=Paper=
{{Paper
|id=Vol-3739/abstract-18
|storemode=property
|title=Actively Learning Ontologies from LLMs: First Results (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-3739/abstract-18.pdf
|volume=Vol-3739
|authors=Matteo Magnini,Ana Ozaki,Riccardo Squarcialupi
|dblpUrl=https://dblp.org/rec/conf/dlog/MagniniOS24
}}
==Actively Learning Ontologies from LLMs: First Results (Extended Abstract)==
https://ceur-ws.org/Vol-3739/abstract-18.pdf
Actively Learning Ontologies from LLMs: First Results
(Extended Abstract)
Matteo Magnini1 , Ana Ozaki2,3 and Riccardo Squarcialupi1
1
Department of Computer Science and Engineering, University of Bologna, Via dell’Università 50, Cesena, Italy
2
Department of Informatics, University of Oslo, Gaustadalléen 23B, Oslo, Norway
3
Department of Informatics, University of Bergen, Thormøhlensgate 55, Bergen, Norway
Abstract
In active learning a learner attempts to acquire some kind of knowledge by posing questions to a teacher. Here
we consider that the teacher is a language model and study the case in which the knowledge is expressed as an
ontology. To evaluate the approach, we present first results testing logical consistency and the performance of
GPT and other language models when answering whether concept inclusions from existing ℰℒ ontologies are
‘true’ or ‘false’.
Keywords
Active Learning, Ontologies, Language Models
1. Introduction
Large language models (LLMs) have reached a point where they have accumulated so much information
and improved on their question/answering capability that we are now willing to interact and learn from
them. Prompts to these models vary from questions about general knowledge such as basic definitions
and historical events, to more domain specific questions, e.g., scientific facts related to health and
medicine. What can we learn from LLMs? And, since it is known that they can give false information,
is there an automated way of discovering whether responses are incorrect or at least inconsistent?
In this work we explore an active learning approach to learn from LLMs. In active learning [1], a
learner attempts to learn some kind of knowledge by posing questions to a teacher. The questions
made by the learner are called membership queries and are answered with ‘yes’ or ‘no’ (or equivalently,
with ‘true’ or ‘false’) [2]. Here we consider that the teacher is an LLM and study the case in which
the knowledge to be learned is expressed as an ontology. We use the Manchester OWL Syntax [3] in
our prompts, as this syntax is closer to natural language. We present preliminary results showing the
performance of GPT and other language models when answering whether concept inclusions created
by an ontology engineer on prototypical ℰℒ ontologies are ‘true’ or ‘false’.
2. Probing Language Models
Here we briefly describe challenges encountered when probing LLMs with ontology axioms and how
we handled them.
Input Format and Unexpected Responses One important factor is the format of the query. To
systematically query an LLM with the goal of learning an ontology, it is useful to standardise the
questions. For the membership queries task, we investigate the use of the Manchester OWL syntax [3],
as this is an ontology syntax designed to be closer to natural language. Another aspect to consider is
that, in principle, there are no constraints in the answers returned by the language model. An LLM may
answer with an arbitrary and unexpected response, even if the expected answer is just a single word like
in the case of membership queries in the exact learning model. To mitigate this issue, one can explicitly
DL 2024: 37th International Workshop on Description Logics, June 18–21, 2024, Bergen, Norway
$ matteo.magnini@unibo.it (M. Magnini); anaoz@uio.no (A. Ozaki); riccard.squarcialupi@studio.unibo.it (R. Squarcialupi)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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tell the LLM to answer with ‘true’ or ‘false’. This particular request can be done in the question itself
(e.g., appending “Answer with ‘true’ or ‘false’.” after the query) or by exploiting some hyper-parameters
of the API of the LLM. In the second case, one can use a system prompt – a.k.a., integrated text into
each query within the chat session – to enrich the model with additional information and useful to
harness the response. We highlight that there are also other hyper-parameters that could help driving
the LLM’s response into the desired format (e.g., maximum number of tokens, temperature, etc.). Even
with all these precautions the model may return an unexpected response. For example: (i) the answer
can have more text than just ‘true’ or ‘false’, (ii) both ‘true’ and ‘false’ can appear in the answer, (iii) the
answer does not have ‘true’ nor ‘false’. While in the first scenario a trivial parsing would determine
the correct classification, in the remaining cases, since there is some ambiguity, we considered a third
value, which we called ‘unknown’.
Correctness and Logical Consistency We also need to deal with challenges regarding the correct-
ness of the responses (assuming the format of the responses returned by the language model are as
expected, see Section 2). Actively learning ontologies has been investigated for various fragments of
ℰℒ [4, 5, 6], though, without using LLMs as teachers. If an LLM is playing the role of the teacher then
there is no guarantee that the responses are correct [7] (in the sense of reflecting the ‘truth’ about the
real world) and, moreover, that they are logically consistent with any ℰℒ ontology. Indeed, it is known
that LLMs can learn statistical features instead of performing logical reasoning [8]. So, we need to
consider the following kinds of errors:
1. 𝐶 ⊑ 𝐷 should be ‘false’ (cf. the real world) but the LLM answers ‘true’;
2. 𝐶 ⊑ 𝐷 should be ‘true’ (cf. the real world) but the LLM answers ‘false’;
3. all concept inclusions in 𝒯 = {𝐶1 ⊑ 𝐷1 , . . . , 𝐶𝑛 ⊑ 𝐷𝑛 } are answered with ‘true’, 𝒯 |= 𝐶 ⊑ 𝐷
but 𝐶 ⊑ 𝐷 is classified as ‘false’.
The last case is a logical inconsistency. One strategy to handle this issue is to consider the closure under
logical consequence [9]. That is, in Point 3, one could consider 𝐶 ⊑ 𝐷 as ‘true’.
3. Experiments
The experiments consist in performing a number of membership queries with multiple LLMs on
prototypical ontologies. These are small ontologies taken from ontology repositories and used for
experiments in the ExactLearner project [6]1 2 , which focuses on ℰℒ ontologies. In all ontologies
considered, the logical closure is finite. We consider the following ontologies:
1. Animals contains knowledge related to the animal realm, including actual animals, subphyla,
classes, orders, etc. The ontology has 12 (explicit) logical axioms in ℰℒ and 20 logical axioms in
the logical closure (that is, taking into account inferred axioms).
2. Cell provides information about different cells based on their type, development stage and
organism. The ontology has 24 logical axioms in ℰℒ and 24 in the logical closure.
3. Football is a minimal ontology that describes the relations between football game, teams, players
and managers. It has 9 logical axioms in ℰℒ and 12 in the logical closure.
4. Generations describes the members and relations within a family. This ontology has 18 (explicit)
logical axioms in ℰℒ and 42 in the logical closure.
1
https://github.com/bkonev/ExactLearner/
2
Generations, University, and Cell were also part of the Protégé Ontology Library. Not maintained anymore at https:
//protegewiki.stanford.edu/wiki/Protege_Ontology_Library but still accessible via web archive at https://web.archive.org/
web/20210226123540/https://protegewiki.stanford.edu/wiki/Protege_Ontology_Library
5. University is a small ontology, focusing on the professor role, with 4 logical axioms in the logical
axioms in ℰℒ and 8 in the logical closure.
Animals University Generations Football Cell
Models
T F U T F U T F U T F U T F U
Mistral (7b) 9 1 2 2 0 2 5 10 3 7 2 0 17 1 6
Mixtral (47b) 11 1 0 4 0 0 3 6 9 9 0 0 15 9 0
Llama2 (7b) 11 1 0 4 0 0 16 1 1 9 0 0 24 0 0
Llama2 (13b) 11 1 0 4 0 0 16 1 1 9 0 0 23 1 0
Gpt3.5 10 2 0 4 0 0 13 4 1 9 0 0 21 3 0
Table 1
Results for the experiments testing correctness w.r.t. axioms in the ontologies. Labels T, F and U mean
‘true’, ‘false’ and ‘unknown’ responses count. We indicate the number of parameters in each model in
parenthesis (e.g. Mistral has 7 billion). It is not known the number of parameters of GPT 3.5.
Animals University Generations Football Cell
T F U L T F U L T F U L T F U L T F U L
14 2 4 2 5 1 2 0 10 27 5 2 9 3 0 0 18 1 5 0
18 2 0 0 8 0 0 0 19 13 10 0 12 0 0 0 17 7 0 0
20 0 0 0 8 0 0 0 40 1 1 1 12 0 0 0 24 0 0 0
18 2 0 1 7 1 0 0 35 6 1 4 11 1 0 1 21 3 0 0
20 0 0 0 7 1 0 0 36 5 1 0 12 0 0 0 18 6 0 0
Table 2
Results for the experiment testing logical consistency. The number of parameters of each model and the
meaning of T, F, U are as in Table 1. L stands for logical inconsistencies (an axiom answered as ‘false’ or
‘unknown’ which can be inferred from the set of the axioms answered as True, see Section 2). Models’
names omitted for better readability (they are the same of Table 1).
Animals University Generations Football Cell
A P R A P R A P R A P R A P R
0.87 0.52 0.72 0.57 0.67 0.5 0.84 0.71 0.23 0.74 0.44 0.65 0.65 0.48 0.81
0.89 0.57 0.69 0.57 0.48 0.92 0.82 0.64 0.66 0.72 0.43 0.76 0.7 0.32 0.64
0.51 0.2 1 0.24 0.24 1 0.4 0.22 0.88 0.21 0.21 1 0.27 0.18 1
0.73 0.31 0.94 0.45 0.3 0.92 0.63 0.32 0.74 0.44 0.26 0.88 0.44 0.21 0.91
0.71 0.3 1 0.69 0.44 1 0.74 0.41 1 0.68 0.4 1 0.61 0.28 0.91
Table 3
Results for the experiments testing negative examples. Labels A, P and R mean ‘Accuracy’, ‘Precision’ and
‘Recall’ respectively [10]. Models’ names omitted for better readability (they are the same of Table 1).
We use a total of 5 LLMs: Open AI’s GPT 3.5 Turbo [11], Mistral [12], Mixtral [13] and two Llama
2 [14] models (we use Ollama’s API3 ). Both Mistral and Mixtral are open models. Llama 2 is free of
charge for research while GPT can be expensive as it charges for each query4 .
For each logical axiom in an ontology we generate a membership query to an LLM using the
Manchester OWL syntax. The goal is to test how well an LLM can correctly answer to membership
queries on different domains and without any fine-tuning, where ‘correctly’ means that it answers ‘true’
for the axioms in the ontology (even though ontologies may not match with the real world, we expect
them to be mostly correct). The results are in Table 1.
We generate all the inferred axioms using the HermiT [15] reasoner (as mentioned above, the logical
closure of the ontologies is finite) and we repeat the experiments with the new ontologies. Probing
3
https://github.com/ollama/ollama
4
The source code of the experiments is publicly available https://github.com/MatteoMagnini/ExactLearner
the LLMs on ontologies with inferred axioms is useful to test logical consistency. While it is possible
that the LLMs could have seen these ontologies during their training (since they are available online),
it is unlikely that this is the case for the inferred axioms, since they are not explicitly present in the
ontologies. The results are in Table 2.
We perform a third experiment where we actively learn ontologies by means of a naive learning
algorithm where all concept inclusions of the form 𝐴 ⊑ 𝐵 with 𝐴, 𝐵 concept names in a given signature
are asked (the ontologies have complex ℰℒ concepts, but in this experiment we only considered concept
names to reduce the number of membership queries). The results are in Table 3. We applied the
Chi-squared test to check the relationship between the answers of the LLMs and the ontologies, with
the null hypothesis being that there is no correlation. We rejected the null hypothesis in every case
(p-value lower than 0.05) except the ones in yellow. Mistral/Mixtral were competitive with GPT 3.5
and had better performance in comparison with the Llama 2 models. The LLMs performed well on
ontologies with general knowledge (e.g., Animals, Generations). As future work, we would like to build
on these first results and extend the experiments to larger ontologies. Moreover, we plan to investigate
the task of actively learning ontologies from LLMs using the ExactLearner [6].
Acknowledgements
Ana Ozaki is supported by the Research Council of Norway, project number 316022.
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