Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study
This paper presents a zero-shot multi-label topic classification framework enhanced with per-article knowledge graphs, and systematically evaluates eight methods across fifteen LLMs and eight datasets. Keyword-enhanced classification (AK) is the best base method. Graph augmentation helps small models but hurts large ones, indicating large models already contain sufficient relational knowledge. Self-consistency decoding shows no benefit while increasing computation costs fivefold.
[2605.30465] Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study
[Submitted on 28 May 2026]
Title:Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study
View a PDF of the paper titled Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study, by Shahana Akter and 3 other authors
View PDF HTML (experimental)
Abstract:Multi-label topic classification without labeled training data is a challenging task, specially when documents contain complex relational information. We present a zero-shot multi-label topic classification framework and systematically investigate how per-article knowledge graph augmentation affects its performance. The base framework classifies topics in documents without labeled training data and has four variants: article-only classification, keyword-enhanced classification, and self-consistency decoding variants of both. Then, we augment each base variant with per article knowledge graph. This graph is extracted from the input document through a pipeline similar to KGGen based on subject-predicate-object triples. We test all eight methods, four base and four graph augmented on fifteen LLMs and eight multi-label datasets across different domains. For the base framework, keyword-enhanced classification (AK) is the best performing method, and six out of fifteen LLMs surpass the sentence-encoder baseline. Graph augmentation has positive and negative impacts on small and large models, respectively. This shows that larger models already contain enough relational information from pretraining. Furthermore, the self-consistency decoding variant does not show performance improvements in any experiment while increasing computation costs about fivefold.
Comments: 15 pages, 1 figure, ACL format. This paper proposes a KG-augmented zero-shot multi-label topic classification framework and evaluates multiple strategies
Subjects:
Computation and Language (cs.CL)
ACM classes: I.2.7; I.2.6; H.3.3
Cite as: arXiv:2605.30465 [cs.CL]
(or arXiv:2605.30465v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.30465
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shahana Akter [view email] [v1] Thu, 28 May 2026 18:39:05 UTC (230 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study, by Shahana Akter and 3 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CL
new | recent | 2026-05
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)