AI Golden Record Pipeline
Auto Content Tagging
486 Patterson Ave
Winston-Salem, NC 27101
– – –
11 Park Place
New York, NY, 10007
– – –
Bagmane Laurel, Krishnappa
Garden, C V Raman Nagar,
Karnataka 560093, India
– – –
1644 Platte Street
Denver, CO 80202
– – –
Lange Dreef 11
4131 NJ Vianen
*Image Inspiration Credit: LLMs & Knowledge Graphs, Medium
Large Language Models (LLMs) like GPT-4 have been at the forefront of the recent AI craze, offering jaw-dropping capabilities in (seemingly) understanding and generating human-like text. These models, trained on vast datasets, have demonstrated proficiency in a range of tasks, from writing essays to coding (as well as demonstrating their abilities in passing a final MBA exam, cloning text dialect to automate Whatsapp Responses, and producing a hilarious acceptance speech at the WSJ Innovators Awards).
However, recent findings have brought to light performance inconsistencies in these LLMs.
Studies have indicated fluctuating levels of accuracy and reliability, raising questions about their dependability for critical applications, as highlighted this year in Davos.
For the above light-hearted use cases, this level of model drift may not be problematic. But in high-stake scenarios, where precise and factual information is critical (such as healthcare, supply chain management, financial accounting, or beyond), organizations cannot afford to gamble on accuracy.
A study conducted by researchers at Stanford and Berkeley found that the ability of LLMs to handle tasks like solving mathematical problems and generating code varied significantly over time. The study showed how GPT-4’s accuracy in certain tasks dropped dramatically, showcasing a major concern in the context of LLM reliability.
This variability in accuracy – particularly a downward slope – poses a significant challenge, especially for applications that heavily rely on the consistent and accurate output of these models. The findings underscore the need for solutions to enhance the stability and reliability of LLMs in practical applications.
Open AI’s models are proprietary, so the exact reasons why accuracy is slipping are unconfirmed. However, we can broadly attribute the cause as a cyclical combination of “fuzzy matching” and model drift.
Model drift simply refers to a degradation of performance, due to:
Combined with LLMs’ fuzzy matching approach to answering questions, we can start to see severe implications.
The “fuzzy matching” approach to input/output of a model is grounded in probabilistic modeling, where the LLM generates content based on the likelihood of word sequences as learned from vast datasets. While this allows for fluid and human-like text generation, it inherently lacks a precise understanding of the factual correctness or specific relationships between entities. It simply knows what’s probably next – think “auto-fill” in Google Docs, or suggested text replies on your iPhone, but instead of being trained on just your words, trained on the internet.
The technical basis of this approach lies in machine learning algorithms, particularly neural networks, which process input data (text) and generate outputs based on learned patterns. These models don’t “understand” content in the human sense but rather rely on statistical correlations. This means they can effectively mimic language patterns but may falter when precise, context-specific accuracy is required.
For instance, when asked about specific factual information or nuanced topics, LLMs might produce answers that are plausible but not necessarily accurate. We know this as the “hallucination” crisis.
In scenarios where factual accuracy and consistency are paramount, this probabilistic approach can be a significant limitation, potentially leading to unreliable or misleading information. Combined with the inevitability of model drift, fuzzy matching can pose a serious threat to a business looking to deploy production AI models with sustainable accuracy.
What is the opposite of “fuzzy matching” when it comes to LLM accuracy? Answer: Explicit, Structured Data.
Unlike fuzzy matching’s probabilistic approach, structured data representation relies on explicitly defined data, definitions, and relationships
This is where knowledge graphs—like Fluree—shine.
Where LLMs might offer ambiguity, Knowledge Graphs offer explicitness.
A knowledge graph is a database that represents information as a graph network of entities and relationships between them. They are commonly used for advanced analytics, recommendation systems, semantic search engines, or data discovery, cataloging and integration.
In knowledge graphs, data is represented in the form of semantic objects (or entities), and the connections or relationships between these objects are explicitly mapped. Importantly, they leverage ontologies, which provide formal, structured representations of data and relationships within data.
Most important to the matter at hand, knowledge graphs are explicit. They don’t just contain raw data; they define and map the relationships between data points in a global, understandable, and machine-readable way. This explicitness ensures that every piece of information is contextualized, making the data not only more accessible but also more meaningful and reliable. This characteristic of knowledge graphs fundamentally enhances their utility in applications where precision and context are critical.
Knowledge graphs can significantly enhance the accuracy and consistency of LLMs, offering a tangible solution to the challenges of fluctuating performance and reliability faced by these models. The explicit, structured, and semantically-defined data that knowledge graphs organize are vital for leveraging LLMs to their fullest potential:
Even further, knowledge graphs help prioritize what’s relevant and important.
Andrew Nyguen suggests in his argument for “data-centric AI” that smaller sets of high quality data are far superior for model training and deployment. While today’s Large Language Models are trained on vast, general data, enterprise AI will instead succeed with this small data approach. Knowledge graphs can serve this “small data” approach through interconnecting across various domains and providing context-rich, targeted information that enhances model understanding and decision-making.
It’s not that LLMs are failing. They are performing what they were engineered to do; generating probabilistic outputs based on statistical correlations. LLMs just need contextual grounding. If LLMS were unleashed on well-defined, well-structured, and well-governed data, their accuracy and reliability would remain very high at scale and over time.
A paper published in IEEE represents the synergistic relationship between Knowledge Graphs and LLMs, pointing to how each technology compliments the other. Where LLMs lack contextual grounding through implicit knowledge, knowledge graphs provide structured truth. And where knowledge graphs might lack completeness and generalized knowledge, LLMs help fill that gap.
Businesses aiming to truly build effective AI should prioritize structured data management and governance as their strategy and should appoint knowledge graphs as the vehicle to deliver and maintain these capabilities.
Fluree offers a full suite of semantic data management products to build, maintain, leverage and share enterprise knowledge. Learn more about our comprehensive knowledge graph solutions and book a call to speak with an expert here. Or, dive into more information about our product suite below:
Blurb about what the company does and how they interact with Fluree
Visit Partner Site
Visit Partner Site More Details
Blurb about what the company does and how they interact with Fluree blah blah blah minim officia amet nulla cupidatat eu id adipisicing velit aliquip elit labore labore aliquip exercitation enim do ea sunt nisi aute amet magna cillum culpa elit voluptate culpa officia eiusmod sunt ipsum duis laborum magna tempor cillum esse do sunt
"*" indicates required fields
Follow us on Linkedin
Join our Mailing List
Subscribe to our LinkedIn Newsletter
Subscribe to our YouTube channel
Partner, Analytic Strategy Partners; Frederick H. Rawson Professor in Medicine and Computer Science, University of Chicago and Chief of the Section of Biomedical Data Science in the Department of Medicine
Robert Grossman has been working in the field of data science, machine learning, big data, and distributed computing for over 25 years. He is a faculty member at the University of Chicago, where he is the Jim and Karen Frank Director of the Center for Translational Data Science. He is the Principal Investigator for the Genomic Data Commons, one of the largest collections of harmonized cancer genomics data in the world.
He founded Analytic Strategy Partners in 2016, which helps companies develop analytic strategies, improve their analytic operations, and evaluate potential analytic acquisitions and opportunities. From 2002-2015, he was the Founder and Managing Partner of Open Data Group (now ModelOp), which was one of the pioneers scaling predictive analytics to large datasets and helping companies develop and deploy innovative analytic solutions. From 1996 to 2001, he was the Founder and CEO of Magnify, which is now part of Lexis-Nexis (RELX Group) and provides predictive analytics solutions to the insurance industry.
Robert is also the Chair of the Open Commons Consortium (OCC), which is a not-for-profit that manages and operates cloud computing infrastructure to support scientific, medical, health care and environmental research.
Connect with Robert on Linkedin
Founder, DataStraits Inc., Chief Revenue Officer, 3i Infotech Ltd
Sudeep Nadkarni has decades of experience in scaling managed services and hi-tech product firms. He has driven several new ventures and corporate turnarounds resulting in one IPO and three $1B+ exits. VC/PE firms have entrusted Sudeep with key executive roles that include entering new opportunity areas, leading global sales, scaling operations & post-merger integrations.
Sudeep has broad international experience having worked, lived, and led firms operating in US, UK, Middle East, Asia & Africa. He is passionate about bringing innovative business products to market that leverage web 3.0 technologies and have embedded governance risk and compliance.
Connect with Sudeep on Linkedin
CEO, Data4Real LLC
Julia Bardmesser is a technology, architecture and data strategy executive, board member and advisor. In addition to her role as CEO of Data4Real LLC, she currently serves as Chair of Technology Advisory Council, Women Leaders In Data & AI (WLDA). She is a recognized thought leader in data driven digital transformation with over 30 years of experience in building technology and business capabilities that enable business growth, innovation, and agility. Julia has led transformational initiatives in many financial services companies such as Voya Financial, Deutsche Bank Citi, FINRA, Freddie Mac, and others.
Julia is a much sought-after speaker and mentor in the industry, and she has received recognition across the industry for her significant contributions. She has been named to engatica 2023 list of World’s Top 200 Business and Technology Innovators; received 2022 WLDA Changemaker in AI award; has been named to CDO Magazine’s List of Global Data Power Wdomen three years in the row (2020-2022); named Top 150 Business Transformation Leader by Constellation Research in 2019; and recognized as the Best Data Management Practitioner by A-Team Data Management Insight in 2017.
Connect with Julia on Linkedin
Senior Advisor, Board Member, Strategic Investor
After nine years leading the rescue and turnaround of Banco del Progreso in the Dominican Republic culminating with its acquisition by Scotiabank (for a 2.7x book value multiple), Mark focuses on advisory relationships and Boards of Directors where he brings the breadth of his prior consulting and banking/payments experience.
In 2018, Mark founded Alberdi Advisory Corporation where he is engaged in advisory services for the biotechnology, technology, distribution, and financial services industries. Mark enjoys working with founders of successful businesses as well as start-ups and VC; he serves on several Boards of Directors and Advisory Boards including MPX – Marco Polo Exchange – providing world-class systems and support to interconnect Broker-Dealers and Family Offices around the world and Fluree – focusing on web3 and blockchain. He is actively engaged in strategic advisory with the founder and Executive Committee of the Biotechnology Institute of Spain with over 50 patents and sales of its world-class regenerative therapies in more than 30 countries.
Prior work experience includes leadership positions with MasterCard, IBM/PwC, Kearney, BBVA and Citibank. Mark has worked in over 30 countries – extensively across Europe and the Americas as well as occasional experiences in Asia.
Connect with Mark on Linkedin
Chair of the Board, Enterprise Data Management Council
Peter Serenita was one of the first Chief Data Officers (CDOs) in financial services. He was a 28-year veteran of JPMorgan having held several key positions in business and information technology including the role of Chief Data Officer of the Worldwide Securities division. Subsequently, Peter became HSBC’s first Group Chief Data Officer, focusing on establishing a global data organization and capability to improve data consistency across the firm. More recently, Peter was the Enterprise Chief Data Officer for Scotiabank focused on defining and implementing a data management capability to improve data quality.
Peter is currently the Chairman of the Enterprise Data Management Council, a trade organization advancing data management globally across industries. Peter was a member of the inaugural Financial Research Advisory Committee (under the U.S. Department of Treasury) tasked with improving data quality in regulatory submissions to identify systemic risk.
Connect with Peter on Linkedin
Turn Data Chaos into Data Clarity
Enter details below to access the whitepaper.
Pawan came to Fluree via its acquisition of ZettaLabs, an AI based data cleansing and mastering company.His previous experiences include IBM where he was part of the Strategy, Business Development and Operations team at IBM Watson Health’s Provider business. Prior to that Pawan spent 10 years with Thomson Reuters in the UK, US, and the Middle East. During his tenure he held executive positions in Finance, Sales and Corporate Development and Strategy. He is an alumnus of The Georgia Institute of Technology and Georgia State University.
Connect with Pawan on Linkedin
Andrew “Flip” Filipowski is one of the world’s most successful high-tech entrepreneurs, philanthropists and industry visionaries. Mr. Filipowski serves as Co-founder and Co-CEO of Fluree, where he seeks to bring trust, security, and versatility to data.
Mr. Filipowski also serves as co-founder, chairman and chief executive officer of SilkRoad Equity, a global private investment firm, as well as the co-founder, of Tally Capital.
Mr. Filipowski was the former COO of Cullinet, the largest software company of the 1980’s. Mr. Filipowski founded and served as Chairman and CEO of PLATINUM technology, where he grew PLATINUM into the 8th largest software company in the world at the time of its sale to Computer Associates for $4 billion – the largest such transaction for a software company at the time. Upside Magazine named Mr. Filipowski one of the Top 100 Most Influential People in Information Technology. A recipient of Entrepreneur of the Year Awards from both Ernst & Young and Merrill Lynch, Mr. Filipowski has also been awarded the Young President’s Organization Legacy Award and the Anti-Defamation League’s Torch of Liberty award for his work fighting hate on the Internet.
Mr. Filipowski is or has been a founder, director or executive of various companies, including: Fuel 50, Veriblock, MissionMode, Onramp Branding, House of Blues, Blue Rhino Littermaid and dozens of other recognized enterprises.
Connect with Flip on Linkedin
Brian is the Co-founder and Co-CEO of Fluree, PBC, a North Carolina-based Public Benefit Corporation.
Platz was an entrepreneur and executive throughout the early internet days and SaaS boom, having founded the popular A-list apart web development community, along with a host of successful SaaS companies. He is now helping companies navigate the complexity of the enterprise data transformation movement.
Previous to establishing Fluree, Brian co-founded SilkRoad Technology which grew to over 2,000 customers and 500 employees in 12 global offices. Brian sits on the board of Fuel50 and Odigia, and is an advisor to Fabric Inc.
Connect with Brian on Linkedin
Eliud Polanco is a seasoned data executive with extensive experience in leading global enterprise data transformation and management initiatives. Previous to his current role as President of Fluree, a data collaboration and transformation company, Eliud was formerly the Head of Analytics at Scotiabank, Global Head of Analytics and Big Data at HSBC, head of Anti-Financial Crime Technology Architecture for U.S.DeutscheBank, and Head of Data Innovation at Citi.
In his most recent role as Head of Analytics and Data Standards at Scotiabank, Eliud led a full-spectrum data transformation initiative to implement new tools and technology architecture strategies, both on-premises as well as on Cloud, for ingesting, analyzing, cleansing, and creating consumption ready data assets.
Connect with Eliud on Linkedin
Get the right data into the right hands.
Build your Verifiable Credentials/DID solution with Fluree.
Wherever you are in your Knowledge Graph journey, Fluree has the tools and technology to unify data based on universal meaning, answer complex questions that span your business, and democratize insights across your organization.
Build real-time data collaboration that spans internal and external organizational boundaries, with protections and controls to meet evolving data policy and privacy regulations.
Fluree Sense auto-discovers data fitting across applications and data lakes, cleans and formats them into JSON-LD, and loads them into Fluree’s trusted data platform for sharing, analytics, and re-use.
Transform legacy data into linked, semantic knowledge graphs. Fluree Sense automates the data mappings from local formats to a universal ontology and transforms the flat files into RDF.
Whether you are consolidating data silos, migrating your data to a new platform, or building an MDM platform, we can help you build clean, accurate, and reliable golden records.
Our enterprise users receive exclusive support and even more features. Book a call with our sales team to get started.
Download Stable Version
Download Pre-Release Version
Register for Alpha Version
By downloading and running Fluree you agree to our terms of service (pdf).
Hello this is some content.