GraphAware: Insights Discovery with KGs: Bringing Archives to Life (GraphSummit Stockholm 2023)
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With the advances in the domain of NLP and NLU in recent years, such as the GPT-3 and other Large Language Models, the industry is finally mature enough to empower organisations to unlock the incredible knowledge potential hidden within omnipresent unstructured data sources. In this presentation, Dr. Vlasta Kus from GraphAware talked about the state-of-the-art technologies and complex pipelines employed with a goal of turning an archive of a major US foundation into a Knowledge Graph which enables surprise (aha-moments), massive modelling complexity and provides previously unavailable level of insights and pattern discovery.
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GraphAware: Insights Discovery with KGs: Bringing Archives to Life (GraphSummit Stockholm 2023)
- 1. © 2022 All Rights Reserved | GraphAware 1 Insights Discovery with Knowledge Graphs: Bringing Archives to Life Graph Summit Stockholm | March 16, 2023 Vlasta Kůs Lead Data Scientist
- 2. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 2 Archives – disconnected data silos • unstructured data • books, journals, meeting minutes … • documents dating back decades, including scans of typewritten reports • structured data • domain specific knowledge gathered over decades of operations • wide range of tools & ML models required to harness the hidden potential • OCR, NLP, graph data science … • complex relationships and knowledge structure The Archives Challenge Credit: Ford Foundation records, published by the Rockefeller Archive Center.
- 3. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 3 Many industries face the same challenges, such as: • Biomedical domain: patient records & research papers for targeted treatments • Supply chain: monitor news feeds to identify potential disruptions • Law enforcement: intelligence from various reports, news, social media, judiciary documents
- 4. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 4 “The Rockefeller Archive Center is a major repository and research center for the study of philanthropy and its impact throughout the world.” The Archives Challenge: The Rockefeller Archives Center The Archive nowadays safeguards & studies historical materials of various major foundations (Rockefeller Foundation, Ford Foundation), cultural organisations, research institutions etc. Main topics of interest: • natural sciences • medicine • public health • social sciences • … and many others
- 5. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 5 The Archives Challenge: The Rockefeller Archive Center use case Officer Diaries • historical typewritten documents • describe conversations the RF officers held with scientists about their research or research of their colleagues • based on these meetings, RF officers recommend (or not) to the Board of Directors to award grants
- 6. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 6 The Archives Challenge: The Rockefeller Archive Center use case Board of Directors minute books • detailed description of BoDs meetings • resolution on grants awarded to a specific university for a concrete topic • goal: link grants with conversations from Officer Diaries
- 7. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 7 The Archives Challenge: The Rockefeller Archive Center use case Typical questions • Are there any patterns that typically precede the funding of an idea? • How many internal discussions and developments take place within the Foundation before the grant is awarded? • Do granted projects tend to run through recommendations of influential scientists or previous grantees? • Are there trends in funding of certain sub-disciplines? Do they change over time?
- 8. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 8 Traditional approach: an army of (under-)graduate students to sift through all the data
- 9. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 9 Or instead … leverage state-of-the-art technologies: • OCR (scans to text) • Natural Language Processing • Graph Data Science
- 10. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 10 Knowledge Extraction using Natural Language Processing PERSON TOPIC The diaries are abundant in relational knowledge identified using Relation Extraction (RE) models: • C. G. Rossby - WORKS_AT -> M.I.T. • Vilhelm Bjerknes - HAS_STUDENT -> R. • R. – WORKS_ON -> meteorological research • and other such as TALKED_WITH, TALKED_ABOUT etc. ORGANIZATION Named Entity Recognition (NER) models identify named entities: • Standard: Persons, Organizations, Locations • Custom: Topics of conversations, i.e. research disciplines, technologies, diseases, treatments
- 11. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 11 Traditional NLP is human-labor heavy: need for human-annotated training data for the ML models to learn from experience Let’s ask - nicely - the famous OpenAI’s GPT-3 … a.k.a. prompt engineering
- 12. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 12 Knowledge extraction: GPT-3 ● Prompt-based knowledge extraction: quick cheap results, no human labour “Given a prompt, identify all entities and relations among them in the format …” ● Fine-tuning GPT-3 first: better prediction quality & stability, more expensive & labour required
- 13. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 13 NLP has matured massively over the years, but … How to surface all this relational knowledge and complexity?
- 14. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 14 Knowledge Graphs “A Knowledge Graph (KG) is an ever-evolving graph data structure, composed of a set of typed entities, their attributes, and meaningful named relationships. They are built for a specific domain from both structured and/or unstructured data to craft knowledge for humans and machines alike.” From book Knowledge Graphs Applied, Manning 2022 MEAP https://www.manning.com/books/knowledge-graphs-applied
- 15. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 15 Knowledge Graphs: Schema Labelled Property Graphs by nature schema-less, but a KG should have a schema: ● enables definition of curated use cases through projections of the underlying DB ● helps to clarify the vision and ensure knowledge modelling consistency ● 100% flexible - easy to adapt/change
- 16. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 16 What now? Conversations Identify Conversations ● conversation = combination of a Topic, interviewee, interviewer, date ● inputs from NER, RE, coreference resolution ● extract also types of conversations - phone calls, lunches, dinners … ● identify FOLLOW-UP chains: conversations in temporal sequence about same/similar topics, with the same person
- 17. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 17 What now? Link Conversations to Grants Based on the conversations RF officers held, the Board of Directors approves grants ● two disconnected unstructured data sources (officer diaries and BoD minutes) have to be connected ● various granularity of Topics in the data sources → necessity of semantic resolution ● grants typically awarded to universities, not concrete people → need Relation Extraction
- 18. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 19 What now? Graph analytics Graph data science for analysing intellectual network ● influencers ● influencees ● bridges ● communities ● patterns for awarding grants ● temporal analysis of emerging and disappearing trends TALKED_ABOUT relation: “H. says that Yerkes has done the “animal husbandry” of the chimp, but very little work of theoretical or basic importance.”
- 19. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 20 DEMO
- 20. GraphAware. | Hume © 2023 All Rights Reserved | GraphAware 21 Q & A @VlastaKus vlasta@graphawre.com hume.graphaware.com
Notas del editor
- John D. Rockefeller (1839 - 1937) considered in his epoch the wealthiest American of all time at peak, he controlled 90% of all oil production in the US worried that fortune was rolling up so fast his heirs would "dissipate their inheritances or become intoxicated with power" defined, together with other industrialists such as a steel magnate Andrew Carnegie, the modern systematic approach of targeted philanthropy through creation of foundations that had a major effect on scientific research, medicine and education The Rockefeller Foundation to this day 39th largest US foundation the most prominent activities: medical, public health and population sciences agricultural and natural sciences arts and humanities social sciences international relations
- High-level professional gossip
- Project goals Potential of KGs in the digitisation of analog historical records New insights & patterns of the grant-making process by analysing intellectual network and the evolution of research fields Cutting-edge analytics toolkit for researchers coming to the Archive each year Patterns - do lunches, dinners increase likelihood of grant being awarded? How much of internal discussions and developments take place within the RF before the projects are funded? Sub-discipline trends - some declining, others rising to prominence
- To humans - KGs offer the ability to explore and analyse the data and knowledge within it in a natural user-friendly way (I’ll show some of the visualisation options in the demos) Regarding machines - well designed/built KGs are used to help complex ML solutions to leverage and learn from relational knowledge (think of GNNs in biomedical domain for drug repurposing etc.; KG embeddings leveraged in NED systems or in link prediction tasks)
- Robert Yerkes - a pioneer in the study of primate intelligence and of the social behaviour of gorillas and chimpanzees - high-level professional gossip
- In the course of this project, we have: Built a KG valuable for obtaining global view of the data as well as well as the ability to drill down in the investigations, explore, understand, hypothesize … and validate the theories Built and analysed the intellectual network Looked into the evolution of the research fields, observed how some rise in prominence while other fade away Helped to identify new patterns and insights related to the grant-making process We showed the value of KGs on the example of the archive domain, but this is easily transferable to other industries as well.