Knowledge and Ontology Based AI Driven Data Extraction

Extracting Data Insights from Complex Documents

A Daily Challenge

Brief Problem Description

Financial executives constantly interact with diverse and complex documents—market research reports, risk analysis documents, and client portfolios, among others. These documents often include a mix of unstructured data like narratives, tables, charts, and infographics. Extracting meaningful insights from such varied formats is essential for making informed decisions yet remains a time-consuming task.

This challenge highlights the ongoing struggle between the abundance of data and the ability to derive actionable insights efficiently.

Challenges with Current Automated Systems

Why extracting accurate data remains difficult

Even with advanced systems, extracting insights from unstructured documents presents significant challenges. Here are some key issues that financial executives encounter:

01. Low Accuracy

Existing systems often fail to accurately extract data from diverse document formats, such as tables, graphics, and narrative reports, often leading to missed information or misinterpretation.

02. Need for Manual Checks

Despite automation, manual checks remain necessary to verify the accuracy of the extracted information. This need for human review slows down workflows and increases operational costs.

03. Missing the Deeper Details

While systems can provide top-level insights, they often overlook the deeper, more nuanced data points, such as specific KPIs buried within tables or detailed metrics within infographics.

Knowledge and Ontology Based Extraction

Deriving the Business Impact

What is the approach?

Extracting data from unstructured data is a common challenge, and most automated solutions face challenges when document structures change, such as tables or infographics. The advanced approach of using Knowledge Graphs and KPI Ontologies enables more accurate and adaptable extraction of data from unstructured sources. The approach utilizes ontology-driven knowledge graphs to map relationships between KPIs, visually capturing how they interact at various levels, while mathematical agents validate the extracted values, reducing noise and enhancing accuracy.

How Does It Work?

Our Knowledge and Ontology based AI makes data extraction smarter and faster

01. Understands Complex Content

This approach can analyze different elements of a report simultaneously, preserving the context that is often lost in traditional extraction processes. This means it doesn't just "read" data – it understands the relationships between various pieces of information.

02. Automatically Checks for Accuracy

The key to reducing human oversight lies in automated validation. By cross-checking extracted data in real-time, our approach ensures a higher degree of precision, making workdowns more efficient.

03. Fills in Missing Details

Even the most comprehensive data sets can have missing elements. Our approach includes the ability to infer or calculate missing details based on existing data patterns, ensuring that no critical insight is overlooked.

Key Business Benefits

Integrating advanced AI into data extraction processes doesn't just make operations more efficient—it transforms how businesses leverage information. Here's what this shift means:

Accurate and Context-Driven Data Extraction

Ensures that critical insights are captured correctly, improving decision-making.

Faster Processing with Automated Validation

Cuts down time spent on manual reviews, leading to faster report generation and analysis.

Complete Insights with Calculated KPI

Provides a deeper understanding of the data, uncovering hidden trends and opportunities that drive strategic growth.

Knowledge and Ontology Based Extraction

An Advanced AI Approach

Reimagining Data Extraction:

AI that Understands Complex Content

Addressing the Challenges of Traditional RAG Solutions

When dealing with documents containing complex tables and infographics, traditional Retrieval-Augmented Generation (RAG) solutions often face significant challenges. These systems struggle to accurately extract data points due to their inability to fully capture the context of KPIs presented in various formats. There are two primary challenges:

01. Weakness of the Retrieval Engine

Traditional RAG solutions rely on splitting the document into chunks for retrieval. This process often results in the AI retrieving incomplete or inaccurate retrieval of relevant data.

02. Lack of Context for Accurate Extraction

In tables and infographics, KPIs are often represented by labels rather than fully contextualized data. This makes it difficult for RAG engines to correctly identify the associated data points. Therefore the context is needed to fully understand the relationship between the labels and the KPI values.

While the first challenge of optimizing the retrieval engine can be addressed iteratively—by adjusting the chunk size and retrieval strategies—the second challenge requires a more sophisticated solution. This is where ontology-driven knowledge graphs and a more structured approach come into play.

Enhancing Data Extraction with Ontology-Driven Knowledge Graphs

This proposed approach builds on traditional RAG by integrating an additional context layer through Knowledge Graphs and KPI Ontologies, offering a more powerful and accurate extraction process. Here’s how the solution works in detail:

Mapping Relationships with Knowledge Graphs

• Ontology-driven knowledge graphs map out the relationships between KPIs, creating a visual and dynamic representation of how these KPIs interact with each other at different levels. This structure allows for first-level validation by understanding the context in which a KPI exists.
• As the system processes the document, it uses this predefined knowledge graph to identify KPI values from tables, infographics, and other complex formats. The knowledge graph provides essential context that traditional RAG engines lack, enabling the system to better identify the correct data points associated with each KPI.

Contextualizing the Extraction Process

• The RAG engine parses documents and utilizes the knowledge graph to accurately tag KPI values to their corresponding graph nodes. This allows the system to go beyond simple label recognition, adding context to the retrieved data and ensuring that KPIs are properly linked to their corresponding values, regardless of how the data is presented (e.g., in tables or infographics).
• By linking rules and relationships, the knowledge graph provides a contextual foundation that helps the RAG engine better understand the interactions between KPIs. This mitigates the risk of incorrect data extraction and allows for more reliable retrieval of complex information.

Knowledge and Ontology Based Extraction

An Advanced AI Approach

Managing Missing Value Estimation

• One of the most powerful features of this approach is its ability to estimate missing KPI values. The ontology rules built into the knowledge graph define relationships between different KPIs, allowing the system to infer missing values based on existing data. For instance, if certain KPI values are missing in a table or infographic, the system can compute estimates by analyzing the relationships between the present KPIs, ensuring comprehensive data extraction.

Mathematical Agents for Validation

• Once the KPI data is extracted and mapped, mathematical agents validate the extracted values. These agents check for inconsistencies, anomalies, and potential errors in the data. By cross-referencing the extracted values with the expected relationships in the knowledge graph, these agents further ensure the accuracy and reliability of the extracted data.

Extraction Architecture

This architecture showcases the Knowledge and Ontology Based AI-driven data extraction process that begins with a data parser handling various document types (PDF, Word, etc.). We then proceed text processing by a Retrieval Augmented Generation (RAG) engine, which uses a knowledge graph to structure the data based on predefined KPI relationships. A validation layer then ensures data accuracy and computes missing values using mathematical agents and business rules. The result is a structured, accurate data output, ready for analysis and reporting.

Summarizing the Approach

This approach transforms how businesses can extract KPIs from unstructured data, particularly when dealing with diverse forms like tables and infographics. By combining ontology-driven knowledge graphs and RAG, businesses can overcome the limitations of traditional solutions, ensuring that their systems not only retrieve data more efficiently but also do so with a higher level of accuracy and contextual understanding.

How this Approach Stands Ahead?

The true value of this advanced AI approach is in its ability to overcome the limitations of traditional methods. Here’s why it makes a difference:

Ontology-Based Extraction for Higher Accuracy

Ontology-based methods are transforming data extraction by mapping the relationships between KPIs, enabling AI systems to grasp complex assumptions and contextual nuances. This approach significantly enhances accuracy, especially when dealing with varied document formats.

Collaborative Agents

Leveraging multiple specialized AI agents, each focusing on different content types like text, visuals, and tables, allows for holistic data extraction from complex, multi-format documents. This ensures that critical insights are not overlooked.

Automated Validation for Faster Results

Real-time validation algorithms help crucial in reducing manual verification needs. By cross-referencing extracted values against predefined KPIs, this system accelerates workflows without sacrificing the precision of extracted data.

Missing KPI Calculation for Complete Insights

Advanced knowledge graphs enable the estimation of missing KPI values through reverse-engineering or data aggregation. This capability ensures that granular insights are preserved, offering a deeper and more complete analysis.

Areas of Application

As AI technology advances, its applications extend beyond traditional processes, offering solutions to complex data challenges across various domains. Here are key areas where AI-driven data extraction can create significant business impact:

01. Document Vetting

Manual review of documents for KPI consistency is time-consuming and prone to errors. AI automates this process, ensuring accurate definition and validation of KPIs, which streamlines document reviews and minimizes human error.

02. Report Filing

Extracting data from unstructured sources to populate reports is time-inefficient. AI automates data extraction, ensuring that reports are consistently filled with accurate KPIs, reducing the need for manual effort.

03. Invoice Validation

With invoices coming in diverse formats, data extraction can be error-prone. AI automates the validation of key invoice information, ensuring accuracy and consistency regardless of the document format.

04. Contract Review

Reviewing contracts to extract performance metrics is labor-intensive. AI automates this process, ensuring that key contractual obligations and performance metrics are monitored accurately.

05. Financial Performance Analysis

Analyzing financial performance often involves manual data extraction, which can result in inaccuracies. AI automates the extraction process from documents, providing reliable data that enables accurate financial analysis.

06. Compliance Monitoring

Monitoring compliance across various reports is challenging due to the complexity and variability of document structures. AI extracts and validates compliance-related KPIs, ensuring accurate and efficient tracking of regulatory requirements.

07. ESG Data Extraction for Green Financing

A Real Case of Application

ESG Data Extraction for Green Financing

A Real Case of Application

Real Business Problem Description

A global banks’ green financing BU faced difficulties in extracting data from complex, multi-format documents, such as ESG reports that included tables, infographics, and text. The lack of standardized KPIs further complicated the process.

Our Approach

Using our Knowledge and Ontology based Extraction method, backed by an ontology of over 2,500 ESG-related KPIs, we enhanced the BU data extraction capabilities.

Results

40% Improvement in
Extraction Accuracy

Achieved through better understanding of contextual relationships between KPIs.

50% Increase in
Data Coverage

Enabled by parsing multi-format documents more effectively, capturing both granular and high-level insights.

Comprehensive Data
Capture

Allowed the BU to extract detailed KPIs, addressing missing values and ensuring consistent data across varied document types.

About the Authors

Vijay Saini

Innovation Labs Lead, SiriusAI

Vijay is an Innovation Labs lead at SiriusAI, specializing in developing component AI solutions for both structured and unstructured data. He has extensive expertise in generating AI-driven data extractions, and AI-powered customer experience analytics. Vijay has successfully delivered over 15 AI-based products for financial services, focusing on enhancing prospect acquisition and customer experience through advanced data interlinking and AI-driven insights. In his previous roles, Vijay has led global solution development, delivery, and architecture teams at leading consulting firms. Prior to SiriusAI, he was a tech consultant. He developed AI-enabled data solutions for major banks in Thailand and the US, and delivered AI-powered customer experience analyzers to over 10 clients in the US.

Parikshit Bawa

Senior AI Consultant, SiriusAI

Parikshit is a senior AI consultant with 8 years of experience. With an MBA from IIM Calcutta, he provides key business solutions. He excels at customizing AI capabilities with strategic business needs. At SiriusAI, he has led projects like developing an AI-driven report generation solution for a leading US banking private investment group, enabling streamlined and ultra-high net-worth client care. He has also played a key role in helping brokerage firms leverage AI for business intelligence. In previous roles, Parikshit has actively used AI for strategic decision-making. Parikshit also specializes in the implementation of AI-to-AI solutions—from identifying high-impact use cases to implementing tailored strategies—empowering businesses to transition smoothly from AI-active to AI-native, driving efficiency, enhancing customer experience, and unlocking new growth opportunities.