KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
Authors: Jiho Kim
Yeonsu Kwon
Yohan Jo
Edward Choi
Venue: EMNLP 2023 Findings (2023)
Keywords: Large Language Model
Knowledge Graph
Reasoning
Question Answering
Fact Verification
TL;DR: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models

Abstract: While large language models (LLMs) have made considerable advancements in understanding and generating unstructured text, their application in structured data remains underexplored. Particularly, using LLMs for complex reasoning tasks on knowledge graphs (KGs) remains largely untouched. To address this, we propose KG-GPT, a multi-purpose framework leveraging LLMs for tasks employing KGs. KG-GPT comprises three steps: Sentence Segmentation, Graph Retrieval, and Inference, each aimed at partitioning sentences, retrieving relevant graph components, and deriving logical conclusions, respectively. We evaluate KG-GPT using KG-based fact verification and KGQA benchmarks, with the model showing competitive and robust performance, even outperforming several fully-supervised models. Our work, therefore, marks a significant step in unifying structured and unstructured data processing within the realm of LLMs.

KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
Jiho Kim', Yeonsu Kwon', Yohan Jo Edward Choi 'KAIST Seoul National University
(jiho.kim yeonsu. k, edwardchoi j@kaist. ac.kr
Abstract
Structured data, particularly in the form of knowledge graphs (KGs), serves as a reservoir of in- teroonneceed factual information and associations, articulated through nodes and edges. The inherent structure of KGs offers a valuable resource that can assist in executing complex reasoning tasks, like multi-hop inference. Even with these advan- tages, to the best of our knowledge, there is no general framework for performing KG-based tasks (e.g. question answering, fact verification) using auto-regressive LLMs.
While large language models (LLMs) have made considerable advancements in under- standing and generating unstructured text, their application in structured data remains underex- plored. Particularly. using LLMs for complex reasoning tasks on knowledge graphs (KGs) remains largely untouched. To address this, we propose KG-GPT, multi-purpose frame- work leveraging LLMs for tasks employing KGs. KG-GPT comprises three steps: Sentence Segmentation, Graph Retrieval, and Inference, each aimed at partitioning sentences, retriev- ing relevant graph components, and deriving logical conclusions, respectively. We evalu- ate KG-GPT using KG-based fact verification and KGQA benchmarks, with the model show- ing competitive and robust performance, even outperforming several fully-supervise models. Our work, therefore, marks a sigmificant step in unifying structured and unstructured data processing within the realm of LLMss!
To this end, propose a general frame- work, called KG-GPT, that uses LLMs' reasoning capabilities to perform KG-based tasks. KG-GPT similar to StruetGPT (Jiang et al., 2023) in that both reason on structured data using LLMs. How- ever, unlike StructGPT which identifies paths from seed entity to the final answer entity within KGs, KG-GPT retrieves the entire sub-graph and then infers the answer. This means KG-GPT can be used not only for KGQA but also for tasks like KG-based fact verification.
1 Introduction
KG-GPT consists of three steps: 1) Sentence (Claim Question) Segmentation, 2) Graph Re- trieval, and 3) Inference. During Sentence Seg- mentation, a sentence partitioned into discrete sub-sentences, each aligned with a single triple (i.e. [head, relation, tail]). The subsequent step, namely Graph Retrieval, retrieves potential pool of rela- tions that could bridge the entities identified within the sub-sentences. Then, a candidate pool of evi- dence graphs (i.e. sub-KG) is obtained using the retrieved relations and the entity set. In the final step. the obtained graphs are used derive logi- cal conclusion, such as validating given claim answering a given question.
The remarkable advancements in large language models (LLMs) have notably caught the eye of scholars conducting research in the field of natural language processing (NLP) (Brown et al., 2020; Chowdhery et al., 2022; OpenAI, 2023a,b; Anil et al., 2023). In their endeavor to create LLMs that can mirror the reasoning capabilities inherent humans, past studies have primarily centered their attention on unstructured textual data. This includes, but is not limited to, mathematical word problems (Miao et al., 2020; Cobbe al., 2021; Patel et al., 2021), CSQA (Talmor et al., 2019), and symbolic manipulation (Wei et al.. 2022). While significant strides have been made in this area, the domain of structured data remains largely unex- plored.
To evaluate KG-GPT, we employ KG-based fact verification and KGQA benchmarks, both de- manding complex reasoning that utilizes structured knowledge of KGs. In KG-based fact verification, we use FACTKG (Kim et al., 2023), which includes
'Code is available at https: /ttps://githuue wttpll..ooooooaoaaae KG-GPT.

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Sentence Segmennaion
Sentence (Claim/ Question)
Sub-senience Anders Entity Set ('Wiliia_AAdee Anders", "AFIT, M.S. 1962"]
William Anders received an MS from ulma Muter, AFIT, 1962 served arti frcificial satellite along with Frank Borman.
an
Sub-sentence
artificial
Entlit Set ('William, Anders artificial satellite]
LLM
Entity ('William Anders', "AFIT, M.S. 1962", 'Fann Borman']
Sub-sentence 3 Trunk Borman served as crew member of an urtificial satellite. Entity Set ('Trank Borman", artificial satellite]
Extract relation candidates
DOpedia
2) Graph Retrieval
Relation candidates of Sub-sentence I callege deathPiace
Top relations
alaatateer
Suub Aontene
collage
fnvvrrratty
Relation candidates Sub-gentence 2
Top relations
czew2Up
alnaNalse coea2llp
Suh-spitenee
Furael kyp-
crew11pp
Entiy
Input senienee
SUPPORTED
crevlUp
relations
crewtember
Set
Suub sonittt
LLM
Groph Retrieal
LLM
Relation candidates sun-ennte
relations crevlUp
Lo0a:
lage
crew2Up
GT9N lip
crewWember
Figure 1: An overview of KG-GPT. The framework comprises three distinct phases: Sentence Segmentation, Graph Retrieval, and Inference. The given example comes from FACTKG. It involves a 2-hop inference from 'William Andders'ne'ss FFrank Borman', requiring verification through evidence graph consisting of three triples. Both "William_Ander "AFIT, M.S. 1962" acts as leaf node. Moreover, artificial satellite represents the Type information absent from the provided entity set.
and Drran_BBrman' serve internal nodes in DBpedia (Lehmann et al., 2015), while
various graph reasoning patterns, and KG-GPT shows competitive performance compared to other fully-supervised models, even outperforming some. MetaQA (Zhang et al., 2018),
ing the in-context learning methodology to max- imize the LLM's reasoning ability. The prompts used for each stage are in Appendix A.
In KGQA, QA dataset composed of 1-hop, 2-hop, and 3-hop inference tasks. KG-GPT shows performance com- parable fully-supervised models. Notably, the performance does not significantly decline with the increase in the number of hops, demonstrating its robustness.
2.1 Sentence Segmentation
Many KG-based tasks require multi-hop reasoning. To address this, we utilize Divide-and-Conquer approach. By breaking down sentence into sub- sentences that correspond a single relation, iden- tifying relations in each sub-sentence becomes cas- ier than finding n-hop relations connected to an entity from the original sentence all at once.
2 Method
We assume S can be broken down into sub- sentences: S1, S2, .... Sn where S; consists of a set of entities & C and a relation r; E R. Each e() & can be concrete entity (e.g. William An- ders in Fig. 1-(1)),
KG-GPT is composed of three stages: Sentence Segmentation, Graph Retrieval, and Inference, as described in Fig.
We assume a graph G (knowledge graph con- sisting of entities & and relations R), a sentence S (claim question), and all entities involved in S. Es C & are given. In order derive logical conclusion, we need an accurate evidence graph GE 9, which we obtain two stages, Sentence Segmentation and Graph Retrieval. Furthermore, all the aforementioned steps are executed employ-
type (e.g. artificial satellite in Fig. 1-(1)). T3 can be mapped one more items in R, as there can be multiple relations with similar semantics (e.g. birthPlace, OlaceOfBirth).
2.2 Graph Retrieval
To effectively validate a claim or answer a question, crucial to obtain an evidence graph (i.e. sub-

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KG) that facilitates logical conclusions. In this stage, we first aim to retrieve the corresponding relations for each sub-sentence S to extract Gg- For each S,, we use the LLM to map T; to one or more items in R as accurately as possible. To do so, we first define R. C R. which is a set of relations connected to all es" E & according to the schema of (i.e. relation candidates in Fig. 1-(2)). This process considers both the relations connected a specific entity and the relations associated with the entity's s type in G. We further elaborate on the process in Appendix B. Then, we feed S; and R, to the LLM to retrieve the set of final top-K relations R. In detail, relations in R2 are linearized (e.g- llocation, birthYear, ... birthDate) and combined with the corresponding sub-sentence S, to estab- lish prompts for the LLM and the LLM generates R1,A = (r: ................................ (k) oulput. In the final graph retrieval step, we can obtain GR, made up of all triples whose relations come from Ri, and whose entities come from & across all S..
reasoning that represent the intrinsic characteris- tics of the KG; One-hop, Conjunction, Existence, Multi-hop, and Negation. To further enhance its practical use, FACTKG integrates claims in both colloquial and written styles. Examples of claims from FACTKG can be found in Appendix D.
(f)
3.1.2 MetaQA
MetaQA (Zhang et al., 2018) is a carefully curated dataset intended to facilitate the study of question- answering that leverages KG-based approaches in the field of movies. The dataset encompasses over 400K questions, including instances of 1-hop, 2- hop, and 3-hop reasoning. Additionally, covers a diverse range of question styles. Examples of ques- tions from MetaQA can be found in Appendix D.
3.2 Baselines
For evaluation on FACTKG, we use the same base- lines as in Kim et al. (2023). These baselines are divided into two distinet categories: Claim Only and With Evidence. In the Claim Only setting, the models are provided only with the claim as their input and predict the label. For this setting, in ad- dition to the existing baselines, we implement 12-shot ChatGPT (OpenAI, 2023b) baseline. In the With Evidence scenario, models consist of an evidence graph retriever and claim verification model. We employ the KG version GEAR (Zhou et al., 2019) as a fully supervised model.
2.3 Inference
Then, we feed S and GE to the LLM to derive a logical conclusion. In order to represent GE in the prompt, we linearize the triples associated with GE (i.e. [lheadi. rel,, tailj], .... Uheadm. relm- Il), and then concatenate these linearized triples with the sentence S. In fact verification, the determination of whether S is supported or refuted is contingent upon Gg. In question answering, the LLM identifies an entity in GE as the most probable answer to S.
In our exploration of MetaQA, we use a selection of prototypical baselines well-known in the field of KGQA. These include models such KV-Mem (Xu et al., 2019), GraftNet (Sun al., 2018), Em- bedKGQA (Saxena et al., 2020), NSM (He et al., 2021), and UniKGQA (Jiang et al., 2022), which operate in a fully supervised fashion. Additionally, we implement a 12-shot ChatGPT baseline.
3 Experiments
We evaluate our framework on two tasks that re- quire KG grounding: fact-verification and question- answering. A detailed description of experimental settings can be found in Appendix C.
Results & Analysis
3.1 Dataset
4.1 FACTKG
3.1.1 FACTKG
We evaluated the models' prediction capability for labels (i.e. Supported or Refuted) and presented the accuracy score in Table 1. As result, KG-GPT outperforms Claim Only models BERT, BlueBERT, and Flan-T5 T5 with performance enhancements of 7.48% 12.75%, and 9.98% absolute, respectively. It also outperforms 12-shot ChatGPT by 4.20%. These figures emphasize the effectiveness of our framework in extracting the necessary evidence for claim verification, highlighting the positive impact
FACTKG (Kim et al., 2023) serves as a practical and challenging dataset meticulously constructed for the purpose of fact verification, employing knowledge graph for validation purposes. It encom- passes 108K claims that can be verified via DBpe- dia (Lehmann et al., 2015), which is one of the available comprehensive knowledge graphs. These claims are categorized as either Supported or Re- futed. FACTKG embodies five diverse types of

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Training
MetaQA 1-hop
MetaQA
MetaQA
Input Type
Strategy
Methods
Accuracy
Stage
FactKG
2-hop
3-hop
Sentence
BERT
65.20
39
63
100
full
Segmentation
BlueBERT
59.93
Claim Only
Groph Retrieval
17
4
3
0
zero-shot
Flan-T5
62.70
Inference
44
93
34
0
12-shot
ChatGPT
68.48
full
GEAR
77.65
12-shot
72.68
Table 3: Number of errors from 100 incorrect samples
With Evidence
8-shat
KG-GPT
67.68
across each dataset.
4-shot
59.53
Table 1: The performance of the models on FACTKG. Except for ChatGPT and KG-GPT, all performances are obtained from Kim et al. (2023).
Interestingly, the performance of KG-GPT closely matches that of fully-supervised model. Particularly, it surpasses KV-Mem by margins of 0.1%, 11.7%, and 45.1% across three distinct tasks respectively, signifying its superior performance. While the overall performance of KG-GPT is simi-
Training Strategy
MetaQA
MetaQA
MetaQA
Methods
I-hop
2-hop
3-hop
KV-Mem
96.2
82.7
48.9
GraftNet
97.0
94.8
77.7
lar to that of GraftNet, noteworthy difference is pronounced in the 3-hop task, wherein KG-GPT outperforms GraftNet by 16.3%. The qualitative results including the graphs retrieved by KG-GPT are in Appendix E.2.
full
EmbedKGQA
97.5
98.8
94.8
NSM
97.1
99.9
98.9
UniKGQA
97.5
99.0
99.1
12-shot
ChaiGPT
60.0
23.0
38.7
12-shot
96.3
94.4
8-shot
KG-GPT
95.8
93.8
68.8
4-shot
94.7
92.8
46.6
4.3 Error Analysis
In both FACTKG and MetaQA, there are cor- responding ground truth graphs containing seed entities. This absence makes quantitative step-by- step analysis challenging. Therefore, we carried out an error analysis, extracting 100 incorrect sam- ples from each dataset: FACTKG, M MetaQA-2hopp and MetaQA-3hop. Table 3 shows the number of errors observed at each step. No- tably, errors during the graph retrieval phase are the fewest among the three steps. This suggests that once sentences are correctly segmented, iden- tifying relations within them becomes relatively easy. Furthermore, comparative analysis between MetaQA- 1hop, MetaQA 2hop, and MetaQA-3hop indicates that as the number of hops increases, so does the diversity of the questions. This heightened diversity in turn escalates the errors in Sentence Segmentation.
Table 2: The performance of the models on MetaQ (Hits@11 1). The best results for each task and those of 12-shot KG-GPT are in bold.
of the sentence segmentation and graph retrieval stages. The qualitative results including the graphs retrieved by KG-GPT are in Appendix E. Nonetheless, when compared to GEAR, fully supervised model built upon KGs, KG-GPT ex- hibits certain limitations. KG-GPT achieves curacy score of 72.68%, which is behind GEAR's 77.65%. This performance gap illustrates the ob- stacles encountered by KG-GPT in a few-shot see- nario, namely the difficulty in amassing a suffi- cient volume of information from the restricted data available. Hence, despite the notable progress achieved with KG-GPT, there is clear room for im- provement to equal or surpass the performance of KG-specific supervised models like GEAR.
4.4
Ablation Study
4.4.1 Number of In-context Examples
4.2 MetaQA
The results for the 12-shot, 8-shot, and 4-shot from the FACTKG and MetaQA datasets are re- ported in Table and Table 2, respectively. Though there predicted improvement in performance with the increase in the number of shots in both FACTKG and MetaQA datasets, this was not uni- formly observed across all scenarios. Notably, MetaQA demonstrated superior performance, ex-
The findings on MetaQA are presented in Table 2. The performance of KG-GPT is impressive, scoring 96.3%, 94.4%, and 94.0% on 1-hop. 2-hop, and hop tasks respectively. This demonstrates its strong ability to generalize from a limited number ex- amples, a critical trait when handling real rorl-world applications with varying degrees of complexity.

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ceeding 90%, in both the 1-hop and 2-hop scenar- ios, even with minimal set of four examples, In contrast, both the FACTKG and MetaQA 3-hop scenarios, the performance of the 4-shot learning scenario was similar to that of the baselines which did not utilize graph evidence. This similarity sug- gests that LLMs may struggle to interpret com- plex data features when equipped with only four shots. Thus, the findings highlight the importance of formulating in- in-context examples according to the complexity of the task.
sufficient low-quality examples. Secondly, de- spite its impressive performance in fact-verification and -answernnn tasks, KG-GPT still lags behind fully supervised KG-specific models. The gap in performance highlights the challenges faced by KG-GPT in few-shot learning scenario due to limited data. Future research should focus on op- timizing language models leveraging KGs to over- come these limitations.
Acknowledgements
4.4.2 Top-K Relation Retrieval
This work was supported by Institute of Informa- tion & Communications Technology Planning & Evaluation (ITP) grant (No.2019-0- 00075), Na- tional Research Foundation of Korea (NRF) grant ANRF-2020111532200110000
Table 11 shows the performance according to the value of k in FACTKG. As a result, performance did not significantly vary depending on the value of k. Table 12 illustrates the average number of triples retrieved for both supported and refuted claims, de- pending on k. Despite the increase in the number of triples as the value of k grows, it does not im- pact the accuracy. This suggests that the additional triples are not significantly influential.
and the Korea Health Industry Development Institute (KHIDI) grant (No.HR21C0198), funded by the Korea gov- ermment (MSIT, MOHW).
References
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Conclusion
We suggest KG-GPT, versatile framework that utilizes LLMs for tasks that use KGs. KG-GPT divided into three stages: Sentence Segmentation, Graph Retrieval, and Inference, each designed for breaking down sentences, sourcing related graph elements, and reaching reasoned outcomes, respec- tively. We assess KG-GPT's efficacy using KG- based fact verification and KGQA metrics, and the model demonstrates consistent, impressive re- sults, even surpassing a number of fully-supervised models. Consequently, our research signifies a sub- stantial advancement in combining structured and unstructured data management in the LLMs' con- text.
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A Prompts
B.2 MetaQA
For the n-hop task MetaQA, R₁ is constructed from the relations within n-hops from the seed entity.
The prompts for Sentence Segmentation, Graph Retrieval, and Inference can be found in Table 4, Table 5 and Table 6, respectively.
C Experimental Settings
B Relation Candidates Extraction Algorithm
We utilize ChatGPT² (OpenAI, 2023b) across all tasks, and acquire more consistent responses, we carry out inference with the temperature and top_p parameters set to 0.2 and 0.1, respectively. For each stage of KG-GPT, 12 pieces of training samples were made into in-context examples and added to the prompt. In FACTKG, there over 500 existing relations (RR) 500), so set l for Top-K relation retrieval. Conversely, in MctaQA. there are only 9 existing relations [ 9), so we set l:
B.1 FACTKG
In FACTKG, we develop a new KG called Type- DBpedia. This graph comprises types found in DB- pedia and connects them through relations, thereby enhancing the usability of KG content. We describe the detailed process of incorporating R, using DB- pedia and TypeDB in Algorithm 1.
We denote the entities E1 and Eg because the sub-sentence always includes two entities in FAC- TKG. Relations (e, peddia) represents the of relations connected to e in Bpedia. Similarly, Relations (T, Type T Bpedia) represents the set of relations connected to T Type Bpedia.
D Data Examples
Examples of data from FACTKG and MetaQA be found in Tables and 8, respectively.
E
Qualitative Results
Algorithm 1: Extract Relation Candidates
E.1 FACTKG
Input: Entity Set E E,, E2}. DBpedia, Type Bpedia Output: Relation Candidates R4
Table 9 includes the graphs retrieved by KG-GPT, along with the prediction results, for five different claims.
Initialization: T = 0, 0, Rr = 0, Rg = D,
R² 0
E.2 MetaQA
for each entity in E do if isType (e) then T (e)
Table 10 includes the graphs retrieved by KG-GPT, along with the prediction results, for nine different questions.
end
else
F Top-K Relation Retrieval
if' isEpty (Rg) then Rg + Relations (e, DBpedia)
F.1
FACTKG
end
The performance and the average number of re- trieved triples are depicted in Table 11 and Table 12, respectively.
else
RE + Rg n Relations (e. D Dpedda)
end
F.2 MetaQA
end
The performance and the average number of re- trieved triples are depicted in Table 13 and Table 14, respectively.
end
not isEmpty (T) then
RT Relations (T.
R4 Rg n Ry
end
else
Rd <- R
end
http:://paatooooooo.cccmmmmeeggp completions-api

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Sentence Segmentation Prompl
Please divide the given sentence into several sentences each of which can be represented by one triplet. The generated sentences should be numbered and formatted as follows: #(number). (sentence), (entity set). The entity for each sentence should contain no more than two entities, with each entily being used only once in all statements. The '#HI' symbol should be used indicate entity In the generated sentences, there cannot be more than entities in the entity set. (i.c., the number of ## must not be larger than two.)
Examples)
Sentence A: Ahmad Kadhim Assad's club Al-Zawra'a SC.
Entity set: ['Ahmad Modhim_Assann #fr "Al-Zawra'a_ a_S""
->Divided:
1. Ahmad Kadhim Assad's club is Al-Zawra'a a SC., Entity set: ['Ahmad Kadhim_Assad" ## "Al-Zawra'a_S" "_S"""
Sentence L: An academic journal with code UPHDE also Acta Math. Hungar.
Entity set: ["Acta Math. Hungar." ## "[JPHDE"] ->Divided:
1. An academicc journal is with code UPHDE. Entity set: ('academic journal" ## "UJPDEE"
academic journal is also Acta Math. Hungar., Entity set: l'academic journal' "Acta Math. Hungar.]
Your Task)
Sentence: <<<<<LAIMM>>>
Entity set: BEIK<ENTITY_SET>>>0
DDivided:
Table 4: Sentence Segmentation Prompt.
Relation Retrieval Prompt
will give you set of words.
the top <<<<TOP K>s>>elements from Words set which are most semantically related to the given sentence. You may select up <<<<TOP K>ssowords. If there is nothing that looks semantically related, pick out any Cec<TTP_K>>>>oloee and give them to me.
Examples)
Sentence A: Ahmad Kadhim Assad's club is Al-Zawra's a SC. Words set: ['club', 'clubs', 'parent', 'spouse", 'birthPlace', 'deathYear', 'leaderName', 'awards', 'award', 'vccepresiden'', 'vicePresident']
Top Answer: ['club', 'clubs']
Sentence L: An academic journal with code IJPHDE is also Acta Math. Hungar. Words set: I'abbreviation', 'placeOfBirth', 'owner', 'coden', 'almaMater', 'dean', 'coach', "writer', 'firstAired", 'director', 'formerTeam' 'starring', 'birthPlace'] Top 2 Answer: ['abbreviation', 'coden']
Now let's find the top <<<<TOP K>s>selements.
Sentence: SCcESENTENCESsS>
Words set: CERERRLAATONSSTTEE
Top <<<<TOP Kesser
Table 5: Relation Retrieval Prompt. The prompt is used when retrieving relation retrieve graph.

---

Inference Prompt
You should verify the claim based on the evidence set. Each evidence is in the form of [head, relation, tail] and it means "head's relation is tail.".
Verify the claim based on the evidence set. (True means that everything contained in the claim is supported by the evidence.)
Please note that the unit is not important. (c.g. "98400" is also same as 98.4kg) Choose one of (Tree, False), and give me the one-sentence evidence.
Examples)
Claim A: Ahmad Kadhim Assad's club is Al-Zawra's a SC.
Evidence set: (''Ahamad_Kadhinee 'clubs', "Al-Zawra" SC"]]
Answer: True, based on the evidence set, Ahmad Kadhim Assad's club is Al-Zawra'a a SC.
Claim L: The place, designed by Huseyin Butuner and Hilmi Guner, is located in a country, where the leader is Paul Nurse.
Evidence set: [["Baku_Turkish__ T "Masss_Memm",,'," 'designer', "Hüseyin Bütüner and Hilmi Güner"), ["Baku_Turkee Turkish_ T Martyrs''Memoriall _emoorial", 'location', 'Azerbaijan']]
Answer: False, there no evidence for Paul Nurse.
Now let's verify the Claim based on the Evidence set. Claim: <<<<CLAIM>>>>
Evidence set: <<<<EVIDENCE_SET.S.
Answer:
Table Inference Prompt.
Reasoning Type
Claim Example
Graph
F'2
One-hop
AIDAstella was built by Meyer Werft.
TTC
AIDA Cruise line operated the AIDAstella which was built by Meyer Werft.
Conjunction
Existence
Meyer Werft had parent company.
m
Multi-hop
AIDAstella was built by a company Papenburg.
F22
T4
Negation
AIDAstella was not built by Meyer Werfi in Papenburg.
m
Table 7: Five different reasoning types of FACTKG. ri parentCompany, shipBuilder, r3: shipOperator, location, m: Meyer Werft, s: AlDAstella, e AIDA Cruises.

---

Task
Question Examples
1. what does [Helen Mack] star in?
1-hop
2. what is the main language in IKarate-Robo Zaborgar]?
3. who is the writer of [Boyz the Hood]?
1. who are movie co-directors of [Delbert Mann]?
2-hop
2. what genres do the films starred by [AI St. John] fall under?
3. which films share the screenwriter with [King Arthur]?
1. the films that share directors with the film [Catch Me You Can] were in which languages?
3-hop
2. who are the directors of the movies written by the writer of [She]?
3. when did the movies release whose actors also appear in the movie [Operator 131?
Table 8: Question examples from MetaQA.
Type
Claim
Retrieved Graph
Prediction
location, 'India"].
Yes, Agra Airport is located in India
('India', leader, M.ddi" ['India', leaderName,, "Narendra_ SaarendraMModii birthPlace, 'India']
Conjunction
Supported
where the leader is Narendra Modi.
|103_ _el__Co__movvvie location, 'Birmingham'J,
wasn't It aware that 103 Colmore Row, Conjunction Incated in Birmingham, with 23 floors, was completed 1976.
103_CClmore___ floorCount, "23").
Supported
['103_ [o3_CColore_Rovee completionDate,, "1976"), ['103 Colmore Row', buildingEodDatee "1976"]
('Alfredo Zitarrosa', deathPlace, 'Uruguay'l, J.
I'Alfredo Zitamossa, birthPlace, 'Uruguay'). ['Montevideo', , country, 'Urugay'l.
Alfredo Ziturrosa died in city, Uruguay (which has Raul Fernando Sendic Rodriguez leader).
('Alfredo Zitamrsa', deathPlace, 'Montevideo'). I'llfredoZiiaarss' Zitarossa, birthPluce, 'Montevideo'l. ['Urugaay,, capital, 'Montevideo').
Multi-hop
Supported
['Uruguay', leader. 'Raál_ Fernand Sendie Rodríguez'). ['Uruguay', leaderName, 'Raúl Fernando Sendiic Rodríguez']
('Al-Taadduum_A _ir_ Base', city, 'Fallujah"].
Al- Taqaaddum Air Base is located in Pallujah which is not Iraq.
Negation
('Al-Taqaddum Air Base', cityServed. "Fallujah"], I'Fallujah', , country, 'Iraq']
Refuted
['Adare_Mann Manor", country, 'Republic_on of [reeland")) ['Republic_ of Ircland', leader, 'Enda. Kenny').
A country the location of the Adare Manor, run by leader Enda Kenny and the natives are Irish people.
Multi-hop
('Republic_ou Bareliic_oooooeeeee Irelund', leaderName, (Endu_Kenny I'AdareMMaoor, locationCountry, 'Republic_of [o_bbiic____ellandd] l'Republic_o ofreeooo__oooee Ireland', demonym, "Irish_people']
Supported
Table 9: Qualitative results from FACTKG.
Task
Question
Retrieved Griph
Prediction
['Cobra', starred_actors, 'Brigitte Nielsen') ). I'Red Sonja', starred actors, 'Brigitte Nielsen')
what films does [Brigitte Nielsen] appear in?
"Cohra"
you name lilm directed
1-bop
(The Red Baron', directed_ by, 'Nikolai Millerschön']
'The Red Baron
INikolai Müllerschhs]]
what type of film is (Six Shooter]]
[Sixx Showter', has genne, 'Short']
"Short"
when did the films starred by
['Mean Guns', starred actors, DDeborah Van Valkenburgh').
'1997'
[Deborah Van Valkenburgh] release?
['Mean Guns', www.eee_ecceeeeeee _yceee '19971]
which films have the same director of
(Te Duellists', directed__yy by, 'Ridley Scort"].
2. 2.p
"The Counseloe'
[The Duellists]
"The Counselor', directed__by by. 'Ridley Scot')
what genres are the movies written by
[oood, Inc.,, writien by. 'Robert Kenner'). J.
Docamentary'
[Robert Kenner]
['ood, Inc.. genre, 'Documentary']
what the genres of the movies whose
The Lives of Bengul Lancer', written by. 'John Balderston']
writers also wrote
(Frankenstein', written_by. by. 'John L. Balderston'), (Frankenstein', has Benngeenee,, 'Horror']
'Horror'
[The Lives of a Rengal Lancer]?
('Seeking Justice", stamed aattor_ actors, "Nicolas Cage').
when did the movies starred by
3-bop
World Trade Center', staured_actoos actors, 'Nicolas Cage").
'2006'
[Seeking Justice] actors release?
World Trade Center', release_yeut "2006']
[A Thin Line Between Love and Hote', diroctedd_ 'Martn Lawrence') ).
['Big Momma's House", starred_ 'Martin Lawrence"),
what types are the mavies stared by actors
["Big Momma's House", has genuee 'Comedy'l.
"Comedy"
in [A Thin Line Between Love and Hate]?
I'A Thin Line Between Love and Hate", wyittenbby 'Martin Lawrence'),
[AA Thin Line Retween Love and Hate", stared actors, "Martin Lawrence")
Table 10: Qualitative results from MetaQA.

---

Input Type
Method
k
Accuracy
3
72.12
With Evidence
KG-GPT
72.68
10
72.40
Table II: Performance changes according the change in the value in FACTKG.
FactKG
k
Supported
Refuted
3
1.93
1.08
5
2.52
1.39
10
3.13
1.86
Table 12:
The average number of retrieved triples changes according the change in the value in FAC- TKG.
MetaQA lhop
MetaQA
MetaQA
Method
k
2hop
3hop
1
97.0
93.4
89.4
KG-GPT
3
96.3
94.4
94.0
5
96.7
95.0
93.7
Table 13:
Performance changes according to the change the value in MetaQA.
MetaQA
k
1-hop
2-hop
3-hop
1
2.06
4.43
3.25
3
2.16
4.53
3.59
5
2.12
4.51
3.65
Table 14:
The average number of retrieved triples changes according to the change in the & value in MetaQA.

---

Motivation
Practical 4.1 4.2
Cognitive
Intrinsic
Fairness
Generalisation type
Compositional 4.122
Structural
Crosy Task
Cross Language Cross Domain
Robustness
4.2
Shift type
Covariate
Label
Full
Assumed
4.1 4.2
Shift source
Naturally occuring 4.1 4.2
Partitioned natural
Generated shift
Fully generated
Shift locus
Train-test
Finetune train-test
Pretrain-train
Pretrain-test
4.1 4.2
Table 15: GenBench Evaluation Card from Hupkes et al. (2022).


Reviews:
[{"review_id": "3NB8bG6YiE", "reviewer": "Reviewer_USSs", "summary": "", "questions": "It is worth noting that StructGPT [1] appears to be very similar to KG-GPT, as both studies focus on the KGQA problem and use the same MetaQA data. Therefore, it would be interesting to highlight the differences between the two approaches. This could provide readers with a better understanding of the unique contributions of the proposed framework and how it compares to existing methods in the literature.\n\n[1] Jiang, J., Zhou, K., Dong, Z., Ye, K., Zhao, W.X. and Wen, J.R., 2023. Structgpt: A general framework for large language model to reason over structured data. arXiv preprint arXiv:2305.09645.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the time and effort spent in carefully reviewing our work. Please kindly find the responses below.\n\n**Reasons To Reject: The paper would benefit from a more detailed explanation of the results, including how each step contributes to the overall performance and why the methods perform differently. This could help readers better understand the proposed framework and its effectiveness in addressing the problem of knowledge graph reasoning.**\n\nIn FactKG and MetaQA, there is no corresponding ground truth graph from the seed entity to the answer, making it challenging to quantitatively analyze each steps. Instead, we conducted an error analysis by extracting 100 incorrect samples each from FactKG, MetaQA-1hop, MetaQA-2hop, and MetaQA-3hop. The number of samples with errors at each step is shown in the table below. Overall, the number of errors that occurred during graph retrieval was the least among the three steps. This implies that when sentences are accurately divided, it's relatively easier to identify the relations within each sentence. Moreover, a comparative analysis of MetaQA-1hop, MetaQA-2hop, and MetaQA-3hop suggests that as the number of hops increases, the questions become more diverse. This diversity subsequently leads to increased errors in sentence segmentation.  We will add this analysis to the final version.\n| | FactKG | MetaQA-1hop | MetaQA-2hop | MetaQA-3hop | \n|---------|:---------:|:---------:|:---------:|:---------:|\n| Sentence Segmentation| 39| 3| 63| 100|\n| Graph Retrieval | 17| 4| 3| 0|\n| Inference | 44| 93| 34| 0|\n\n\n\n**Questions For The Authors: It is worth noting that StructGPT [1] appears to be very similar to KG-GPT, as both studies focus on the KGQA problem and use the same MetaQA data. Therefore, it would be interesting to highlight the differences between the two approaches. This could provide readers with a better understanding of the unique contributions of the proposed framework and how it compares to existing methods in the literature.**\n\nOur research shares similarities with StructGPT in terms of reasoning from structured data. However, while StructGPT identifies paths from a seed entity to the final answer entity within Knowledge Graphs, our approach involves retrieving the Graph and then obtaining the answer through inference in the end. One advantage of our framework over StructGPT is that finding an entity isn't our final output. This means our framework can be used not only for KGQA but also for tasks like KG-based fact verification. Moreover, we focus on a KG-based framework and KG-GPT outperforms StructGPT in MetaQA as described in the table below. Notably, we observe over a 13% performance difference in MetaQA-3hop. We will include a comparative analysis with StructGPT in the final version.\n| | MetaQA-1hop | MetaQA-2hop | MetaQA-3hop | \n|---------|:---------:|:---------:|:---------:|\n| StructGPT| 94.2| 93.9| 80.2|\n| KG-GPT | 96.3| 94.4| 94.0|", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The problem addressed in this manuscript is intriguing, and the proposed framework is technically sound. The results obtained from the experiments are promising, indicating the potential effectiveness of the proposed approach.", "reasons_to_reject": "The paper would benefit from a more detailed explanation of the results, including how each step contributes to the overall performance and why the methods perform differently. This could help readers better understand the proposed framework and its effectiveness in addressing the problem of knowledge graph reasoning.", "questions_to_authors": "It is worth noting that StructGPT [1] appears to be very similar to KG-GPT, as both studies focus on the KGQA problem and use the same MetaQA data. Therefore, it would be interesting to highlight the differences between the two approaches. This could provide readers with a better understanding of the unique contributions of the proposed framework and how it compares to existing methods in the literature.\n\n[1] Jiang, J., Zhou, K., Dong, Z., Ye, K., Zhao, W.X. and Wen, J.R., 2023. Structgpt: A general framework for large language model to reason over structured data. arXiv preprint arXiv:2305.09645.", "paper_topic_and_main_contributions": "This manuscript delves into the problem of knowledge graph reasoning using Large Language Models (LLMs) and proposes a general framework consisting of three steps: sentence segmentation, graph retrieval, and inference. The triples associated with the sub-KG generated in the first two steps are linearized and concatenated with the input sentence. The framework is evaluated on fact-verification and KGQA tasks, and the results demonstrate its effectiveness. ", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "[1] Jiang, J., Zhou, K., Dong, Z., Ye, K., Zhao, W.X. and Wen, J.R., 2023. Structgpt: A general framework for large language model to reason over structured data. arXiv preprint arXiv:2305.09645."}, "extra_text": {}}, {"review_id": "ba4yF9lPAP", "reviewer": "Reviewer_PVWa", "summary": "", "questions": "Question A: Why is it necessary to use Large Language Models (LLMs) for sentence segmentation and graph retrieval when there are already methods for relation extraction, entity linking, and graph query and matching?\n\nQuestion B: What is the rationale behind segmenting a sentence into sub-sentences, each containing only one relation?\n\nQuestion C: see W2.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the time and effort spent in carefully reviewing our work. Please kindly find the responses below.\n\n**Reasons To Reject W1**\n\nWe think the statement “RoBERTa is one of LLMs” is not entirely agreeable, as the term “LLM” typically refers to generative language models (e.g. ChatGPT, GPT-4, LLaMA). Still, we will use “Generative Large Language Model” instead of “Large Language Model” in the revised manuscript. Additionally, it seems unreasonable to put this in the “reasons to reject” section, when there is a more appropriate section “Typos Grammar Style And Presentation Improvements”.\n\n\n**Reasons To Reject W2 and Question C**\n\n$S_i$ is a sequence of tokens that semantically contains the meanings of entities and relations. As shown in lines 104-107, $r_i$ semantically carries a single meaning but can map to more than one relation (e.g. birthPlace, placeOfBirth), thus defining the set $R_i$. $TypeDBpedia$, as depicted in lines 422-425, is a Knowledge Graph connecting types found in $DBpedia$. For instance, the triple [United_States, president, Obama] in $DBpedia$ is transformed into [country, president, person] to compose $TypeDBpedia$. $E_1$ and $E_2$ refer to the entities that appear in the sentence. In FactKG, entities included in the sub-sentence are always two, which is why they are denoted as $E_1$ and $E_2$. $Relations (T, TypeDBpedia)$ represent the set of relations connected to $T$ in $TypeDBpedia$, and $Relations (e, DBpedia)$ represent the set of relations connected to $e$ in $DBpedia$. We will add the explanations in the final version.\n Additionally, it seems unreasonable to put this in the “reasons to reject” section, when there is a more appropriate section “Typos Grammar Style And Presentation Improvements”.\n\n**Reasons To Reject W3**\n\nIn FactKG [1], all the baselines were evaluated in an entity-given setting, and in KGQA, the assumption where a seed entity is provided is commonly used in most KGQA research [2, 3, 4, 5]. Although entity identification/linking is an essential step in any knowledge-based QA, it is rather easily achievable by interacting with the user whenever necessary (e.g. the machine can ask which entity the user was referring to, or let the user choose the intended entity from the entity list), which is far safer than the model trying to guess which entity the user was referring to, simply based on the very short input context. This is why most past KGQA studies and FactKG focus on the logic part, rather than trying to perfect the entity identification/linking part. We will add this discussion in the revised manuscript.\n\n\n**Reasons To Reject W4**\n\nThe fact that FactKG is not widespread does not change the core value of our work. We chose to use FactKG because it is the only KG-based fact verification dataset where the claim is in natural language. And it encompasses the most diverse reasoning types among all KG-based fact verification datasets (other datasets typically only require 1-hop inference), so the fact that we tested KG-GPT on FactKG instead of older (but easier) ones should be viewed as a strength of our work, not weakness.\n\n FEVER uses unstructured text as evidence, so it's not even compatible with the scope of our work, which is giving LLMs the ability to reason based on knowledge graphs.\n\n\n\n**Reasons To Reject W5**\n\nThe most significant technical contribution of our research is that it provides the first framework that enables the use of LLMs for KG-based tasks. Within this framework, there are three steps, each bearing technical significance.\n\n- First, in the sentence segmentation step, sentences are divided into simpler sub-sentences that correspond to a single relation. This can be seen as dividing a complex problem into simpler ones using a Divide-and-Conquer strategy.\n\n- Second, in the graph retrieval step, LLM is employed to retrieve relations. Recent studies [6] have used generative language models as document retrievers, and our approach similarly leverages generative models for graph retrieval.\n\n- Third, in the inference step, triples from the provided subgraph are combined to reason and derive the final answer. This process, which involves chaining multiple triples for reasoning, can be viewed as the Knowledge Graph version of Chain-Of-Thought [7].\n\nIn this manner, each inherent step holds technical significance beyond mere prompting.\n\n\n**Question A**\n\nIt is not our research objective to solve KG-based tasks with whatever means necessary. It is to design a framework to grant the LLM the ability to reason with KG (as we explicitly described in the Abstract and Introduction), which we demonstrated successfully using two complex KG-based datasets.\n\nMoreover, even if you were interested in solving KG-based tasks with whatever means necessary, LLMs still have a clear advantage compared to combining multiple modules each developed for a specific sub-task (e.g. relation extraction, NER, etc.), since the latter typically requires a separate training data and training process for all individual modules. However, using LLMs does not require any of those extra overhead, as you can harness the power of In-Context Learning. \n\n**Question B**\n\nMany KG-based tasks require multi-hop reasoning. To address this, problems are tackled using a Divide and Conquer approach. By breaking down a sentence into sub-sentences that correspond to a single relation, identifying relations in each sub-sentence becomes easier than finding n-hop relations connected to an entity from the original sentence all at once. Additional explanations will be added to the revised manuscript to clarify.\n\n**Typos Grammar Style And Presentation Improvements P3**\n\nOur framework employs In-Context Learning without fine-tuning process and 12-shot, 8-shot, 4-shot refer to 12, 8, and 4 instances, respectively.\n\n\n[1] Kim, Jiho, et al. FactKG: Fact Verification via Reasoning on Knowledge Graphs. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. 2023.\n\n[2] Jin, W., et al. Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning. Data Min Knowl Disc 37, 255–288 (2023).\n\n[3] Saxena, Apoorv, et al. Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.\n\n[4] He, Gaole, et al. Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals. In WSDM'2021.\n\n[5] Jiang, Jinhao, et al. \"UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph.\" The Eleventh International Conference on Learning Representations. 2022.\n\n[6] Zhu, Yutao, et al. Large Language Models for Information Retrieval: A Survey. arXiv preprint arXiv:2308.07107 (2023).\n\n[7] Wei, Jason, et al. \"Chain-of-thought prompting elicits reasoning in large language models.\" Advances in Neural Information Processing Systems 35 (2022): 24824-24837.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "S1 The paper designs a framework for reasoning based on knowledge graph. And the framework shows competitive performance, which gives us some insights of using ChatGPT for reasoning based on knowledge graph. \nS2 The details of the way of training the model are provided, for instance, the prompts used in every step. This level of transparency enhances the reproducibility and understanding of the proposed approach.", "reasons_to_reject": "W1 The paper makes several claims that some have found to be factual but not entirely accurate. To strengthen its credibility, a more thorough literature review may be necessary.\n---- The paper claims “there is no general framework for performing KG-based tasks (e.g. question answering, fact verification) using LLMs.” However, I found [1] proposes a framework using RoBERTa-large and KG for question-answering. There are also some other tasks like knowledge graph completion and graph-to-text generation which are KG-based tasks use LLMs, for example, [2] and [3], respectively. \n\n-------------------------------------------\nPut W1 to presentation improvement.\n\nW2 The formal notations and definitions have various problems related to soundness, clarity, and rigor. \n--- line 101-102, “Si consists of a set of entities and a relation”, it is unclear why a sequence of tokens consists of a set of entities and a relation. And in lines 115-118, it mentions Ri is a set of relations, isn’t each sub-sentence only have one relation according to lines 101-102?\n--- Algorithm 1, what is TypeDBpedia and how it is gotten; it is unclear what E1 and E2 are. What is “Relations (T, TypeDBpedia)” and “Relations (e, DBpedia)”? \n\nW3 One aspect of the paper's precondition raises concerns for me. It assumes that \"all entities involved in S are given,\" which may not align with real-world applications. In practice, users may not specify all entities in their questions or claims, making this assumption less realistic.\n\nW4 The choice of datasets for the experiments seems atypical. For instance, FactKG, released in 2023, might not have gained widespread adoption yet, whereas [4] FEVER serves as a widely used benchmark for claim verification.\n\nW5 The paper appears to lack technical depth, as all three components primarily rely on providing prompts to ChatGPT.", "questions_to_authors": "Question A: Why is it necessary to use Large Language Models (LLMs) for sentence segmentation and graph retrieval when there are already methods for relation extraction, entity linking, and graph query and matching?\n\nQuestion B: What is the rationale behind segmenting a sentence into sub-sentences, each containing only one relation?\n\nQuestion C: see W2.", "paper_topic_and_main_contributions": "This paper studies the problem of knowledge graph reasoning using large language models (LLMs), specifically, ChatGPT. A framework, called KG-GPT is proposed to solve this problem. KG-GPT consists of three parts: Sentence Segmentation, Graph Retrieval, and Inference. Sentence Segmentation split a claim or question into several sub-sentences which contain only one triple, Graph Retrieval retrieves the knowledge graph and finds evidence for the claim or question, and Inference do the final decision. All three components are leveraging ChatGPT by providing prompts. The method is evaluated on two datasets: FactKG (fact verification) and MetaQA (question-answering). The experiment results show competitive performance with fully supervised models.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "[1] Yasunaga, Michihiro, et al. \"QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering.\" Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.\n\n[2] Wang, Liang, et al. \"SimKGC: Simple contrastive knowledge graph completion with pre-trained language models.\" arXiv preprint arXiv:2203.02167 (2022).\n\n[3] Agarwal, Oshin, et al. \"Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training.\" Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.\n\n[4 Thorne, James, et al. \"FEVER: a Large-scale Dataset for Fact Extraction and VERification.\" Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.", "Typos_Grammar_Style_and_Presentation_Improvements": "P1 See W1, W2.\n\nP2: I will suggest conducting experiments using all available training instances for ChatGPT to determine if the performance remains consistently good.\n\nP3: Regarding the terms \"12-shot,\" \"8-shot,\" and \"4-shot,\" do they refer to using 12, 8, and 4 instances, respectively, for training the model, or do they represent using 12%, 8%, and 4% of training set instances for training the model?"}}, {"review_id": "fTGBnRqLeP", "reviewer": "Reviewer_Z28W", "summary": "", "questions": "Q1. My concern regarding this paper pertains to the graph retrieval step in addressing KBQA. In this step, the relation candidates extraction algorithm is employed to extract candidate subgraph relations, which are subsequently filtered through the LLM method to obtain the top-k relations that form the subgraph. However, in this process, how do the authors ensure that the extracted subgraphs contain the answer nodes, especially in the case of multi-hop questions?\n\nQ2. The experiments of this paper only present the performance of ChatGPT in the zero-shot setting. However, my main concern lies in the fact that the proposed KG-GPT does not truly comprehend the graph structure. It appears that for fact verification, KG-GPT relies on its pre-existing knowledge to determine the plausibility of given statements. As for KBQA, KG-GPT first generates answers independently and then selects similar entities from the provided candidate answers. No ablation experiments are provided in the study concerning this issue, nor is there any evidence to suggest that KG-GPT's judgments are based on the provided knowledge graph. The authors should include ChatGPT's experiment results in the same experimental setting but without the extraction of sub-KG.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the time and effort spent in carefully reviewing our work. Please kindly find the responses below.\n\n**Reasons To Reject 1**\n\nMany KG-based tasks require multi-hop reasoning. To address this, problems are tackled using a Divide-and-Conquer approach. By breaking down a sentence into sub-sentences that correspond to a single relation, identifying relations in each sub-sentence becomes easier than finding n-hop relations connected to an entity from the original sentence all at once. Additional explanations will be added to the revised manuscript to clarify.\n\n**Reasons To Reject 2 and Question 2**\n\n| | FactKG | MetaQA-1hop | MetaQA-2hop | MetaQA-3hop | \n|---------|:---------:|:---------:|:---------:|:---------:|\n| 12-shot ChatGPT| 68.48| 60.0| 23.0| 38.7|\n| KG-GPT | 72.68| 96.3| 94.4| 94.0|\n\nWe conducted an additional 12-shot ChatGPT experiment. The results are in the table above, where 12-shot ChatGPT demonstrated lower performance than KG-GPT in all cases, proving the positive impact of the retrieved graph. However, as you can see, the performance gap between the two models in FactKG is quite narrow (both perform quite well), whereas in MetaQA, there's a significant difference (KG-GPT is near perfect, while 12-shot ChatGPT is not). There are two reasons for this. \n\n- First, the number of unique relations used in MetaQA’s KG (i.e. WikiMovies) is 9, whereas FactKG’s (i.e. DBPedia) has over 500. Thus, KG-GPT can utilize graphical knowledge much more effectively in MetaQA, where relation retrieval is comparatively easier. \n\n- Second, while MetaQA is a question-answering task, FactKG is a binary classification task. Thus, as you pointed out, it is possible for ChatGPT to make a correct guess based on its pre-existing knowledge. However, in MetaQA, it's crucial to find the correct entity. Without proper multi-step inference based on the KG, it becomes extremely challenging to find the right answer, leading to ChatGPT's lower performance in MetaQA.\n\n- In conclusion, as you've mentioned, in certain situations, it seems possible for LLMs to rely on their prior knowledge to get the right answer. But fundamentally, we've experimentally confirmed that KG-GPT certainly utilizes graphical knowledge. In the future, we will further develop our algorithm to retrieve more precise subgraphs in complex KGs.\n\n\n\n**Question 1**\n\nIn graph retrieval, we sequentially visit neighbors from the seed entity using the top $k$ relations to construct a subgraph. If sentence segmentation and relation retrieval for each sub-sentence are done correctly, the answer will inevitably be in the subgraph.\n\n We will demonstrate the process of extracting a subgraph of a 2-hop question using an example. The subgraph $G$ starts as an empty set, and triples are added according to the algorithm below to obtain the final graph containing the answer node of the question.\n\n First, we segment the question $S$ (with seed entity $e$) into sub-sentences $S_1$ (containing $e$) and $S_2$. Then, for each $S_1$ and $S_2$, we retrieve the top $k$ relations $R_1 = ${$r_{1}^{1}, r_{1}^{2}, ..., r_{1}^{k}$} and $R_2 = ${$r_{2}^{1}, r_{2}^{2}, ..., r_{2}^{k}$}. For each $r_{1}^{m} \\in R_1$, we determine the set of entities connected to $e$ via $r_{1}^{m}$ as $E_{(1, m)} = ${$e_{(1, m)}^{1}, e_{(1, m)}^{2}, ..., e_{(1, m)}^{n}$}. Then, for each $e_{(1, m)}^{p} \\in E_{(1, m)}$, we find the entities connected by every $r_{2}^{l} \\in R_2$ as $E_{(2, m, l)} = ${$e_{(2, m, l)}^{1}, e_{(2, m, l)}^{2}, ..., e_{(2, m, l)}^{t}$}. If $E_{(2, m, l)}$ is an empty set, subgraph $G$ remains unchanged. If not, we randomly select $e_{(2, m, l)}^{w}$ from $E_{(2, m, l)}$ and add the two triples $[e, r_{1}^{m}, e_{(1, m)}^{p}]$ and $[e_{(1, m)}^{p}, r_{2}^{l}, e_{(2, m, l)}^{w}]$ to $G$. This algorithm is used for every $r_{1}^{m} \\in R_1$ and $r_{2}^{l} \\in R_2$ pair to complete $G$.\n\n The mathematical notation might not be intuitively clear, so in the revised manuscript, we will include a figure detailing the steps to help explain.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. This paper proposes a versatile framework that utilizes LLMs for tasks that use KGs.\n\n2. This paper evaluates KG-GPT using KG-based fact verification and KGQA benchmarks, which fills the gap in complex reasoning on knowledge graphs for LLMs.", "reasons_to_reject": "1. This paper fails to provide a clear introduction to the sentence segmentation step, which constitutes a crucial aspect.\n\n2. This paper presents a feasible method for combining LLMs with KGs. However, it claims that KG-GPT can comprehend graph structures, yet the experiments provided in the paper fail to substantiate this claim. Additional comparative experiments and case studies are required to support this assertion adequately.", "questions_to_authors": "Q1. My concern regarding this paper pertains to the graph retrieval step in addressing KBQA. In this step, the relation candidates extraction algorithm is employed to extract candidate subgraph relations, which are subsequently filtered through the LLM method to obtain the top-k relations that form the subgraph. However, in this process, how do the authors ensure that the extracted subgraphs contain the answer nodes, especially in the case of multi-hop questions?\n\nQ2. The experiments of this paper only present the performance of ChatGPT in the zero-shot setting. However, my main concern lies in the fact that the proposed KG-GPT does not truly comprehend the graph structure. It appears that for fact verification, KG-GPT relies on its pre-existing knowledge to determine the plausibility of given statements. As for KBQA, KG-GPT first generates answers independently and then selects similar entities from the provided candidate answers. No ablation experiments are provided in the study concerning this issue, nor is there any evidence to suggest that KG-GPT's judgments are based on the provided knowledge graph. The authors should include ChatGPT's experiment results in the same experimental setting but without the extraction of sub-KG.", "paper_topic_and_main_contributions": "This paper proposes KG-GPT, a multi-purpose framework leveraging LLMs for tasks employing KGs. KG-GPT comprises three steps: sentence segmentation, graph retrieval, and inference. The sentence segmentation step partitions a sentence into discrete sub-sentences, each aligned with a single triple. The graph retrieval step retrieves a potential pool of relations within the sub-sentences. In the final step, inference obtains graphs used to derive a logical conclusion, such as validating a given claim or answering a given question.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "In the 97th line of the second page of the paper, the author's discussion on sentence segmentation is overly rudimentary. The author should provide a succinct and refined elucidation of the module's workflow at this juncture."}}]

Author rebuttal:

Meta-review: This paper proposes KG-GPT,  a multi-purpose framework leveraging LLMs for tasks employing KGs. KG-GPT comprises three steps: Sentence Segmentation, Graph Retrieval, and Inference, each aimed at partitioning sentences, retrieving relevant graph components, and deriving logical conclusions. The sentence segmentation step partitions a sentence into discrete sub-sentences, each aligned with a single triple.  The graph retrieval step retrieves a potential pool of relations within the sub-sentences. In the final step, inference obtains graphs used to derive a logical conclusion, such as validating a given claim or answering a given question. Meanwhile, this paper evaluates KG-GPT using KG-based fact verification and KGQA benchmarks, which fills the gap in complex reasoning on knowledge graphs for LLMs.
Decision: Accept-Findings