In the past years, sophisticated methods for extracting knowledge graphs from Wikipedia, like DBpedia,YAGO, and CaLiGraph, have been developed. In this talk, I revisit some of these methods and examine if and how they can be replaced by prompting a large language model like ChatGPT.

1. 05/29/23 Heiko Paulheim 1
Knowledge Graph Generation
from Wikipedia in the Age of ChatGPT:
Knowledge Extraction
or Knowledge Hallucination?
Heiko Paulheim

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Yeah, I’ve been Invited for this Keynote!

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A Brief History of Knowledge Graphs
Google’s
Announcement
DBpedia
YAGO
ResearchCyc Wikidata
Freebase
NELL

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A Brief History of Knowledge Graphs

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Wikipedia as a Knowledge Graph
• Wikipedia based Knowledge Graphs
– DBpedia: launched 2007
– YAGO: launched 2008
– Extraction from Wikipedia
using mappings & heuristics
• Present
– Two of the most used knowledge graphs
– ...with Wikidata catching up

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Wikipedia as a Knowledge Graph

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Wikipedia as a Knowledge Graph
city
campus
state
c
i
t
y

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Wikipedia as a Knowledge Graph
• Mapping to a central schema/ontology
University
chancellor Person
Organisation
Agent
campus Place
range
range
domain
domain
subclass of
subclass of
subclass of

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Wikipedia as a Knowledge Graph

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DBpedia Extraction, ChatGPT Style

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DBpedia Extraction, ChatGPT Style

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DBpedia Extraction, ChatGPT Style

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DBpedia Extraction, ChatGPT Style
• Looks nice, but there are some glitches…
– Handling datatypes:
– Handling coordinates:
• But maybe we can resolve this with better prompt engineering...

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DBpedia Extraction, ChatGPT Style
;

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DBpedia Extraction, ChatGPT Style

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Knowledge Graph Completion, ChatGPT Style

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Knowledge Graph Hallucination, ChatGPT Style
• Some more findings:
• None of those are real!
• cf. DBpedia:

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Knowledge Graph Completion, ChatGPT Style

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Knowledge Graph Hallucination, ChatGPT Style

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Knowledge Graph Hallucination, ChatGPT Style
• My first reaction: • My second reaction:

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Knowledge Graph Hallucination, ChatGPT Style
Mannheim is a city in the southwestern part of
Germany, the third-largest in the German state of
Baden-Württemberg after Stuttgart and Karlsruhe with a
2019 population of approximately 309,000 inhabitants.

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But While We’re at it...
• Hey ChatGPT, did you know this paper?

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Back to my Original Presentation

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Flashback to 2018
• Much of the missing information is in the Wikipedia text
• ...and already in the abstracts
• Abstracts follow a structure
municipality state country
+
+
-
-

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Flashback to 2018
• The first three populated places linked in an abstract about a town
are that town’s municipality, state, and country
• All genres linked in an abstract about a writer
are that writer’s genres
• The first place linked in an abstract about a person
is that person’s birthplace
• The types are already in DBpedia
• Automatically finding those patterns:
We can use existing relations as training data
– Using a local closed world assumption for creating negative examples

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Flashback to 2018
• Target: use only models that have >95% precision
– We want extra knowledge, but not much extra noise
• Outcome
– Models could be learned for 99 relations
– Almost 1M additional statements

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style
Only the first three
facts are extracted
from the abstract

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style
DBpedia uses
dbo:federalState here

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style
• In the original paper, we trained general ML models...

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Flashback to 2018
• We used solely position and type features
– Nothing language specific
– i.e.: we can apply this to any language
• Extension to 12 largest language editions of DBpedia
– Exploiting inter-language links
– 187 relations (was: 99), 1.6M axioms (was: 1M), at precision >0.95
– #statements per language correlates with #language links to English!

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style
• Let’s challenge ChatGPT a bit more...

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style
Mostly hallucination…
this is not the population
value from the abstract!

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style

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Relation Extraction from Wikipedia Abstracts:
ChatGPT Style

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Knowledge Graph Hallucination, ChatGPT Style
• ChatGPT seemed to be eager on “extracting” coordinates from
infoboxes and abstracts

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Knowledge Graph Hallucination, ChatGPT Style
• At least, all are different coordinates in Mannheim

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Funny Footnote –
Even more Knowledge Hallucination
• Trying to create the input file for Google Map on the previous slide:
Even more hallucination…
many of these values
are not
from the responses

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Back to my Original Presentation

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Cat2Ax: Axiomatizing Wikipedia Categories
 dbo:Album
 dbo:artist.{dbr:Nine_Inch_Nails}
 dbo:genre.{dbr:Rock_Music}
See: ISWC 2019 Paper on Uncovering the Semantics of Wikipedia Categories

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Cat2Ax: Axiomatizing Wikipedia Categories
– Frequency: how often does the pattern occur in a category?
• i.e.: share of instances that have dbo:genre.{dbr.Rock_Music}?
– Lexical score: likelihood of term as a surface form of object
• i.e.: how often is Rock used to refer to dbr:Rock_Music?
– Sibling score: how likely are sibling categories sharing similar patterns?
• i.e., are there sibling categories with a high score for dbo:genre?

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Cat2Ax: ChatGPT Style

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Cat2Ax: ChatGPT Style

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Cat2Ax: ChatGPT Style

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Cat2Ax: ChatGPT Style

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CaLiGraph Example
Category: Musical Groups established
in 1987
List of symphonic metal bands
Category: Swedish death metal bands
List of Swedes in Music

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CaLiGraph: ChatGPT Style

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CaLiGraph: ChatGPT Style

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CaLiGraph: ChatGPT Style

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Back to my Original Presentation

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Improving Entity Coverage:
Lists in Wikipedia
• Only existing pages have categories
– Lists may also link to non-existing pages

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Pushing Entity Coverage Further
• Beyond red links (2020) • Beyond explicit lists (2021)

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Cat2Ax: ChatGPT Style

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Entity Extraction from Listings: ChatGPT Style

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Entity Extraction from Listings: ChatGPT Style

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Entity Extraction from Listings: ChatGPT Style

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Entity Hallucination from Listings:
ChatGPT goes Rogue

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Entity Hallucination from Listings:
ChatGPT goes Rogue

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Entity Hallucination from Listings:
ChatGPT goes Rogue

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Entity Hallucination from Listings:
ChatGPT goes Rogue

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Entity Hallucination from Listings:
ChatGPT goes Rogue
This went on for a while, but lead nowhere.

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Revisiting CaLiGraph: Entity Disambiguation
• Examples: Wikipedia pages of Die Krupps and Eisbrecher
?

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Revisiting CaLiGraph: Entity Disambiguation
Proper solution:
”NASTyLinker: NIL-Aware Scalable
Transformer-based Entity Linker”
Tuesday, 12 am

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Entity Disambiguation: ChatGPT Bloopers

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Entity Disambiguation: ChatGPT Bloopers

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Take Aways
• Basic KG creation with ChatGPT can work
– At least in a human in the loop setup
• Reinforcement signals might help here
– Main challenge: hallucinations
• On the other hand: consider them
“extraction of additional facts”
• Isn’t that just like heuristic KG completion?
• Disclaimer:
– No PhD students were harmed or replaced by ChatGPT.
• Full ChatGPT protocol available here.

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Knowledge Graph Generation
from Wikipedia in the Age of ChatGPT:
Knowledge Extraction
or Knowledge Hallucination?
Heiko Paulheim