=Paper=
{{Paper
|id=Vol-3759/workshop4
|storemode=property
|title=Scaling Scientific Knowledge Discovery with Neuro-Symbolic AI and Large Language Models
|pdfUrl=https://ceur-ws.org/Vol-3759/workshop4.pdf
|volume=Vol-3759
|authors=Wilma Johanna Schmidt,Diego Rincon-Yanez,Evgeny Kharlamov,Adrian Paschke
|dblpUrl=https://dblp.org/rec/conf/i-semantics/SchmidtRKP24
}}
==Scaling Scientific Knowledge Discovery with Neuro-Symbolic AI and Large Language Models==
https://ceur-ws.org/Vol-3759/workshop4.pdf
Scaling Scientific Knowledge Discovery with
Neuro-Symbolic AI and Large Language Models
Wilma Johanna Schmidt1,4 , Diego Rincon-Yanez2,6 , Evgeny Kharlamov1,4 and
Adrian Paschke3,5
1
Bosch Center for AI, Robert Bosch GmbH, Renningen, Germany
2
University of Salerno, Fisciano, Italy
3
AG Corporate Semantic Web, Freie Universität Berlin, Berlin, Germany
4
SIRIUS, Centre for Scalable Data Access, University of Oslo, Oslo, Norway
5
Data Analytics Center, Fraunhofer FOKUS, Berlin, Germany
6
Universidad de Santander, Facultad de Ingenierías y Tecnologías, Cucuta, Colombia
Abstract
The increasing amount of available research data leads to the need to scale scientific knowledge discovery,
e.g., the conduction of systematic literature reviews (SLRs), to keep up with fast developments in research and
further support decision-making in the industry. AI-based methods are gaining importance in these tasks and
have been integrated into many SLR tools. Yet, several challenges are still open on applying especially neural
methods on scientific knowledge discovery tasks. To address this, we evaluate various neural and neuro-symbolic
scenarios on a specific generative writing task. While confirming existing concerns on pure Large Language
Model (LLM) approaches for these tasks, we obtain a heterogeneous picture of Retrieval-Augmented Generation
(RAG) approaches. The most promising candidate is a Knowledge Graph (KG) based context-enhanced LLM
approach for Knowledge Discovery.
Keywords
Neuro-Symbolic AI, Knowledge Graph, Large Language Model, Retrieval-Augmented Generation (RAG), System-
atic Literature Review
1. Introduction
Recent AI approaches are drastically impacting solutions and the ways of working in several industries at
an additional fast-ongoing development pace. Yet, at least two trends are expected to remain predictable:
(i) the high, even increasing need for fast decision-making and (ii) the continuously increasing amount
of available data to make decisions. This is highly reflected in the growing research field of data-driven
decision-making.
Large language models (LLMs) are a novel generative AI approach that shows promising results
on various industrial challenges, yet LLMs tend to encounter limitations on reliability [1] and inter-
pretability [2] [1]. Fortunately, e.g., smart prompting techniques may "enhance the model’s ability to
explain their reasoning and justify their decision" [2]. With context-enhanced prompts, LLMs can be more
strongly guided toward suitable responses. The versatility and capability of LLMs mark a paradigm shift
in how we interact with machines, making these interactions more intuitive and resembling human-
like conversations. However, a notable challenge with LLMs is their occasional tendency to produce
information not rooted in reality or their training data, a phenomenon often termed "hallucinations"
[3] [1]. To mitigate these hallucinations, the concept of Retrieval Augmented Generation (RAG) has
arisen as the ability of the LLM to analyze text with the capacity to retrieve relevant information from
selected external sources; this enhances the accuracy and reliability of the produced answer.
On the other hand, neuro-symbolic AI, as a combination of neural and symbolic methods [4], positions
itself as a promising candidate for industrial applications[5]. One benefit of neuro-symbolic solutions
First International Workshop on Scaling Knowledge Graphs for Industry, co-located with 20th International Conference on Semantic
Systems (SEMANTICS) - Amsterdam, Sept. 17–19, 2024
$ Wilma.Schmidt@de.bosch.com (W. J. Schmidt); drinconyanez@unisa.it (D. Rincon-Yanez);
Evgeny.Kharlamov@de.bosch.com (E. Kharlamov); adrian.paschke@fu-berlin.de (A. Paschke)
0000-0002-8982-1678 (D. Rincon-Yanez); 0000-0003-3247-4166 (E. Kharlamov); 0000-0003-3156-9040 (A. Paschke)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
includes the integration of domain knowledge [6], e.g., in the form of Knowledge Graphs (KGs) [1].
Integrating KGs as a structured and symbolic knowledge representation into RAG-type applications
offers a powerful approach to addressing the challenge of reducing the hallucinations[7] by combining
the ability of language models to analyze text with the capability to retrieve relevant information from
external sources, such as knowledge bases.
Nowadays, it is virtually impossible to keep track of new research, considering the overload in
scientific publications worldwide [8]. Research needs to support the decision-making process at an
industrial scale, meaning the engineering of scientific knowledge and discovery that comes with the
necessity of analyzing a massive corpus of data. There are established methods in research that can
be applied for systematic analyses of a large landscape of publications, such as a Systematic Literature
Review (SLR) [9]. Yet, SLRs are time-consuming if conducted manually. AI methods have shown to be
effective for increasing efficiency, such as paper selection[10], yet recent research has not fully exploited
these capabilities [10]. Specifically, LLMs open up new steps to automate SLRs further with knowledge
representation and smart prompting. While some open challenges in scientific knowledge discovery are
addressed by AI-based techniques [10], neuro-symbolic approaches have not been explicitly assessed
on their potential and limitations in this field.
Considering this potential, this paper identifies the benefits and limitations of different approaches for
scientific knowledge discovery, specifically answering research questions of an SLR. We evaluate LLM-
based and neuro-symbolic, specifically document-based RAG and RDF-KG-based context-enhanced
LLM-based approaches. Additionally, a prompt engineering process was conducted based on different
neuro-symbolic approaches drafted as systematic experimentation scenarios.
Moreover, this work tackles the missing transparency on proprietary SLR tools with AI support ([2]
[10]); For this reason and unpredictability concerns, a GitHub repository1 with the used system and
user prompts was prepared including different specific scientific knowledge discovery questions and
the respective answers.
The further parts of this paper are structured as follows: we analyze and discuss research on the
status and open challenges of AI-supported SLRs in Section 2. We present our approach in Section 3. In
Section 4, we first describe the different scenarios of our experiment. Second, we show the obtained
results and analyze the benefits and limitations. After discussing open challenges on scaling scientific
knowledge discovery with neuro-symbolic AI in Section 5, we conclude in Section 6 and point to the
limitations of our work and future steps.
2. Related Work
This section shows relevant related work on scaling scientific knowledge discovery, with a focus on
neuro-symbolic AI.
One of the most prominent LLM challenges is hallucination reduction. An ML-oriented method to
solve this is fine-tuning, but this comes at a high cost in terms of time and effort [11]. It is possible to
develop a model that allows for the prediction of multiple tail or head entities for a given relation and
entity, leveraging the relevant neighbors of the entities[12]. This has resulted in improved efficiency and
effectiveness of LLMs in utilizing KG information in specialized or personalized domains. However, both
cases generate new challenges, such as increased costs due to the need for fine-tuning on LLMs, although
it is significantly lower than other methods since very specific, compressed, and previously validated
information is mapped. An additional challenge is the risk of information loss in the graphs due to the
difficulty in leveraging the most relevant neighbors because of the large number of connections a node
can have.
As one example of scientific knowledge discovery, SLRs have proven valuable. An SLR consists of
three main phases: planning, conduction, and reporting. De la Torre-López et al. [10] show in an SLR that
most AI-based support in automating SLRs is on the conduction phase of SLRs, specifically the task of
paper selection. Phase planning is semi-automated with traditional methods (see, e.g., [13] on duplicate
1
GitHub Repository - https://github.com/d1egoprog/KG-SLR4LLM
identification), and the reporting phase is commonly done manually. The authors see accordingly a gap
in more research on AI-driven writing tasks [10].
Bolaños et al. reviewed AI opportunities and challenges for literature reviews [2] by reviewing
existing SLR tools. The authors stress the importance of the research direction on integrating advanced
NLP technologies to replace possibly outdated methodologies in available SLR tools and the "promising
research direction" of "the use of semantic technologies [...]" particularly knowledge graphs, to enhance the
characterization and classification of research papers [2]. An interesting work on integrating advanced
NLP technologies by Jansen et al. employs LLMs in survey research [14]. The authors see "potential
advantages to using LLMs like ChatGPT for survey research to generate survey responses" and discuss
potential issues such as bias and lack of contextual understanding of LLMs. Our work addresses the
latter by evaluating neuro-symbolic approaches to knowledge injection.
Further work (e.g., [15] [16]) shows research interest in this field, yet still lacks research on neuro-
symbolic, e.g., RAG and Graph RAG, Memory-based, to improve the reporting phase in scientific
knowledge discovery.
Focused on the medical domain, Yun et al. [17] summarizes that "further research is warranted for
using LLMs for literature reviews in other domains as our study only focused on the task of writing medical
systematic reviews." While van Dinter et al. [18] extend the domain view in their work, the focus
still remained on the medical and computer science domain, leading to no SLRs evaluated from the
manufacturing domain.
In summary, the related work shows interest in the AI-support for scientific knowledge discovery.
The exploration focuses on SLRs as a method and general medical or computer science as a domain.
To the best of our knowledge, no SLR has been conducted manually and then challenged against LLM
capabilities in any way. Further, no AI-based support for SLRs started with a KG, but only on metadata
of publications or texts containing the respective content of a publication. With our work, we address
the previously mentioned gaps.
3. Building Neuro-Symbolic AI Frameworks for Scientific Knowledge
Discovery
In this section, we describe the underlying neuro-symbolic approaches and the architectural pattern
employed in our work’s neuro-symbolic scenarios.
In order to address scalability in the realm of scientific knowledge discovery, we evaluate different
approaches on the example of an SLR’s generative writing task. In addition to the human and LLM-based
responses to specific research questions, an evaluation of neuro-symbolic potentials and challenges
is needed. In this section, we describe a document-based RAG approach and a framework for an
RDF-KG-based context-enhanced LLM; these are the basis of the selected neuro-symbolic scenarios in
our experiments.
Figure 1: Neuro-Symbolic AI Enhancement approach for ingesting Knowledge Graphs into the LLM; NeuroSym-
bolic AI Architecture {d-K-s-M-d}, using the boxology notation [19]
Lewis et al. [7] introduce Retrieval-Augmented Generation (RAG) as the combination of "pre-trained,
parametric-memory generation models with a non-parametric memory through a general-purpose fine-
tuning approach". In our work, the RAG approach is based on an LLM for the parametric-memory model
based on a folder of text files for the non-parametric memory, see Figure 1. The LLM is executed in
scenarios with different GPT models from OpenAI2 .
2
https://platform.openai.com
The document base contains 49 text files of the final search corpus from a recently conducted SLR
[20]. Each text file was scrapped, and the text was extracted from the main publication website. With the
selected document base, a Knowledge Graph construction process was performed using the extracted
paper content and the paper metadata and a schema was assembled by leveraging existing ontologies
such as BIBO3 , SWRC4 , ORKG5 and others.
To test the RDF-KG-based context-enhanced LLM, see Figure 1, the public API of OpenAI was
employed, specifically on the GPT-4-turbo model. The KG includes entities from the 49 assessed
publications, authors, venues, and identified research fields. The complete publication list (49) can be
found in the GitHub repository6 ; as well as the assembled schema and the fully populated KG.
4. Scaling Knowledge Discovery with Knowledge Graphs and
Neuro-Symbolic AI
In this section, we describe the experimental framework 4.1 conducted, RAGs and RDF-KG-based
context-enhanced LLMs. We conclude with the results of our experiments 4.2.
4.1. Experimental Configuration
The evaluation was centered on evaluating two approaches on LLMs, RAGs (1) and RDF-KG-based
context-enhanced (2). The main goal is to scale scientific knowledge discovery as can be detailed in
Figure 2a. The performed evaluation was centered on the results of five research questions (a main
research question and an additional four) drafted for the selected document base. With the scope of
assessing the generative writing capabilities and knowledge discovery by leveraging research questions
of an SLR.
(a) RDF-KG-Based Context-Enhanced LLM (Scenario: S5); Zero-Shot (b) RAG (Scenarios: S2, S4); Zero-
Prompting Shot Prompting
Figure 2: High-Level Architecture View
To increase comparability between the LLM with no knowledge and the RAG-based approach, GPT-
3.5-turbo and GPT-4-turbo were employed in both scenarios, the scenario detail is listed in Table 1.
The approach on an RDF-KG-based context-enhanced LLM is conducted only on GPT-4-turbo. The
GPT-4-turbo serves as the basis for the evaluation across the neural and neuro-symbolic approaches.
Table 1
Model Information
Scenario Model Setup
S1 gpt-3.5-turbo temperature 0.5; zero shot
S2 gpt-3.5-turbo temperature 0.5; zero shot; 10 message sources
S3 gpt-4-turbo temperature 0.5; zero shot
S4 gpt-4 temperature 0.5; zero shot; 10 message sources
S5 gpt-4-turbo temperature 0.5; zero shot; five times 9-10 message sources in context, then
the summary of 5 responses in additional prompt
3
Namespace: http://purl.org/ontology/bibo/
4
Namespace: http://swrc.ontoware.org/ontology#
5
Namespace: http://orkg.org/core
6
https://github.com/wAIlma/SLR-NeSyAI-KGC-I40/data
In each scenario, five steps are undertaken, each of them addressing the research questions (RQ)
from [20]: (1) Which role play neuro-symbolic AI approaches in knowledge graph construction for Smart
Manufacturing? (Main RQ), (2) What are publication characteristics on neuro-symbolic AI in knowledge
graph construction for Smart Manufacturing? (RQ1), (3) In which steps of the knowledge graph construction
process are neuro-symbolic AI methods applied in Smart Manufacturing? (RQ2), (4) What are common
neuro-symbolic AI architectures in knowledge graph construction? (RQ3), and (5) For which manufacturing
use cases are knowledge graphs constructed with neuro-symbolic AI? (RQ4).
Considering that, the scenario 5 holds the model token constraint. Hence, the KG containing 49
documents is split into five SubKGs with a separate context, each, and asked to merge the five responses.
4.2. Evaluation
In this Section, we present the evaluation approach and the analysis of the results. The underlying
framework of all scenarios is shown in Figure 2. The conducted LLM-based and neuro-symbolic
scenarios are listed as follows:
1. LLM only: No further data provided. Scenarios: S1 and S3
2. Document-based RAG Files contain the retrieved text retrieved from the manually selected 49
publications. Scenarios: S2 and S5
3. RDF-KG-based context-enhanced LLM An RDF KG is provided as the context in addition to a
system prompt and user prompt to LLM. Scenario: S5
Considering the lack of gold standards for evaluating an LLM response, an evaluation model was
selected that reflects on the known weaknesses of LLMs and yet might not cover all requirements for
answering a scientific research question. The selected evaluation criteria were adapted from [14], each
with a score from 1 to 5, on the scenarios, see Table 2.
Table 2
Evaluation Criteria.
Id Criterion Description Score
Name
C1 Domain-specific Use of neuro-symbolic- and manufac- 1 (specific vocabulary not used or used
vocabulary turing domain-specific vocabulary in the wrong context) to 5 (specific vo-
cabulary correctly employed)
C2 Contextual Degree of “nonsensical or inappropriate 1 (completely inappropriate response)
understanding responses” to 5 (appropriate response)
(hallucination)
C3 Compelling mis- Share of “highly convincing text that is 1 (at least 50% of response is factu-
information factually wrong” ally wrong) to 5 (response is completely
true)
C4 Lack of trans- Degree of increasing transparency 1 (no or ineligible sources provided) to
parency caused by “disclosing LLM participation 5 (all relevant sources provided and all
and intractability of LLM training and cited in-text)
the text-generation process”
We show our results in Figure 3. Based on the results, we see that scaling scientific knowledge with
LLMs and improving this approach with RAGs is at an interesting yet not applicable level. On the one
hand, the responses vary significantly across scenarios and research questions. On the other hand,
scientific criteria are not met as hallucinations occur, and references are handled unreliably. In contrast,
we obtain promising results from the RDF-KG-based context-enhanced LLM. We discuss these specific
points in our next section 5.
5. Discussion
Overall, the responses across the different scenarios show a wide range from disappointing to promising
answers. Some responses (e.g. S2-RQ1, S2-RQ2) do not attempt an answer although the relevant context
Figure 3: Results on scenarios for scaling scientific knowledge discovery
is provided via text chunks and the LLM is trained on general knowledge to at least return a more
complex answer. On the contrary, one of the best answers (S4-RQ3) includes an outlook on evolutionary
knowledge which is not explicitly requested by the prompts. Underneath the variety, at least two
common flaws can be identified, that apply to all scenarios: (i) missing (references to) definitions and
(ii) missing tables, charts or figures to illustrate the statements.
We see on LLM-based and RAG-based scenarios severe challenges. With consistent system prompts
and varying research questions, the responses vary unexpectedly on several factors: (i) the reference
list (S1-RQ1 and S1-RQ4 contain no references at all), (ii) in-text citations (none provided by e.g. S4-RQ4)
and, (iii) whether the provided references are not made up (e.g. S1-RQ2 returns a template for references
with no actual values included). S2-mainRQ quotes directly from a provided source, yet omits quote
indication and citation. The RDF-KG-based context-enhanced LLM is a promising direction, yet it also
needs further improvement to ensure responses on a scientific level.
Neuro-symbolic approaches are one way of reducing hallucinations in LLMs. Our results show a
good performance of S1 and S4, yet a disappointing performance of S2. The RAG-based approach with a
GPT-3.5-turbo model (S2) describes neuro-symbolic AI as a combination of “merits of statistical learning
with semantical knowledge and reasoning”, omitting the neural perspective, which is crucial.
6. Conclusion
In summary, our work shows a promising neuro-symbolic approach of an RDF-KG-based context-
enhanced LLM for scaling scientific knowledge discovery. One further benefit of this approach is the
foundation for handling evolutionary knowledge. Via the KG the knowledge can be updated and made
available for future scientific queries to the LLM with minimal effort.
Our results show a need for caution when working with RAG-based approaches. Based on the overall
results, we see that scaling scientific knowledge with LLMs and improving this approach with simple
RAGs is not at an applicable level. On the other hand, scientific criteria are not met as hallucinations
occur, and references are treated unreliably. RDF-KG-based context-enhanced LLMs appear to be better
suited for this task based on our results, yet also require further improvements before being applicable.
Our experiment sheds light on scientific knowledge discovery from research data from the manufac-
turing domain yet is applicable to SLRs across industries.
Our work does not cover the whole area of scientific knowledge discovery, omitting, e.g., paper
selection tasks in SLR or expert interviews as approaches.
Lastly, token processing is a costly parameter. As a research paper may contain about ten thousand
tokens, processing a large data corpus quickly runs into a token issue. Smart prompting and suitable
neuro-symbolic architectures are needed to address this.
In future work, we plan to evaluate different parameter configurations, especially temperature and
number of message sources on RDF-KG-based context-enhanced LLMs.
Acknowledgements
We want to thank Valentin Knappich and Cem Akdag for their helpful support and insights during our
work.
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