Objective

Participants are required to discover a taxonomy (class hierarchy) in a target domain after training or observing ontologies from other source domains. Unlike End-to-End OL or Ontology Extension tasks, this task focuses solely on hierarchical structure discovery and tests cross-domain generalization. The participant will do the training on ontologies from domains such as medicine, engineering, or chemistry. Evaluate on an unseen domain, e.g., material science. The goal is to correctly infer is-a / subclass relationships among types in the new domain. Participants can either develop their own taxonomy discovery algorithm or adapt existing taxonomy induction methods, including LLM-based or embedding-based approaches. 

What must participants build?

A system that, starting from a given unseen ontology's types (classes) and participants, should perform:

• Taxonomic Discovery (is-a/subclass)

A toy example

Considering that the model learned how to create a taxonomy. Given a list of types as follows:

device

sensor

system

application

environmental condition

measurement

person

We expect the following outputs:

(sensor, is-a, device)

(system, is-a, device)

(application, is-a, device)

Dataset

More information on datasets (formatting) will be available soon! Stay tuned!

Evaluation Metrics

• Standard Metrics:  Precision, Recall, F1