Exact word matches	Context and meaning
Understanding	Limited	Deep
Results	Often literal	More relevant
Complexity	Struggles with complex queries	Handles complex queries well
Efficiency	Faster	May require more processing time

How NLP Enhances Semantic Search

Semantic search operates on the basis of Natural Language Processing (NLP) and Machine Learning (ML). NLP is a branch of AI that enables machines to understand, interpret and respond to human language in a meaningful way. On the other hand, ML is a subset of AI that allows systems to automatically learn from data patterns and improve performance without the need of any explicit programming.

When a user queries, “How can I optimize Snowflake costs?”, a keyword search might return results that discuss cost in Snowflake generally. However, with semantic search using NLP, the system identifies that the user is seeking specific cost optimization strategies related to the Snowflake. The context given by “optimize” signals the search engine about the user’s intent of going beyond a general search for cost information.

NLP algorithms work by breaking down queries into components like syntax, semantics and context. They identify relationships between words, infer meaning and even detect nuances like tone or sentiment. By doing so, semantic search can pinpoint what the user wants, moving from keyword matching to understanding full sentences and questions.

By explicating the relationships between words in the asked question, NLP enables semantic search to focus on intent rather than word frequency. This capability is particularly valuable in conversational analytics, where users ask questions in natural language and expect answers in a similar manner.

Semantic Search with Kyvos

As organizations seek to unlock more value from their data, Kyvos offers a cutting-edge solution that integrates semantic search and conversational analytics to provide fast, accurate insights. Kyvos allows users to interact with their data naturally, making data analytics as intuitive as holding a conversation.

How Kyvos Implements Semantic Search

Kyvos takes the core principles of semantic search and applies them to complex data environments. Using vector databases and NLP, Kyvos can understand and respond to natural language queries—making it possible for users to extract insights from large datasets, unstructured documents and even technical PDFs without any need to know specific database query languages.

For instance, a user may ask Kyvos, “How do I configure data security settings for different user roles?” To effectively handle this type of natural language query on unstructured data, Kyvos leverages its integration with a vector database. This enables the system to store documentation in the form of embeddings and use semantic search to interpret the question.

By applying similarity search functions, Kyvos can accurately retrieve and present the relevant paragraphs or embeddings from the documentation that provide the answer to configuring data security settings. This way, Kyvos doesn’t just find keywords; it finds the context that matters to the user.

The Role of Conversational Analytics

Other analytics platforms require structured queries and predefined filters, limiting users who are unfamiliar with SQL or other query languages. In contrast, Kyvos allows users to engage with their data conversationally, in plain language.

If a user asks, “What were last quarter’s sales trends?” Kyvos takes this query, embeds it using the same models it uses to store metadata and runs a similarity search on its vector database to identify which semantic models and which columns in the model hold the answer. By identifying the relevant data elements, Kyvos constructs the necessary SQL query to pull the sales data for the last quarter—all without the user needing to touch a single line of code. This capability transforms the way users interact with data, making it more accessible to non-technical users and ensuring that queries are answered more efficiently.

Kyvos: Enhancing Data Discovery with Semantic Search

One of the unique strengths of Kyvos is how it enhances data discovery. It leverages semantic models and vector databases to help users interact with large datasets effortlessly, using natural language to ask questions and get precise answers without having them to write complex queries.

For example, when a user asks, “Show me sales for last year,” Kyvos first determines which semantic model is most likely to answer this question, based on the embeddings of both the query and existing models. Kyvos then narrows down the necessary columns (e.g., “sales” and “date”) to generate the appropriate SQL query. By setting thresholds (e.g., an 80% similarity match), Kyvos ensures that the most relevant data is retrieved, reducing noise and improving accuracy. This process significantly enhances data discovery, enabling businesses to extract actionable insights faster.

Conclusion

The shift from keyword-based search to context-driven understanding is reshaping how enterprises interact with their data, making insights more accessible and meaningful. By leveraging NLP and vector databases, Kyvos leads this transformation, enabling users to engage with complex data through natural language.

Kyvos’ integration of semantic search goes beyond traditional BI, offering a powerful tool for businesses to query large datasets and receive precise answers effortlessly. As more organizations embrace conversational analytics, the ability to ask questions in a natural, human-like way will become an essential part of making data-driven decisions.

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