Latent Spaces of Heritage Crime

Shawn Graham, Carleton U

shawn.graham@carleton.ca | scholar.social/@electricarchaeo

Using Knowledge Graph Embedding Models to Explore the Gaps in What We Think We Know

follow along!

https://slides.com/shawngraham/latent-spaces-of-heritage-crime/

Previous on the behaviourspace of stories

How can we discover and visualize the things we don't know?

... we can do it with a knowledge graph.

• a knowledge graph can have many different kinds of relationships and many different kinds of entities
• linked open data is a special kind of knowledge graph where entities and predicates are tied to authorities
  • not mission critical, but if you wish to link your graph across repositories, then absolutely necessary.

• knowledge graphs can be queried by traversal (neo4j)
• knowledge graphs manage data by adding more links

...but wait, there's more!

Knowledge graphs

good ol' wikipedia, eh? https://en.wikipedia.org/wiki/Knowledge_graph_embedding

Models I used to create the embeddings:

DistMult - uses diagonals; can't represent asymetric relations well

ComplEx - like DistMult but with complex numbers, can handle asymetric relations

RotatE - models relationships as rotations in complex space. Can handle inversion/composition tool.

<- across 3 dimensions, contrasting between actors and places

<- across 3 dimensions, organizing across domestic/wild divide, and who acts upon whom

<- across 3 dimensions, notice the close overlap of wolf/grandma in dimension 0 and dimension 2

Graham, Yates et al 2023

Antiquities Trade Example

[link to article]

Here, we were merely interested in clustering, and we didn't appreciate the difference that model geometry made

...each step is theory-laden and dangerous.

follow along...

Humans

• Thoughtful process
• Hard to coordinate
• Dictionary of predicates, or resolve afterwards?
• Inconsistent
• Slow
• Return fewer triplets of higher quality

Machines

• LLM as word calculator
• Relentless
• Careful schemas, rules mostly followed but often flummoxed by the passive voice
• Returns more triplets of lower quality

A pattern for building temporally-grounded knowledge graphs from scholarly and documentary sources, optimised for downstream knowledge graph embedding (KGE) training. 

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## The core problem

Most knowledge graph construction from text produces triples: `(subject, predicate, object)`. Applied to scholarly or historical sources, this collapses a critical distinction. A monograph arguing that the Treaty of Westphalia established state sovereignty is doing something categorically different from the same monograph reporting that previous historians have made this argument. The first is a knowledge claim being advanced; the second is a claim being attributed. Embedding models trained on undifferentiated triples will represent both as equivalent facts which could create a fundamental error of provenance.

A second problem: triples are atemporal. `(Napoleon, commanded, Grand Armée)` is true for a bounded interval. Without a temporal anchor, the triple cannot be placed on a timeline, cannot be compared with contradicting triples from different periods, and cannot serve as useful training data for temporally-aware embedding methods such as TComplEx, TNTComplEx, or DE-SimplE. The date is not metadata about the extraction but is instead a first-class component of the knowledge claim itself.

This pattern addresses both problems. Every extracted unit of knowledge is a **quad**, rather than a triple, and every quad carries a **provenance class** distinguishing what the source is *claiming* from what it is *reporting others have claimed*.[...]

The idea is to describe the problem, and describe the tests that would indicate whether or not the problem was being solved, and then instructions to invoke the creation of the relevant code.

This is a hermeneutic approach.

The pattern that results can be fed to your language model (word calculator) of choice. Or graduate students.

Spec Driven Development

Using local model qwen3.5 9b hosted at Carleton University (example is building a knowledge graph from a synoptic archaeological project report).

interface: GUI, TUI, CLI (for batches)

Choose your model!

Different model geometries, i.e, how they mathematically represent 'knowledge' imply different kinds of attention/value/noticing

so.... generate a variety of models

"...so you've got a knowledge graph. That don't impress me much." - not Shania Twain

k ~ number of dimensions, which should be roughly same order of magnitude as number of predicates.

https://github.com/shawngraham/kge-explorer/

There is no one 'correct' way to explore an embedding space.

so if time allows, here are some other experiments.

In the knowledge graph embeddings, the two 'best' scoring models are TransE and RotatE implying that the underlying data is best represented as chains of simple hierarchical relationships, and also chains of inference and composition

• which could be saying something about the nature of the underlying articles

We then use principle components analysis to reduce to 3 dimensions. We search that 3 dimensional space for gaps where otherwise the distribution of points would suggest there should be something.

The gaps therefore represent ideas/entities that should be present but aren't.

RotatE organized the network primarily by real-world context: cultural geography, market layer, and hierarchical status.

TransE organized the network purely by structural node types (Person, Event, Location, Document), explaining much more of the dataset's variance in just 3 dimensions.

Gap 1 exists in the space between: Etruscan black-figured kalpis, Piedras Negras Stelae 35 & 26, the raid on Medici's storerooms, Piedras Negras Stela 2

Gap 4 is similar, includes more of the Piedras Negras Stelae;

Gap 8 is defined by Hecht-Medicci-Becchina-Camera on the edges;

This gap exists in Cluster 1, which is defined solely by the Piedras Negras Stelae.

The TC article suggests that the looting of the site is done by drug traffickers and loggers.

The model is suggesting however that mathematically, there is a gap here that is reminiscent of what we see in Italy: so not ad-hoc looting of convenience, but rather something far more systematic.

I'm still working out the method here, which depends on exploring the topology of this 3d space.

For another approach, let's look at the next slide!

This approach surfaces patterns through a kind of tug-o-war between the different principal components

• Dim 0 "Masterminds" (Medici, Becchina, True) at the negative pole, and the "Macro-Events" (repatriations, raids, excavations) at the positive pole.
• Dim 1 Ground-level labour / Looting Sites.
• Dim 2 Documents / Physical Artifacts.

I expected 'significant gaps' at each apex, but this didn't happen (it does in the RotatE model).

The clustering and gaps here shows not a truth about the antiquities trade, but a truth about the underlying source data. The strong clustering in the one pole is the key.

The case studies from TC focus on strong hierarchical networks, and mathematically this shows up by suggesting gaps in our knowledge regarding the low-level sourcing of materials.

Or: TransE picks up on the historiography of the case studies.

Map: a two-dimensional projections of the model’s multidimensional latent space, mapped to a simplex (triangle) using the top three Principal Components. Entities in the corners are most strongly dominated by that particupar dimension / principle component. Entities in the centre bridge concepts.

〞

So what?

– You, in your head,

right now

(always ask: so what!)

Summary

• Vector algebra lets us play 'what if', and try to find the characteristics of unknown entities/relationships

• TransE surfaced a kind of computational historiography of the source data, showing gaps in attention and how those gaps then might affect attempts at link prediction or clustering.

• RotatE suggested similarity of patterns from one geographical region to another

• I've built more models with different geometries but haven't analyzed fully yet. But they will surface other aspects.

Thanks!

shawn.graham@carleton.ca

• knowledge graph construction can be machine-accelerated
• data cleaning is the single most important step
• different embedding model geometries pick up different facets of the trade
• everything is a direction; strike out in a new direction and you'll surface new ideas in your old data.
• no one embedding model will give you the complete picture
• an embedding model can find clusters, predict unknown relationships, and be queried to reveal gaps in the underlying data
• knowledge graph embedding models can represent all kinds of narrative information, leading to new insights about things we thought we knew