Inductive Reasoning for Temporal Knowledge Graphs with Emerging Entities
Authors: Ze Zhao
He Yuhui
Lyuwen Wu
Gu Tang
Bin Lu
Xiaoying Gan
Luoyi Fu
Xinbing Wang
Chenghu Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Temporal Knowledge Graph
Inductive Learning
Primary area: learning on graphs and other geometries & topologies

Abstract: Reasoning on Temporal Knowledge Graphs (TKGs) is essential for predicting future events and time-aware facts. While existing methods are effective at capturing relational dynamics, their performance is limited by a closed-world assumption, which fails to account for emerging entities not present in the training. Notably, these entities continuously join the network without historical interactions. Empirical study reveals that emerging entities are widespread in TKGs, comprising roughly 25\% of all entities. The absence of historical interactions of these entities leads to significant performance degradation in reasoning tasks. Whereas, we observe that entities with semantic similarities often exhibit comparable interaction histories, suggesting the presence of transferable temporal patterns. Inspired by this insight, we propose TransFIR (Transferable Inductive Reasoning), a novel framework that leverages historical interaction sequences from semantically similar known entities to support inductive reasoning. Specifically, we propose a codebook-based classifier that categorizes emerging entities into latent semantic clusters, allowing them to adopt reasoning patterns from similar entities. Experimental results demonstrate that TransFIR outperforms all baselines in reasoning on emerging entities, achieving an average improvement of 28.6\%  in Mean Reciprocal Rank (MRR) across multiple datasets. The implementations are available at https://anonymous.4open.science/r/TransFIR-C72F.

Published as a conference paper at ICLR 2026
INDUCTIVE REASONING FOR TEMPORAL KNOWLEDGE GRAPHS WITH EMERGING ENTITIES
Ze Zhao', Yuhui He', Lyuwen Wu', Gu Tang', , Bin Lu', Xiaoying Gan Luoyi Fu', Xinbing Wang', Chenghu Zhou Shanghaa Jiao Tong University, RIGSNRR, Chinese Academy of Sciences {zhaoze, hyhui iiii, wlw201 6, gutang, robinlu1209, oow..nnull.......lolooo....... yiluofu, a jtu. edu. en, zhouch mrrcchhrreii... an
ABSTRACT
Reasoning on Temporal Knowledge Graphs (TKGs) is essential for predicting fu- ture events and time-aware facts. While existing methods are effective at capturing relational dynamics, their performance is limited by closed wooldd assumption, which fails to account for emerging entities not present in the training. Notably, these entities continuously join the network without historical interactions. Em- pirical study reveals that emerging entities are widespread in TKGs, comprising roughly 25% of all entities. The absence of historical interactions of these entities leads to significant performance degradation in reasoning tasks. Whereas, we ob- serve that entities with semantic similarities often exhibit comparable interaction histories, suggesting the presence of transferable temporal patterns. Inspired by this insight, we propose TRANSFIR (Transferable Inductive Reasoning). a novel framework that leverages historical interaction sequences from semantically sim- ilar known entities to support inductive reasoning. Specifically, we propose a codebook-based classifier that categorizes emerging entities into latent semantic clusters, allowing them to adopt reasoming patterns from similar entities. Experi- mental results demonstrate that TRANSFIR outperforms all baselines in reasoning on emerging entities, achieving an average improvement of 28.6% Mean Re- ciprocal Rank (MRR) across multiple datasets. The implementations available at htt.:s//giiib..ooo//////////////
INTRODUCTION
Reasoning on Temporal Knowledge Graphs(TKGs) facilitates the prediction of future events and time-aware facts, significantly enhancing the utility and applicability of temporal knowledge graphs. By explicitly modeling relation dynamics as the graph evolves, TKG reasoning captures temporal dependencies and interaction patterns, thereby supporting event forecasting and time-aware infer- ence (Liang et al., 2024 Zhang et al. 2025a). These capabilities form the foundation for applications such temporal question answering, clinical risk analysis, and recommendation systems (Xue et al. 2024 Postiglione et al. 2024 Hu et al. 2024)..
However, existing reasoning methods focus on modeling relation dynamics while neglecting the emergence of new entities. In real-world graphs, both entities and relations evolve continuously. Emerging entities often join the network without historical interactions. This phenomenon is ob- served in various contexts, from social platforms adding new users (Wang et al., (022a)) to molecu- lar networks coming new compounds (Hadipour al. 2025). Although current methods effectively capture relation dynamics and achieve strong forecast performance (Li et al. 2022] Xu et. (0236), they typically assume a closed entity set. Due to the absence of historical interactions, these models lack adequate supervision and representation for emerging entities, which significantly limits their reasoning capability. For instance, as shown in Fig. I when Barack Obama first assumes office, predicting his first state visit becomes challenging due to the absence of historical interactions.
To clarify the challenges and opportunities for reasoning on emerging entities, we conduct an empir- ical study from three progressively deeper perspectives (see Sec. 3). From the Data perspective, we
"Correspnndence Xiaoying Gan (ganxiaoying @stu.edu.cn)...

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Published as a conference paper at ICLR 2026
observe that emerging entities widespread in TKGs, with nearly 25% of entities not appearing in the training set. Meanwhile, existing models show a significant performance drop on related events. From the Representation perspective, attribute this degradation to representation collapse, caused by the lack of supervision signal from historical interactions Finally, from the Feasibility perspective, we explore invariant patterns transfer to emerging entities and find that entities of semantically similar type often exhibit comparable interaction histories.
As inspiring method, inductive learning provides a promising approach for reasoning new en- tities in knowledge graphs. Unlike transductive methods, which rely on entity-specific embeddings, inductive approaches learn transferable patterns from subgraphs (Chen al.) 2022). For instance, InGram (Lee et al. 2023b) constructs relation relation-affinity graphs to capture neighbor interactions, while ULTRA (Galkin et al. (024) generalizes to unseen entities through relative interaction representa- tion. However, these methods are primarily designed for static KGs, where new entities already have known interactions. In contrast, emerging entities in TKGs often arrive without any inter- actions. This lack of supervision signals can lead to representation collapse, raising significant challenge: how can we prevent representation collapse in the absence of historical interactions.
To address this challenge. we propose TRANSFIR (Transferable Inductive Reasoning), an inductive rea- soning framework designed to handle emerging entities in TKGs. Inspired by our empirical observation that se- mantically similar entities exhibit transferable patterns, we propose Interaction Chain to model such structures. TRANSFIR extracts these patterns from Interaction Chains and employs codebook-based classifier to map entities into latent semantic clusters, thereby transferring patterns to emerging entities. Specifically, TRANSFIR follows a SessiffaatoonRRpreeentaaai Reneeeeeeettioooonnnnaiiiiiioiii pipeline: (i) Classification maps entities to latent semantic clusters via an interaction-awarc codebook; (ii) Representation encodes entity's Interaction Chain to capture reasoning patterns; (iii) Generalization propagates learned patterns within each cluster, enabling emerging entities to obtain informative embeddings. Together, these steps help prevent representation collapse and improve forecasting performance for emerging entities.
known interaction predict interaction
Transductive Reasoning
lend to visil, L host visil, C
g Govermeent Tag: Country
President
Country A
(b) Inductive Reasoning
without piror interaction
lend to visit, Lg
Emerging entity
Canda
Figure 1: Illustration of Transduc- tive vs. Inductive Reasoning on Emerging Entities.
In summary, the main contributions of our work are as follows:
Novel Framework. We propose TRANSFIR, an inductive framework designed to transfer rea- soning patterns from semantically similar entities to enable reasoning on emerging ones.
Kodebook-based Classifier for Transfer. We propose interaction-awart VQ codebook that maps entities into latent semantic clusters. This facilitates reasoning pattern transfer while pre- venting representation collapse.
Problem Evidence. We formally define the task of inductive reasoning on emerging entities without historical interactions. Additionally, empirical study demonstrates that such entities are widespread in TKGs and existing methods suffer significant performance degradation.
State-of-the-art Results. TRANSFIR outperforms strong baselines across multiple benchmarks, with an average improvement of 28.6% in MRR on four datasets.
PRELIMINARIES AND PROBLEM FORMALIZATION
Reasoning on Temporal Knowledge Graphs. A temporal knowledge graph (TKG) is structured as a sequence of timestamped snapshots & = (9.)eeT where each snapshot G, (Ei R, Fi). Here E1s is the set of entities observed up to time 4, R is the relation set, and F, C E x R E {t} represents the set of timestamped facts.
Given query (e., r, fg) with to in the future, temporal KG reasoning aims to predict the missing entity based on the historical context He, =ler, F. It follows a standard chronological time split to prevent leakage future information. However, a non-trivial fraction of entities remain unseen during training, posing significant challenges for generalizing emerging entities.
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Beaworing Poltera
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(a) Entity Emergence with Time
(b) Performance Comparison
(c) Representation Collapse
(d) Transferable Patterns
Figure 2: (a) Entity emergence over time. Across four TKGs, new entities continuously emerge; about x 25% of entities are unseen during training. (b) Performance comparison. Under Vanilla vs. Emerging settings, strong baselines consistenly drop on emerging entity triples. (c) Represen- tation collapse. On ICEWS14, t-SNE of LogCL shows representation collapsing after training, while known entities drift to a separate manifold. (d) Transferable patterns. Semantically similar entities share celation-condition
patterns, enabling transfer to emerging entities.
Inductive Reasoning for Emerging Entities in TKGs. We formalize inductive reasoning for TKGs: reasoning for emerging entities that enter the graph without historical interactions. Define the aipst-apeearance time of entity be
(e(e) = min & E T participates some (e, () E J.
At timestamp f, is considered as emerging entity if e EL \ E1a-1. The goal is to answer temporal queries involving such entities at the moment they emerge- ie.. queries of the form (e, ?, fng) or e.t where f.g (e), and no historical interactions available. This setting reflects real-world acenarios-suce as the introduction of new users, proteins, or organizations-ande and highlights the difficulty of reasoning in the absence of historical interaction.
3 EMPIRICAL INVESTIGATION
In this section, we empirically investigate the proposed inductive reasoning task for emerging enti- ties from three complementary angles. From the Data perspective, we address the prevalence and impact of emerging entities on forecasting by answering Q1: How frequently do emerging entities appear in TKGs, and how do they influence forecasting performance? From the Representation perspective, we explore the underlying causes of performance degradation by addressing Q2: What factors contribute to failures on emerging entities? From the Feasibility perspective, we investigate potential alternatives to overcome the limitations of sparse interactions by answering Q3: Are there transferable temporal patterns that support reasoning without historical interactions? Details of datasets, models, and metrics are provided in Sec. 5
To address Q1, We quantify entity emergence and its impact on performance across multiple TKG datasets. Specifically, we track both the number and the proportion of emerging entities over time. To assess impact on forecasting, we evaluate representative baselines in two settings: (i) overall test triples (Vanilla) and (ii) triples involving least one emerging entity(Emerging).
Observation 1. From Fig. a)),, we find that new entities continuously emerge over time. Nearly 25% of entities appear only inference set, having no historical interactions available for training. indicating that entity emergence is widespread in TKGs. From Fig. 2b), all models exhibit signif- icant performance degradation on emerging triples compared with vanilla triples, underscoring the challenge of generalizing to entities without historical interactions.
To address Q2. we evaluate the representation quality for all entities using t-SNE (known vs. emerg- ing). We visualize both the initial embeddings (all entity embeddings are randomly initialized) and the learned entity embeddings after training in baseline model, LogCI. (Chen et al. 2024). Addi- tionally, inspired by Zbontar al. al.) (202)). we propose rotation-invarii quantify the degree of collapse. Collapse ratio measures the geometric spread (log-det covariance) of emerging-entity embeddings relative to reference set; lower values indicate stronger collapse. See Appendix C.2 for the full definition and details.
mertic, Collapse Ratio, to
Observation 2. From Fig. ()c), we find that after training, emerging entities deviate sharply from known entities in the embedding space. Quantitatively, their Collapse Ratio drops from 1.0201 to 0.0055 after training, evidencing severe representation collapse.
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Textual Eacoding
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Representation
Chain Construction
Encoding
MdC
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Emerging
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Pattern Transfer
Ranking
Generalization
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Embedding Space
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Figure 3: The overall architecture of proposed TRANSFIR.
To address Q3, we investigate whether models can perform reasoning that is independent of entity embedding. Inspired by prior work on inductive and path-based reasoning, we analyze the feasibility to transfer interaction patterns across entities, and identify concrete instances of this phenomenon.
We observe that certain reasoning patterns can be transferred across entities of similar semantic types. For example, shown in Fig. ((d). visit-negotiation sequences patterns can be reused when a new president takes office in another country with no interaction history. This suggests that invariant event-sequence patterns can be captured and extended semantically similar entities, thereby supporting inductive inference for emerging entities.
Observation 3.
4 METHODOLOGY
In this section, we present TRANSFIR, inductive framework designed for emerging entities without interaction history. As shown in Fig. 3 TRANSFIR employs a three-stage Classifica- ReepreeetationnGeennnnd resentations:
pipeline that transform raw interactions into transferable rep-
(1) Codebook Mapping (Classification): Assign emerging and known entities latent types (se- mantic clusters) via a vector- qurotizqad (VQ) codebook, providing history-free categorical priors.
(2) Interaction Chain Encoding (Representation): Construct and encode Interaction Chains (ICs) around query entities to capture transferable interaction sequences.
(3) Temporal Pattern Transfer (Generalization): Propagate learned temporal patterns within each cluster, enabling emerging entities to acquire informative, time-aware embeddings.
4.1 CODEBOOK MAPPING (CLASSIFICATION)
Entities of similar semantic types often share comparable interaction history (e.g., states show recur- ring diplomatic rhythms, while individuals follow distinct patterns). Inspired by this, categorizing entities into latent semantic clusters offers promising way import type-level priors for emerging entities. However, two straightforward strategies prove inadequate: (i) updating entity embeddings directly risks collapse for emerging entities lack of supervision; (ii) based frozen embeddings fails to adapt to dynamic interactions within temporal knowledge graphs.
To address these challenges, propose learnable vector quantization (VQ) codebook: entity em- beddings are fixed for stability, and cluster prototypes are trained to become interaction-awarre This results in adaptive latent semantic clustering mechanism to facilitate effective transfer learning.

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Vector-quantized clustering. For each entity &, we first obtain static textual embedding h. E Iº from its title using a pretrained BERT encoder. These embeddings remain fixed during training. allowing emerging entities to be encoded even without any interaction history.
{ct,...,,C'' where each codeword C₁ E IR denotes a latent cluster. Entity entity embedding is quantized by mapping it to the nearest codeword:
We maintain a learnable codebook =
π (e) = arg min l|h, - cx
(1)
where (e) is the cluster index of entity e. This process groups both observed and emerging entities into consistent categories, forming an adaptive semantic cluster structure for downstream reasoning.
Codebook optimization. ensure meaningful latent semantic clusters, we adopt two comple- mentary objectives. The codebook loss updates prototypes toward their assigned embeddings:
Leb
||s][he]
Cx(c)
(2)
where sgl-] denotes the stop- -raddrtdient operator. The commitment loss encourages embeddings to stay close to their prototypes:
Commmit = llb sg(Cx(e))l1..
(3)
The overall objective
Loodebook aLob + Cooomitt
with 3 >> 0 as weighting coefficients. This optimization refines the codewords into semantically coherent clusters and stabilizes the assignment of entities to prototypes. Unlike static clustering methods, approach jointly learns the prototypes with the task objective, making it suitable for fixed entity embeddings while enabling adaptive category representations for effective classification.
4.2 INTERACTION CHAIN ENCODING (REPRESENTATION)
To capture transferable interaction sequence patterns for emerging entities, we introduce an Interac- tion Chain (IC) around each query entity. Unlike unordered temporal neighborhoods, ICs preserve the sequential structure of interactions, thereby reflecting entity-invariant temporal dynamics-such as periodic behaviors or events that follow specific order.
its past interactions in chronological order: ?g, Definition. Given a temporal query (eg, Tg. ?, 2,4g) and a window size T, the IC of Eg collects
C ((e,, or (s, Cq. 4)) l1 T S <G)
(5)
which captures the behavioral trajectory of 6g piror to time fg. This chain-based structure is mo- tivated by Observation 3., which suggests that such sequential patterns largely independent of specific entities and are more effectively modeled through chains than through unordered neighbor- hoods (see Appendix D.I for further details).
Construction. At query time byg for each query (eg. Tq 2,4g) we collect past interactions of e, within window T' to form C.p. Let r; be the relation of the i-th interaction in C₁ with trainable relation embeddings hr E Rd. We keep the interactions whose relations are most similar to the query relation:
c(O)) = TopK, (sim((hr,, Co):
(6)
where sim is cosine similarity. Selected interactions are then kept in chronological order; doing this for all queries at / yields ICs (C/*)) the temporal context for the snapshot.
Encoding, Each interaction (ss Fe, os, to) E ((1)) first mapped by component-specific trans- forms (.). and fused by f(:):
(h..) (hr,), de (ho,), or (hsi)))
where hy hr E 2dd are frozen entity embeddings (from a pretrained encoder; see Sec. 4.1 hr, E Rd is trainable relation embedding, and hAr; encodes the relative time gap Al; by 1,-
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The sequence ) is then contextualized by Transformer encoder yield (ha]-.. We apply relation-guides attention, modulated by the query-relation embedding hr
exp(w tanh(Wi,h, Wyhr,)) a; Σ, exp(w tanh(W,,,, + ww)hr)))
h
>a aahh: hi,
(8)
which produces the -peryfspe chain representation Eyy emphasizing interactions most relevant to T4 while down-weigtt wwightitgtin irrelevant context.
4.3 CHAIN PATTERN TRANSFER (GENERALIZATION)
Although IC encodings capture query-specific dynamics, entities with limited interactions remain static, limiting generalization to emerging cases. To address this, we propose Chain Pattern Trans- fer, mechanism that propagates interaction patterns across semantic clusters. This approach en- ables even newly emerging entities to acquire time-aware representations.
Cluster pooling. At each timestamp i, aggregate IC embeddings (h( based codebook assignments. Let (e) be the cluster index of entity e. The dynamic prototype of cluster is
dyn
Σ EEQN
h
Qs {e E & I(e) =k},
Qu
which summarizes the shared temporal evolution within semantic cluster k.
Pattern Transfer.
Each entity e combines its static embedding h, with the cluster-level prototype:
h. || ()
(10)
where ||
denotes concatenation. parametric mapping (-) generates the transfer vector:
(ze),
h. = he + we =(e))
(11)
Through the Pattern Transfer module, we transfer Interaction Chain's 's information from semantically similar known entities to emerging ones, resulting in informative entity representations.
Ranking and optimization. Given query (eq, Tg ?, l.). candidate entities e, are scored as eee rg. Co. t) [he, br, he.))
(12)
where f(-) is implemented with ConvTransE (Shang et al. 2019). strong score function that is widely adopted for the recent TKG reasoning methods. The training objective is cross-entropy loss over all candidate entities:
4p
Σ
Σ
Σ
vi, log dhe Tig e, kg);
(13)
t=l Port,,)))
where vf, is the one-hot indicator for the correct entity. The overall objective is
= Lip + XLeodebook-
(14)
In our work, both link prediction loss and codebook loss are trained simultancously. complete algorithmic workflow, detailed pseudo code, and complexity analysis are provided in Appendix D.2
EXPERIMENTS
We evaluate the effectiveness of TRANSFIR through extensive experiments and analyses, guided by the following research questions:
RQ1: How does TRANSFIR compare with SOTA baselines in emerging entity reasoning?
RQ2: What insights can be obtained from the learning behavior of TRANSFIR?
RQ3: How does each component TRANSFIR contribute to its overall effectiveness?
RQ4: How well does TRANSFIR generalize inductive scenarios?

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Table I: Performance comparison of inductive reasoning on emerging entities on four benchmarks. In each column, best results are highlighted in bold and second-best are underlined. For generative model GenTKG, MRR is unavailable due to it's reliance on multiple generations for each query.
ICEWS14
ICEWS18
ICEWS00S15 15
GDELT
Method
MRR
Hits@3
Hits@
10
MRR
Hits@3
Hiss@
MRR
Hits@3
Hits@
MRR
H
Hits@ 10
CyGNe122211)
0.0111
0.0098
0.0202
0.0031
0.0020
0.0047
0.0031
0.0048
0.0067
0.0031
0.0147
REGCN(2021)
0.1175
0.1263
0.2232
0.0947
0.1004
0.1797
0.0887
0.0961
0.1803
0.0222
0,0209
0,0393
Graph- bosed
HiSMatch(2022)
0.0284
0.0285
0.0418
0.0055
0.0058
0.0076
0.0242
0.0238
0,0443
0.0159
0.0141
0.0270
MGESL(2024)
0.0309
0.0361
0.0603
0.0747
0.0809
0.1031
0.1069
0.1166
0.1563
0.0516
0.0471
0.0815
LogCL/2024)
1.54
0.1770
0.2273
0.0903
0.1064
0.1548
0.1917
0.2452
0.2855
0.0473
0.0479
0.0973
HisRes(2025)
0.1169
0.1107
0.2132
0.0445
0.0434
0.0735
0.1325
0.1332
0,1407
0.0416
0.0737
0.0932
MLEMKDX2025)
0.0685
0.0728
0.1303
0.0402
0,0382
0.0831
0,0833
0.0848
0.1717
0,0229
0,0215
0,0436
TLogie(2022)
0.0122
0.0107
0.0257
0.0141
0.0131
0.0262
0.0121
0.0108
0.0285
0.0733
0.0749
0.1131
Path
TILP(2024)
0.0397
0.0471
0,1114
0.0498
0.0669
0.1659
0.0358
0.0374
0.1030
0.0053
0.0025
0,0084
BCTBfommen)(224
0.0461
0.0496
0.0915
0.0323
0.0680
0.0454
0.0587
0.0642
0.1141
0.0426
0.0410
0.0872
GGnTKGr2024)
0.0983
0,1919
0,0540
0.1512
0.1105
0.1873
0.0734
0.1013
CompGCN(2020)
0.0682
0.0906
0.1213
0.0638
0.0745
0.1049
0.1885
0,2103
0.2479
0.0472
0.0791
0.0934
Inductive
ICL((023)
0.0252
0.0261
0,0388
0.0639
0.0727
0.0938
0.0254
0,0302
0.0373
0.0277
0.0326
0.0362
PPT(2023)
0.0093
0.1062
0.1716
0.0368
0.0386
0.0650
0.0015
0.0005
0.0022
0,0406
0.0425
0.0764
MorsB(2022)
0.0136
0.0074
0.0185
0.0078
0.0075
0.0126
0.0381
0.0167
0.0439
0.0039
0.0040
0.0152
InG)am(2023)
0.0563
0.0596
0.1138
0.0254
0,0265
0.0518
0,0771
0.0793
0.1454
0,0471
0,0430
0.0847
Ours
TRANSFIR
0.1687
0.1935
0.3246
0.1177
0.1344
0.2324
0.2204
0.2617
0.3827
0.1103
0.1129
0.2278
Improvements
24.6%
9.3%
42.8%
24.3%
26.3%
29.3%
15.0%
6.7%
34.0%
50.5%
42.7%
101.4%
5.1
SETUP
Datasets. Our experiments are conducted on four widely used benchmark datasets for TKG rea- soning: ICEWS14, ICEWS18, ICEWS05-15, and GDELT. Unlike the conventional 8:1:1 split, we adopt a 5:2:3 chronological split. This approach helps reveal more emerging entities and better eval- uates inductive reasoning performance. A detailed description of the datasets and their statistics can be found in Appendix E.
Baselines. To demonstrate the effectiveness of TRANSFIR, we compare it with thirteen strong base- lines across three complementary categories: (1) Graph-based methods; (2) Path-based methods; (3) Inductive methods. The details of the description and implementation of all methods are provided in the Appendix E.2
Evaluation Metrics. We report results using Mean Reciprocal Rank (MRR) and Hits (k=3,10), the standard metrics for link prediction. We pay particular attention to triples involving emerging entities, which directly reflects the ability to generalize beyond entities observed during training.
5.2 PERFORMANCE COMPARISON (RQI)
The overall performance of TRANSFIR and all baseline methods on the four benchmark datasets is summarized in [T The best scores are highlighted in bold, and the second-best scores are underlined. From the experimental results, we draw the following observations:
Firstly, TRANSFIR achieves the highest average performance across all four benchmarks, consis- tently ranking first in both MRR and @its@k on every dataset. Its consistent superiority over graph- based, path-based, and static inductive basclines confirms the effectiveness of TRANSFIR for induc- tive reasoning on emerging entities (c.g., average MRR gain of 28.6 % over the strongest baseline).
Secondly, the ICEWS series, TRANSFIR demonstrates notable gains. On ICEWS14 it surpasses the best baseline by 24.6% % in MRR. Notably, the advantage persists on ICEWS05-115 15 (longer tempo- ral horizon) and ICEWS18 18 (larger, denser graph), with improvements of 15.0% % and 24.3% respec- tively. Such robustness across varying time spans and graph dynamics indicates that the proposed Beaprroo--RRpeeeeettttnnnnneeeee
pipeline enables reliable inductive generalization.
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Thirdly, on GDELT, large and rapidly evolving dataset, TRANSFIR still outperforms all baselines, with an MRR gain of 50.5%. We attribute this to the latent semantic cluster that supplies strong categorical priors for emerging entities and propagates cluster-level dynamics.
5.3 REPRESENTATION AND LEARNING ANALYSIS (RQ2)
We analyze what TRANSFIR learns during training and how it addresses the challenges of emerging entities, summarized in Fig.
LogCL
Ours
Country
1. France
Transferable Patterns
2. Canada
fq-22
The United Stutes
Prrne Minoner
Make
B. Civic Parties
Prime Ninister (itomania)
(Mexico)
(Madbo)
(Mesic))
Prositental Gandidate (Molool
support
C. Citizen
Cliiznn (Nigeria)
Presidertial
(Ciluoo Rutte 00877
(Thalland)
Children (Canada)
Known Entities
Emerging Entities
Predict Result: Gov(Mexico)
(a) Representation Collapse of baseline and model
(b) Case Analysis for Cluster and Emerging Entities
Figure 4: (a) t-SNE visualization showing the improved separation of clusters in TRANSFIR com- pared to LogCL, with Collapse Ratio improvement from 0.0055 to 0.8677. (b) Case analysis of three representative clusters and how TRANSFIR transfers reasoning patterns to emerging entities.
(a) Representation Quality and collapse. Compared to LogCL, TRANSFIR produces well- separated clusters in embedding space, rather than single dense cloud. The Collapse Ratio im- proves markedly from 0.0055 to 0,8677, indicating that embeddings of emerging entities remain well distributed and informative. These results suggest that the VQ codebook, combined with pat- tern generalization, jointly prevent representation collapse and yield informative embeddings.
(b) Cluster Structure and Emerging Entities. closer look three latent semantic clusters identified by the codebook reveals semantically coherent, type-like groupings: A. Country (e.g., France, Canada, United States); B. Civic & Parties (e.g., Prime Minister (Romania), Officials (Mexico), Presidential Candidate (Mexico)); C. Citizen (e.g., Citizen (Nigeria), Student (Thailand), Children (Canada)). Emerging entities (red) are consistently categorizes to appropriate clusters alongside known ones (blue), providing type-level priors even without historical interactions.
(c) Case study. Consider the query: "Where did the presidential candidate in Mexico make a state- ment tgg" ?" This query is structured as( Presidential Candidate (Mexico), MAKE STATEMENT,
TRANSFIR retrieves transferable pattern chains from the B. Civic & Parties cluster, including: (i)
Gov (Romania)), and (iihintinrcountry patterns (Officials (Mexico) MAKE STATUMINT Gov (Mexicol). By leveraging such transferable supervision signals, TRANSFIR successfully predicts Gov (Mexico). illustrates how TRANSFIR utilizes cluster-level priors to extract transferable patterns, enabling reasoning on emerging entities.
Cross patterns, such as (Prime Minister (Romania MAKE STATEMENT
5.4 ABLATION STUDY (RQ3)
We conduct ablation experiments to evaluate the contribution of each module in TRANSFIR. The following variants considered:
-IC: removing the Interaction Chain construction and using only entity embeddings.
-Codebook: removing the codebook mapping and using static clustering features only.
-Pattern Transfer: removing the pattern transfer mechanism and using statie representations.
-Textual encoding: removing frozen textual embeddings and using random initialization.
The results are summarized in Fig Across all four benchmarks, removing any individual module leads to a decline in performance. While the relative impact of each ablation varies across datasets,
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MI w/a interaction chain construction
M3 w/o pallern transfer
TransFIR (Ours)
M2: w/o codebook driven cluster mapping
M4: w/o textual encoding
ICEWS14
ICEWS18
ICEWS05-15
GDELT
0.34
0.24
0.38
0.24
Hits@a
0.17
0.19
0.00
M1
M2
MB
M4
Ours
MI
M2
M3
M4
Ours
M1
M2
M3
M4
Ours
M1
M2
M3
M4
Ours
Figure 5: Ablation study results on four benchmarks, showing the performance impact of removing different components. Reported results are for Hits@ 10, with additional metrics in Appendix F2
two consistent patterns emerge (i) removing the codebook-driven mapping or the pattern- trateen-transfer typically results in the most performance drops, highlighting the need to both mapping entities and propagate pattern signals; (ii) removing IC construction or textual encoding also degrades perfor- mance. These findings demonstrate the complementary functions the modules.
Besides, we observe that in GDELT, removing textual encoding can sometimes lead to better perfor- mance. We believe this is due to the quality of the input text in GDELT, where entity titles often in- clude abbreviations and symbolic elements (e.g., "EGYPT (EGY @ OPP REF LEG SPY...),, which makes challenging for the textual encoding module to extract clear semantic information. Incor- porating external knowledge sources to enrich entity descriptions may further enhance TRANSFIR 's performance under such conditions.
5.5 EXTENDED EXPERIMENTS (RQ4)
To evaluate the generalization capability and robustness of TRANSFIR in inductive scenarios, we
conduct four additional experiments: (i) performance under the Unknown setting, (ii) robustness across different temporal splits with varying entity emergence rates; (iii) sensitivity analysis to key hyperparameters; (iv) model sensitivity to different pretrained language models; and (v)model effi- ciency in GPU memory and computational time.
0.22
Unknown
Emerging
Generalization to the Unknown Setting. We further assess TRANSFIR in a more permissive inductive setting where test entities, although unseen during training, may have historical in- teractions observable at inference time (i.e., Ger is observable). This differs from the strieter Emerging setting, where entities arrive without any interactions. As illustrated in Fig. 6 all meth- ods exhibit improved performance compared the Emerging setting, suggesting that even limited test-time historical context benefits model inference. TRANSFIR maintains a stable perfor- mance advantage, highlighting its ability to effectively leverage local interaction patterns for inductive reasoning. Complete re- sults and experimental details are available in Appendix F.3
MRR
0.11
0.00
RIGUN Lnght
HisRa MILDMD Dhrs
0.38
Unknown
Fmerging
11
0.19
0.00
REGON LogeL HisRes MLEMKD Ous
Figure 6: Experiment results on ICEWS14 under the Unknown and Emerging settings.
Robustness under Different Temporal Splits. We construct
four chronological data splits by varying the test horizon to 10%, 30%, 50%, and 70% of the full timeline. For each ratio, we re-partition the dataset chronologically into training, validation, and test sets. This setup reduces the observed historical context and increases the proportion of emerg- ing entities over time. We compare TRANSFIR against strong baselines strong baselines- LogCL, LogCL, REGCN, and MLEMKD. Across all splits, TRANSFIR achieves the best MRR/Hits @ 10 and ex- hibits the smallest degradation emergence increases, demonstrating robustness to reduced histor- ical coverage. Detailed partitioning protocols and full results are provided in Appendix F4
Hyperparameter Sensitivity. We analyze the sensitivity of TRANSFIR to several key hyperpa- rameters: codebook size K, Interaction Chain length k, hidden dimension d, and number of layers

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GPU Memory Usage
Training Analysis ICEWS14
Memory (MB)
125.00
116640
0.15
MGESL
REGENN
10000
MRR
LoyCl
Ha
75.00
FiunsFil
5564.
1000
3301.3
0.02
NGESL
LegeL Hates MLE TrunFFiR
100n
2000
3060
4000
2003
6000
7000
Model
Training Time (seconds)
Figure 7: GPU memory usage and empirical rumning time on ICEWS14. TRANSFIR achieves significantly lower peak GPU memory usage while maintaining competitive training speed.
L. TRANSFIR demonstrates consistent performance across wide range of parameters. Our ex- periments show that datasets with greater diversity (e.g.. ICEWS18) require a larger number of codebooks. For chain length, most datasets achieve best performance at k = 30, longer chains potentially introducing noise. Comprehensive experiment results can be found in Appendix F.5)
Different Textual Encoder, We investigate the im- pact of different pretrained language models(PLM) on TRANSFIR, as its latent semantic clustering re- lies textual representations. Specifically, we eval-
Table 2: Different Textual Encoder exper- iment result on TRANSFIR.
PLM
ICEWS14
ICEWS18
GDELT
uate four widely-used PLMs: BERT (Devlin et al. 2019) RoBERTa (Liu et al. 2019). T5 (Raffel al. 2020). and Diven3-Embeddigg (Zhang et al.) (0225b).
Bascline
0.2273
0.1797
0.1131
T5
0.3057
0.2061
0.2082
shown in Table 2 TRANSFIR consistently outper- forms the strongest baseline across all pretrained lan- guage models, demonstrating both its robustness and adaptability.
RoBERTa
0.2934
0.1939
0.2289
Qwen3
0.2567
0.2009
0.2030
BERT
0.3246
0.2324
0.2278
Model Efficiency in GPU Memory and Training Time. We evaluate the efficiency of TRANS- FIR in terms of the runtime and GPU memory usage, comparing it against several strong baselines, including HisRes and LogCL., on the ICEWS14 dataset. As shown in Fig. 7 TRANSFIR achieves significantly lower peak GPU memory usage while maintaining competitive training speed, demon- strating strong efficiency and scalability. This suggests that TRANSFIR is not only effective in performance but also efficient in resource utilization, making it scalable for large-scale datasets and suitable for long-term applications in temporal knowledge graphs (TKGs). This efficiency is crucial for real-works deployment, where both computational resources and time are limited.
CONCLUSION
In this work, we introduce TRANSFIR, novel inductive reasoning framework designed to handle emerging entities in temporal knowledge graphs. By leveraging transfcrable reasoning patterns and utilizing interaction-aware codebook, TRANSFIR effectively bridges the gap for emerging tities in the absence of historical interactions. Experimental results demonstrate that TRANSFIR outperforms strong baselines across multiple benchmarks, with significant improvement in MRR on four datasets, showcasing its ability to perform effective inductive reasoning on emerging entities.
In the future, plan improve TRANSFIR by enhancing entity textual embeddings through ternal knowledge and L.MMs to handle noisy or sparse entity descriptions. We also aims to extend TRANSFIR handle emerging relations and explore its application in more open-world scenarios. Furthermore, we will investigate methods to model long-term evolution of entity semantics, enabling TRANSFIR adapt changing knowledge over time.
ACKNOWLEDGMENTE
The authors of this paper were supported by National Natural Science Foundation of China (No. 92579211, 62272301, T2421002, 62525209, 62432002), Postdoctoral Fellowship Program of CPSF under Grant Number No. GZB20250806. the Al for Science Seed Program of Shanghai Jiao Tong University (project number 2025AI4S -YO0)) and Natural Science Foundation of Shanghai 10.21101400214.
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7 ETHICS STATEMENT
This work complies with the ICLR Code of Ethics. Our research involves no human subjects, sensitive personal data, or experiments that could cause harm individuals, communities, or the environment. All datasets utilized are publicly available and commonly used within the research community. The methods introduced this work are intended for general machine learning research purposes and present no foreseeable risks of misuse or harmful applications. To the best of our knowledge, this study raises no conflicts of interest or ethical concerns.
8 REPRODUCIBILITY STATEMENT
We have taken concrete steps to ensure the reproducibility of our work. The full implementation details of our models, training setup, and baselines are provided in Appendix E To further en- hance reproducibility, we make our code publicly available at the following anonymous repository: These resources should enable independent verifi-
hutps://githb.. www. hwwm//haadzzuung2233//mm cation of our results.
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A LLM USAGE DISCLOSURE
In this work, we used GPT-4o to assist with grammar checking and polishing. All LLg--errerteedd content thoroughly reviewed and validated by the authors to ensure the accuracy of the pre- sented information. Additionally, the items representing "President" and "Government" in Fig. [ and Fig. 2 are generated by GPT for illustrative purposes. The use of LLM aligns with ICLR's ethical guidelines, and all contributions from the LLM have been transparently acknowledged and reviewed to avoid any false or misleading statements.
B RELATED WORK
Reasoning on Temporal Knowledge Graphs. Reasoning on temporal knowledge graphs (TKGs) aims to infer missing or future facts by modeling temporal evolution. Prior work falls into inter- polation (filling unobserved facts within the time window) and extrapolation (forecasting beyond training) Cai et al. (2023). Representative interpolation approaches extend static models with tem- poral mechanisms: TTransE. introduces temporal constraints across adjacent facts Gareia- Duran et al. (2018); TNTComplEx employs fourth-order tensor for time-aware entity/relation embed- dings Lacroix et (2020); TEILP parameterizes temporal logical rules with neural modules Xiong
al. 2024b).
In contrast, the extrapolation setting focuses on predicting future events using only historical in- teractions, without access to future information during training. Extrapolative methods typically aggregate historical interactions and capture cross-time dependencies: CyGNet uses time-aware copy mechanisms for recurrence Zhu al. (202I); CENET applies contrastive learning to disen- tangle historical vs. non-historical influences Xu et al. (2023b); LogCL. blends local and global temporal context [Cen al. (2024). More recent approaches leverage Transformer architectures
and large language models (LLMs): GenTKG combines eurieval-augmente generation with in- struction tuning [iao al. (002)); LLM-DA dynamically updates temporal rules for domain adap- tation Wang et al. (2024b); and ECEformer encodes chronological event chains using a Transformer
structure Fang et al. (2024).
Despite these advances, a key challenge remains unaddressed: handling emergent entities that ap- pear during graph evolution. Current methods operate under a closed-world assumption and typ- ically initialize the embeddings of all new entities randomly. As these emerging entities lack any historical interactions during the training phase, the absence sufficient supervision often results in representation collapse.
Knowledge Graph Inductive Learning. Inductive learning on static KGs aims to generalize to unseen entities/relations, or even entirely new graphs without retraining under fixed vocabulary. Classical approaches such as GralL Teru et al. (2020) and TACT Chen et al. (2021) reason from local subgraph structure and relational patterns, reducing reliance pre-learned entity embeddings. Recent work strengthens inductive reach from complementary angles: INDIGO enables fully induc- tive link prediction directly from GNN outputs Liu et al. (202T); ) MorsE employs meta-learning to transfer knowledge for initializing unseen- unseen-entity embeddings Chen et al. (2022); InGram integrates relation-awar attention to better handle novel relations Lee al. (2023b); ; and ULTRA learns con- ditional relational representations for zero-shot generalization across different graphs Galkin et al. 2024).
However, these methods are designed for static KGs, where new entities typically possess at least some known relations. Few works focus on inductive reasoning in TKGs. ALRE-IR (Mei et al.) 2022) combines embedding-based and logical rule-based methods to capture deep causal logic, demonstrating strong zero-shot reasoning capabilities. zrL.MM (Ding et al.. 2024) leverages large language models to generate relation representations from text descriptions, enabling reasoning for unseen relations. POSTRA (Pan et al. 2025) enables cross-dataset knowledge transfer through si- nusoidal positional encoding.
Despite these advances, they overlook the fact that emerging entities in temporal knowledge graphs often arrive without any historical interactions, common scenario in real-world applications. The absence of relational context makes it particularly challenging to derive meaningful representations for such entities.
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C EMPIRICAL STUDIES
C.I VISUALIZATION DETAILS FOR Q2
For the visualization study, adopt LogCL the base model. We record entity embeddings at two stages: (i) init, right after model initialization, and (ii) trained, after convergence on the training set. Entities are categorized known if they are present in the training data, and as emerging otherwise.
All embeddings are first reduced to 50 dimensions via PCA and then projected into 2D space using t-SNE (perplexity=30, 2000 iterations). The main text presents visualization results ICEWS14, while additional plots for other datasets are provided in Appendix [22 for comparison with TRANS- FIR. Known and emerging entities are distinguished by color facilitate comparative analysis.
F.2
C.2 REPRESENTATION COLLAPSE AND COLLAPSE RATIO
Representation Collapse. In representation learning, collapse refers to degradation in the pressiveness the embedding space, where multiple input instances are mapped to (approximately) identical points confined to low-rank subspace. This phenomenon typically manifests ishing variance along principal directions, rank deficiency, or excessively homogeneous node repre- sentations in graph models Thrampoulidis al. (2022); Jing al. (2022). Common causes include inadequate supervision, degenerate learning objectives, or limited contextual information. As a re- sult, collapsed representations exhibit poor separability and diminished generalization performance.
Collapse Ratio. In TKGs, emerging entities arrive with no historical interactions, so their learn- ing signal is under-constrained and easily pulled toward generic priors. To quantify this, let X = { C Id be centered set of embeddings with covariance estimator Σx. We measure dispersion the generalized variance (the geometrie mean of principal-axis standard deviations)
GS(X) det Σx) 2d
which decreases whenever variance collapses along any eigen-direction and iuation-invariante derson et al.] (1958); Zhootar et al. (2021). For numerical stability when n < d or directions are nearly collinear, we compute log det (Exx from the (nonnegative) eigenvalues of Ex. Given an emerging set Xemerg and reference set Xeer(egg. the set of known entities), we define
CR = GS(Xemerg)
GS(X
Values < indicate collapse (e.g., CR 0.2 means the average per-axis scale is 5x smaller). Be- cause GS summarizes the available variance across all informative directions, lower Collapse Ratio corresponds to reduced separability and weaker discriminative capacity of emerging-entic represen- tations. We report Collapse Ratio alongside t-SNE visuals as quantitative evidence of representation collapse.
D ADDITIONAL DETAILS OF METHODOLOGY
D.1 CHAIN STRUCTURE MOTIVATION
In this section, we provide additional motivation for modeling historical interactions as Interaction Chains (ICs).
Sequential nature of temporal reasoning. Reasoning over temporal knowledge graphs often in- volves sequential dependencies akin multi-step inference paths. For example, consider en- tity representing a person with interactions such as "visited Country A at tt followed by "visited Country B at tz " At later time typ predicting that this person may "visit Country C" often depends on the sequential chain of prior visits, rather than an unordered set neighbors. Such sequential dynamics difficult capture when historical interactions are aggregated as bag-of-events.
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Entity-invariaa temporal patterns. Many chains reflect patterns that are largely intity-ivvariant (e.g.. successive state visits). Organizing history into chains exposes such transferable regularities, enabling generalization emerging entities with prior representations; in contrast, updating static emheddings tends to overfit well-observed entities and fails to extrapolate.
Benefit of chain formulation. By preserving the temporal order of events, the chain formula- tion naturally captures the progression of interaction dynamics, making it particularly suitable for inductive temporal reasoning. Our proposed Interaction Chain (IC) design offers principled ap- proach to extracting reusable temporal patterns directly from raw interaction logs, thereby forming the foundation of our framework.
D.2 ALGORITIM FLOW AND PSEUDOCODE
Here we include a detailed pseudocode of our framework TRANSFIR, covering the Classifica- -e--eresennttion--Genanaaaiiiiie pipeline.
Initialization. Entity textual embeddings (he Jeee are obtained with a pretrained BERT encoder and kept frozen. Learnable parameters include relation embeddings {h, |.ER, the IC encoder O (component-wise MLPs, Transformer, query-aware attention), the VQ codebook C = {cu ] the Training and inference proceed strictly in
drift MLP Vr, and the scoring module f(-) chronological order.
Training-time Flow (per timestamp) At each timestamp t with query set Q ((e,,,,3,8) TRANSFIR executes:
Ii Classification quantize frozen he to the nearest codeword in C; get VQ losses Loodebook
(ii Representation build and encode an IC for each query g, yielding b
(iii) Generalization form cluster-level dynamic prototypes (c by pooling (h } per cluster of the query entity; propagate temporal transfer to non-query entities via h. h. + v [he (())))
dyn
"s(e)-
(iv) Ranking & Loss score candidates with ConvTranse and optimize L = Lip ACoodebook For implementation details and the step-by-step routine, please refer to Alg.
D.3 COMPLEXITY ANALYSIS
We analyze the time and space complexity of TRANSFIR per timestamp f. Let n4, [2l be the number of queries at f, l the Interaction Chain length (Top-k), d the hidden size, the number of Transformer layers, K the codebook size, the hidden width of the drift MLP, and []) R = R.
Codebook (classification). Vector-quantized assignment has worst-case time (EEdd) per update search) and space O (Kd) for the codebook. Because entity text embeddings are frozen, assignments can be cached and updated lazily; thus the amortized assignment cost is small relative to encoding.
IC construction(rprreentttio IC construction keeps bounded chain of length k for each query, yielding time On,kkd for token projections. The Transformer encoder dominates with
On, L 4k2d+
(attention and feed feed-forward), and memory Onnykd) for activations.
Pattern transfer (generalization) Forming cluster prototypes requires O(n,d Kd). Broad- casting drift via the MLP costs O(Emd) with space ((E)) for (temporary) updated embeddings. In practice apply drift only non-query entities at f.
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Algorithm TRANSFIR Training (per epoch, chronological)
Require:
Train timestamps {l,..., Itrain J: frozen entity embeddings (he) jegg learnable { FER. IC encoder ene
VQ codebook C = {cx [c Rm
transfer MLP V,
scorer f Eo) window T, Top-k.
1: for epoch 1,2,.... do for timestamp = 1 to terain do
2:
3:
2 <- re.
All queries time /
4:
(1) Codebook Mapping (Classification) for each e E & do (e) -- arg ming ||h. - cx cx1
5:
6:
VQ assignment
7:
end for Leodebook f Leb + B Leammit
Lob Ee ||gg(h,] cr(e 11 Ccommit < -e lb, sg|c
8:
9:
10:
(2) IC Encoding (Representation)
11:
for each q = (eg: ?,t) E 2, do
12:
Cgt [(ss ri, 0i, 44) 1-T <t, eg (s,
13:
C2 Topp,,
(Tipp,ssim(Ma (sim(h,, hr,). Co)
D cosine sim
Encode ( with Oncii relation-guice guided attn IC end for
14:
15:
16:
(3) Temporal Pattern Transfer (Generalization)
17:
Group (b,,) by ((g)); for k=1.. cx
dyn
beg
IC
1Qul
ex: ((e)()))
18:
S {eg Tq: ?, E 2t}
P Query entities at time
19:
for each E do
20:
Ze - [he *(c)
We + (22)
21:
h. + he - We (((e)
dyn
22:
end for
23:
Ranking & Loss
24:
Lip +
25:
for each q = (es) ?,t)
Score all (or sampled) eo: obes. Ty: eo, = (fhe, hy h..)) Lip += log softmaxe, (ő(es: Py. Co, t))
27:
28:
end for
29: Update by backprop on L. Lip + X Lenetebook: update (hr}, Oenc, V,
30:
end for
31:
end for
Overall.
Ignoring the shared scoring cost, the dominant model-specific comppepeiiccccccoimioeeccccc of TRANSFIR per timestamp is
On L (k2d+ kd))) + O(EKd) + O(Emd)
Since k. L, K, and m are small constants (e.g., k 32), TRANSFIR scales linearly with the number of queries and entities, and its controllable chain length avoids dependence on the full neighborhood size.
E ADDITIONAL EXPERIMENTAL SETTINGS
E. DETAILED DATASET INFORMATION
Table E.I presents comprehensive statisties for all datasets, encompassing entity counts, relation counts, fact counts, and the proportion of emerging entities in validation and test splits. We uti-
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lize four temporal event datasets spanning crisis early- warning contexts and diverse global event landscapes to evaluate the model's multi dimensional performance.
Table 3: Statistics of all datasets, including ICEWS14, ICEWSI8, ICEWS05-15 and GDELT.
Dataset
Entities
Relation
Time Snapshots
Total Triples
Emerging Entities
ICEWS14
7128
230
365
90730
1301
ICEWS18
23033
256
304
468558
3434
ICEWS05-15
10488
251
4017
461329
1954
GDELT
7691
240
2976
2278405
1020
Irivel et al. (2017)): subset of the Integrated Crisis Early Warning System (ICEWS) dataset for 2014, focusing short-term conflict events within single year. After prepro- cessing (e.g., entity standardization, confidence filtering). it contains high-frequency event types (e.g., protests, attacks). It is used to evaluate the model's performance in local temporal window event prediction.
ICEWS18 (Boschee et al. 22055)): The 2018 ICEWS dataset, maintaining the focus crisis events but introducing emerging subtypes (e.g.. "economic sanctions") to reflect modern conflict dynamics. tests the model's 's cross-year stability and adaptability emerging event types.
ICEWS05-15Cin et al. (2019)) : A long-term crisis dataset covering 2005-2015, including histor- ical events such as financial crises and regional conflicts. Characterized by sparse daily events and a large time span, it serves as the primary training set to validate the model's long-term temporal dependency modeling and generalization under low-resource scenarios.
Leetar & Schrodt (2013)): The Global Database of Events, Language, and Tone, cov- ering political, economic, and cultural events beyond crises. It complements ICEWS by including non-conflict scenarios, enabling validation of the model's cross-domain generalization and utiliza- tion of multi-dimensional information.
E.2 BASELINES (OVERVIEW AND IMPLEMENTATION)
Families. Graph-based (temporal GNN/mbeeding:: mostly transductive), Path-based (query- centered relational paths reasoning rules), and Static inductive (inductive graph learning but without temporal encoder).
Implementation. We follow chronological splits (5:2:3) consistent with the main paper. For Graph-based and Path-based methods, keep the original settings and only adjust the temporal split and test set to fit the emerging-entipy evaluation. For fule-mining approaches (e.g.. TILP) with high search complexity, we reduce the maximum rule length from to control computation while preserving the core mechanism. For Static inductive methods, which assume static graph, we merge small window timestamps (e.g., 7) into subgraph to run, and we inject relative time into features to enable comparison under the same prediction protocol.
(ICEWS) and (GDELT)
Baseline briefs.
CyGNet [GRAPH] Zhu al. (2021). Sequential copy-generation with time-aware dual-mode in- ference to predict recurrent and de novo events.
2021). Recurrent GCN that learns evolving entity/relation states by capturing temporal structural patterns and injecting static constraints.
REGCN [GRAPH] Li et al.
HiSMatch [GRAPH] Li et al.
2022). Historical structure matching with entityfrelationntime seman- tics and sequential cues; backgroun knowledge improves matching.
MGESL [GRAPH] Mingcong et al. (2024). TKG reasoning model combines multi-granulariin his-
tory and entity similarity via hypergraph convolution, includes candidate-know setting.
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gooal-global contrastive learning with entity- aware attention to mine query-relevant histories and suppress noise.
LogCL [GRAPH] Chen al. (2024).
HisRes [GRAPH] Zhang (202a). Historically relevant event structuring with multi culli-graaularity
evolution and global relevance encoders, fused adaptively.
MLEMKD [GRAPH] Qian et al. 2025). Mutual-leaming KD for temporal KGs using soft-label
filtering and adaptive distillation curb anomaly diffusion with minimal drop.
TLogic [PATH] Liu al.
2022). Time-constrained random-walk rule mining that yields time-
consistent explanations and competitive forecasting.
TLIP [PATH] Xiong al.
20244)) Differentiable temporal rule learner extracting interpretable patterns via constrained walks and temporal features.
ECEformer [PATH] Fang et al. (002)). Transformer over Evolutionary Chains Events with intra-
quadruple representation and inter-quadruple context mixing.
GenTKG [PATH] Liao et al. (2024).
Retrieval-augmenned generation: temporal rule retrieval few-shot instruction tuning for LLM -ased forecasting.
(2020). Multi-relational GCN with relation composition operators, unifying KG embedding tricks beyond plain graph conv.
CompGCN [INDUCTIVE] Vashishth al.
ICL [INdUCTIVE] Lee et al. (2023a) TKG forecasting via in-context learning with LLMs requires no fine-tuning or prior semantic knowledge and performs competitively on benchmarks.
PPT [INDUCTIVE Xu al. (2023a) TKG completion uses pre-trained LMs and time prompts, via masked token prediction, with competitive benchmark results.
MorsE [INDUCTIVE] Chen
(2022). Meta-knowledge transfer that learns entity-agnostic struc-
tural priors for unseen entities via relation- initialization.
InGram [[NDUCTIVE] Lee et al. (2023b). Inductive KG embedding using relation-affinity graphs and attention-based aggregation form embeddings for unseen nodes/relations.
E.3 EVALUATION
Metries fined
We use two standard metrics: Mean Reciprocal Rank (MRR) and Hits@K. MRR is de-
1 N MRR
+ N
where is the rank of the correct answer for the i-th query. Hits@K measures the proportion of queries for which the correct answer is ranked in the top K.
Experimental Setup We evaluate all models on emerging entity-related quadruples using MRR, Hits @ 3, and Hits@ 10. For inverse relation triples (e., r- .e, tg), we also perform tests, and report the average of both directions. During testing, we follow the same filtering strategy as LogCL[Chen al. (2024), excluding quadruples involving the same query entity and relation at the same times- tamp to avoid redundant results.
All experiments of TRANSFIR are conducted with three random seeds, and the reported results are the averages across these runs. Detailed results are presented Table [ Note that GenTKG generates 10 samples to compute Hits, so MRR values are not available for this method.
F EXTENDED EXPERIMENTAL RESULTS
F.1 REPRESENTATION AND LEARNING ANALYSIS (RQ2)
Representation quality and collapse. We further evaluate TRANSFIR's ability to represent emerging entities through t-SNE visualizations across multiple datasets. As illustrated in Fig. g TRANSFIR consistently yields well-separated clusters in the embedding space, in contrast to LogCL, which only distinguishes between emerging and known entities, resulting in distribu- tion shift between their embeddings. In comparison, our approach clearly groups emerging entities into distinct latent semantic clusters. The Collapse Ratio is significantly improved across all four
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datasets, underscoring the effectiveness of our VQ codebook and pattern transfer mechanism in preventing representation collapse. This enhancement enables the model to produce informative embeddings that support inductive reasoning for emerging entities.
ICEWS14
ICEWS18
ICEWS05-15 15
GDELT
LogCL
CR: 0.0055
CR: 0.0241
CR: 0.0220
CR: 0.29660
Ours
CR: 0.8677
CR: 0.8867
CR: 0.8787
CR: 0.8622
Known Entities
Emerging Entities
Figure 8: I-SNE visualizations comparing LogCL and TRANSFIR on multiple datasets. The top row shows LogCL embeddings. with a clear representation collapse for emerging entities (red). The bottom row shows TRANSFIR, where emerging entities are well-separated into latent semantic clusters, significantly improving the Collapse Ratio across all datasets.
Failure case analysis. To provide a deeper understanding of the model's limitations, provide a failure case due to insufficient semantic information in dataset (CEWS14: ((Court Judge (Nigeria), INVESTIGATE, (Bala Ngilari). (184 ). In this case, the emerging entity Bala Ngi- lari lacks sufficient semantic information in the textual input. Since TRANSFIR relies semantic- based clustering to align emerging entities with known entities, the absence of meaningful textual features prevents the model from assigning Bala Ngilari to the correct latent semantic cluster. Con- sequently, the model fails to infer that Court Judge (Nigeria) will investigate Bala Ngilari at t184-
F.2 ABLATION: ADDITIONAL METRICS (RQ3)
Beyond Hits @ 10 reported in the main paper, further evaluate the ablations MRR and Hity@ 3. As shown in Fig. | the qualitative conclusions remain unchanged across four benchmarks: (i) re- moving the codebook mapping yields the largest drop, confirming the importance of aligning enti- ties into latent semantic clusters for reliable transfer; (ii) both IC construction and pattern transfer contribute consistently; (iii) discarding textual encoding degrades performance, since text provides a stable prior for emerging entities. Results are averaged over three random seeds; error bars denote standard deviation.
F.3 GENERALIZATION TO THE UNKNOWN SETTING(RQ4)
Definition. We keep the temporal KG notation G = (GehEe with G, (E Rt).). Let the timeline be split into disjoint windows Ti., Tral, Teo. For any window W C CT, define the entity set Ew = {el 3(,,,,,e,,)) EF,, LEW, eE (a) }. The Unknown entity set is
Eunk = EYr. \ (ET, TTa))
During testing, we evaluate queries of the form (e,, r, ?, tg) or (?, tg), where ty E Tte and the queried entity Eunk. Unlike the Emerging setting (Sec. ( which enforces ty = te(e) (zero history), the Unknown setting allows the model to observe local pre-query history within the test window, defined
Hie
U
F.
iET Ktt
while future facts (2 f.)) remain hidden.
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MI: w/o interaction chamn construction
M3: w/o pattern transfer
TransFIR (urs)
M2: w/o codcbook driven cluster mapping
M4: w/o textual encoding
ICEWS14
ICEWSIS
ICEWS05-15
GDELT
0.18
0.12
0.22
0.12
MRR
0.09
0.06
0.11
0.06
0.00
0.00
0.00
0.00
M1
M2
M3
MI Ours
MI
M2 M3 M4 Ours
MI
M2
M3
M4 Ours
MI
M2
M3
M4 Ours
ICEWS14
ICEWS18
ICEWS0S-15
GDELT
0.22
0.14
0.26
0.14
1H
1111
Hix
0.07
0.07
0.00
0.00
0.00
0.00
M1
M2
M5
M4 Ours
MI
M2
M3
M4 Ours
MI
M2
MB
M4 Ours
M1
M2 M3 M4 Ours
Figure 9: Ablation results on four benchmarks under MRR (top row) and Hits@3 (bottom row). The ranking of variants mirrors the main main-paper Hits@ 10.
0.14
Unkmown
Emenying
0.34
Unknown
Emerging
0.12
Unknown
Emerging
MRR
0.07
0.17
0.06
0.00
0.00
0.00
REGON LogCE
HicRos MLFMDD Oues
LogCI
REOCN TayCL HisRos MI FMKD
Dun
0.26
Unknown
Emerging
0.52
Emerging
0.22
Chnknown
Finrgiig::
H LogCL
Hits@::0
0.13
0.26
0.11
0.00
0.00
0.00
LogCl HiRts MLEMKD Out.
LogCl. HidRes MLEMKD
(OICEWSIS
(bJICEWS0S-15
(oIGDELT
Figure 10: Results on ICEWSI8, ICEWS05-15, and GDELT under the Unknown (blue: observable Get) vs. Emerging (green; zero history) settings. All methods improve with pre-query history, and TRANSFIR remains best on both MRR and @its@ 10 across datasets.
Relation to the Emerging setting We distinguish between two test settings. Let The denote the test window, and let H. Ulst, F; represent the test-time history available prior to time tq-
Emerging. In this setting, queries restricted the first appearance of entity. For target entity the query time is set to L, = (e) (its emergence time). Consequently, H/s contains no prior interactions involving e (strict hiro-history condition).
Unknown. Here, entities also unseen during training and validation. However, queries can at any time 1g > (e( within the test window T.. Therefore, H may include earlier test-time interactions of e, providing a short local history. In practice, since an unseen entity can appear multiple times during testing, evaluate its predictions specifically non-first occurrences. This allows us to isolate the benefit of having limited test-time context.
Experiment Results. As shown in Fig. 10 across all datasets, every method achieves higher MRR and Hits @ 10 scores in the Unknown setting than in the Emerging setting, confirming that even brief interaction histories (G are beneficial. TRANSFIR consistently outperforms all baselines on ev- ery dataset and metric, maintaining clear advantage when test-time history provided. This
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ICEWS14
ICEWS14
ICEWS14
ICEWS14
0.161
0.38 9.161
0.35 0.163
0.33 0.105
0.35
0.102
0.32
.032
0.33
0.32
0.155
0.31 0.55
+0.1 0.15
0.31 0.155
0.190
0.190
0.100
0.150
100
3024
ICEWS18
ICEWS18
ICEWS18
ICEWS18
0.320
0.120
0.120
0.120
0.115
0.28
0.115
0.33
0.110
0.22
0.110
0.110
0.22
RMMGE
0.301
0.303
0.105
0.105
0.100
0.20
100
1024
ICEWS05-15
ICEW505-15
1CEWS05-15
ICEWS0S-15
0.30
0.22
0.39
0.30
0.39
0822
00.30
0.21
6.30
0.21
0.33
0.21
BMSS
0.37
0.20
0.37
0.20
10
25
00
100
15
30
00
256
012
765
1024
K
GDELT
GDELT
GDELT
0.110
9.110
0.110
0.110
0.103
0.005
0.100
0.100
0.32
0.091
0.000
HAL
0.045
MBC
0.20
0.20
0.090
0.000
0.000
0.000
10
25
30
300
15
31
50
230
512
765
1024
Figure 12: Hyperparameter study on four benchmarks, exploring effects codebook size K, Inter- action Chain length k; hidden dimension d, and the number of layers Brown and green represent MRR and HITS @ 10, respectively.
suggests that TRANSFIR effectively leverages both local historical patterns and type-level regular- ities, while baseline methods rely primarily entity-specific history and still fall short. Overall, these results demonstrate the robust inductive generalization capability of TRANSFIR: its perfor- mance gains do not hinge the zero-history setup, and its superiority persists more historical context becomes available.
F,4 DETAILED RESULTS FOR DIFFERENT TEMPORAL SPLITS(RQ4)
To test generalization under varying emergence, we build four chronological splits with test horizons of (10% 30%, 50%. 70%]. corresponding to traint timeline ratios [8 : 1]. 3:3 [3: 2: 5). [2: :::7: For each split, we re-partition the data strictly in time (validation is re-cut per split), which short- ens training history and increases the share of first-appearance entities. We evaluate
Transtr/ROeer - LogeL + MLUMKD RBBOO
0.39
ICEW505-15 ICE
0.26
0.13
0.00
10%
30%
50%
70%
ICEWS14 and ICEWS05-15, report- ing MRR and Hits 10, and compare TRANSFIR against strong baselines (LogCL, REGCN, MLEMKD); all models are re- trained for each split with the main hyperparameters.
0.46
0.34
0.22
0.10
10%
30%
50%
70%
different time split
Results and discussion. As shown in Fig. F.4 performance drops for all methods as the test horizon expands and emer- gence increases. TRANSFIR consistently attains the best scores across splits and exhibits the smallest degradation, indicating ro- bustness when historical coverage is reduced. A mild uptick for some baselines at the 70% horizon likely stems from un- dertraining with a shorter history, which narrows the gap between known and emerging enti- ties and partially curbs collapse. Overall, these trends support that TRANSFIR Classifica- Rion-Representationn-GGe Generaaaiionnneeeeeereiiiiitio pipeline remains effective across diverse temporal partitions.
Figure 11: Experiment results on ICEWS14 and ICEWS05-15 un- der different time splits.
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F.5 HYPERPARAMETER (ENS)TIVITY(RQ4)
We investigate the hyperparameter sensitivity of TRANSFIR, focusing on codebook size K, Inter- action Chain length k; hidden dimension d, and the number of layers L in the IC Encoder.
First, we examine the impact of codebook size K, testing values {10, 25, 50, 100). As shown in Figure [2] performance improves with increasing K, with K 50 yielding the best results across most datasets,
Next, analyze the effect of Interaction Chain length k testing values {10. 15, 30, 50). While the best length varies across datasets, the performance remains stable across different lengths for all datasets, with no significant drop in performance.
Additionally, we assess the hidden dimension d and the number of layers L. Performance is stable across a range of hidden dimensions, with d 768 providing optimal results most cases. Sim- ilarly, two to three layers in the Chain Encoder provide the best performance, with no significant improvement from adding more layers.
24


Reviews:
[{"review_id": "4I7i52FEVs", "reviewer": "Reviewer_h8RY", "summary": "The paper proposes TRANSFIR, an inductive reasoning framework for temporal knowledge graphs, designed to handle emerging entities that appear without historical interactions. The authors first conduct an empirical investigation showing that approximately 25% of entities in common TKG benchmarks are unseen during training, leading to severe performance degradation and representation collapse. To address this, TRANSFIR introduces a Classification–Representation–Generalization pipeline:\n1.\tCodebook Mapping via a learnable vector-quantized (VQ) codebook that clusters entities into latent semantic categories, even for unseen ones.\n2.\tInteraction Chain Encoding, which models temporal dynamics as ordered interaction sequences instead of unordered neighborhoods.\n3.\tPattern Transfer, which propagates learned temporal dynamics within semantic clusters, preventing collapse and enabling inductive generalization.\nExperiments across four standard datasets (ICEWS14, ICEWS18, ICEWS05-15, and GDELT) demonstrate significant performance improvements (average +28.6% MRR) compared to strong baselines such as LogCL, REGCN, and InGram. Ablation, sensitivity, and visualization analyses confirm the contribution of each component and show how TRANSFIR prevents embedding degeneration. The paper provides theoretical motivation, detailed methodology, and strong empirical validation.", "questions": "1.\tCausal Path Discovery Assumptions\nThe paper defines causal path discovery as the foundation of CausER’s reasoning process, but the assumptions that guarantee the validity of discovered causal paths remain implicit. Could the authors specify under what structural or temporal conditions the learned paths can be regarded as causally valid rather than correlational? Clarifying how the model ensures causal sufficiency and mechanism stability in multi-relational temporal graphs would help readers understand the theoretical boundary of the proposed intervention objective.\n2.\tIdentifiability and Theoretical Guarantees\nThe theoretical section presents an identifiable counterfactual objective but does not detail how identifiability is maintained under partially observed temporal data. Are there specific assumptions—such as temporal faithfulness or stable mechanism transitions—that must hold for the causal estimator to remain unbiased? A more explicit discussion of these conditions and their relation to the structural causal model defined in Section 3.2 would strengthen the theoretical contribution.\n3.\tCausal Path Generator Efficiency and Scalability\nThe causal path generator explores multi-hop relational paths using differentiable interventions, which can be computationally intensive on dense graphs. Could the authors provide empirical runtime and memory profiles for this module on larger datasets such as GDELT? Including a quantitative comparison with baselines in terms of cost per epoch or per sample would clarify whether the causal discovery process scales efficiently to real-world graph sizes.\n4.\tEffect and Behavior of the Counterfactual Regularizer\nThe counterfactual regularizer is presented as a key mechanism that improves robustness to temporal confounding, yet its operational behavior is described qualitatively. Could the authors further explain how this regularizer alters the score distribution during training? For instance, how does it affect the relative weighting of causal versus spurious temporal correlations over epochs? More detailed training dynamics or representative examples would make its impact on model behavior clearer.\n5.\tEvaluation Protocol and Emerging Entity Setting\nThe paper emphasizes inductive generalization to unseen entities and uses chronological splits for evaluation. Could the authors clarify whether the evaluation explicitly separates emerging entities from known ones and whether metrics are reported both for emerging and overall subsets? Such clarification would allow more precise comparison with other inductive temporal reasoning frameworks and highlight how CausER handles first-appearance nodes.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tClear Problem Definition and Motivation\nThe paper explicitly defines inductive reasoning on emerging entities — a setting rarely formalized before. The authors provide convincing empirical evidence that around one-quarter of TKG entities lack training interactions, motivating the need for inductive treatment. This establishes a meaningful gap between existing “closed-world” assumptions and real-world scenarios.\n2.\tWell-Designed Methodology\nThe Classification–Representation–Generalization pipeline is logically structured and technically coherent. Each stage (codebook clustering, interaction chain encoding, and pattern transfer) addresses a distinct aspect of the emerging-entity problem: type-level priors, temporal dynamics, and generalization.\n3.\tEmpirical Rigor and Breadth\nThe experimental setup is comprehensive: four datasets, multiple categories of baselines (graph-based, path-based, inductive), and both strict Emerging and relaxed Unknown evaluation settings. Quantitative improvements and stable results across hyperparameters demonstrate robustness.\n4.\tInsightful Analysis and Visualization\nThe inclusion of t-SNE visualizations and the quantitative Collapse Ratio metric provides clear evidence that TRANSFIR effectively mitigates representation collapse. The cluster case study concretely illustrates transferable reasoning patterns.\n5.\tClarity and Organization\nThe writing is technically clear, equations are well formatted, and the pipeline diagram helps convey the overall structure. The ablation and sensitivity analyses provide transparency regarding the influence of each module and hyperparameter.", "weaknesses": "1.\tLimited Theoretical Explanation of Codebook Semantics \nThe VQ-based codebook serves as the foundation for TRANSFIR’s semantic generalization, yet the paper offers limited theoretical or empirical analysis of what these latent clusters truly capture. Beyond a few illustrative examples, there is no quantitative assessment of the semantic coherence or stability of the learned clusters. It remains unclear whether the grouping behavior arises from shared linguistic semantics, co-occurrence frequency, or inductive biases in the embedding space. A more explicit discussion of how the codebook representation links to underlying entity semantics would strengthen the interpretability claim.\n2.\tIncomplete Scalability and Efficiency Evaluation\nAlthough Appendix D.3 presents an asymptotic complexity discussion, the main text lacks direct empirical comparisons of runtime and memory usage with strong baselines such as REGCN and LogCL. Given that TRANSFIR integrates multiple computational stages—including codebook updating, transformer-based interaction encoding, and intra-cluster pattern propagation—a detailed runtime profile and resource breakdown on large-scale datasets would be valuable for assessing its real-world feasibility and computational efficiency.\n3.\tSensitivity to Textual Initialization and Encoder Choice\nThe model initializes entity representations using fixed BERT-based textual embeddings, yet the influence of these pretrained representations is not examined. The paper does not analyze whether the model’s performance depends on the semantic quality of textual inputs, nor whether substituting alternative or domain-specific encoders would change outcomes. Since the codebook mapping step relies heavily on the textual embedding space, understanding this dependency is important for assessing generalization across domains or datasets with varying textual richness.\n4.\tLimited Exploration of Temporal Chain Configuration\nThe Interaction Chain length parameter defines the temporal window used for reasoning, but the paper provides minimal empirical or theoretical discussion on its effect. The impact of varying chain length on information propagation, noise accumulation, and temporal dependency modeling remains underexplored. A systematic analysis of how chain truncation influences accuracy and stability across datasets would clarify how TRANSFIR balances temporal coverage with computational overhead.\n5.\tAbsence of Detailed Error and Failure Case Analysis\nThe qualitative examples focus on successful transfer cases and reduced collapse, but the paper omits analysis of failure conditions. Instances where semantic clusters merge unrelated entity types or where temporal transfer fails due to inconsistent interaction histories are not discussed. Identifying and characterizing such failure modes—especially on heterogeneous datasets like GDELT—would provide important diagnostic insights and demonstrate a more complete understanding of model behavior.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hqlkGvbg8T", "reviewer": "Reviewer_2KcX", "summary": "Existing Temporal Knowledge Graph (TKG) reasoning methods primarily focus on modeling relation dynamics but typically assume a closed entity set. In the real world, new entities are continuously added to the graph but lack historical interaction data, leading to a significant drop in reasoning performance for these entities. TRANSFIR offers a systematic solution to the inductive reasoning problem for emerging entities without historical interactions. It enables transferable temporal reasoning through semantic similarity transfer and a codebook-based classification mechanism, achieving significant progress in both performance and scalability.", "questions": "1. In the ablation experiment on the GDELT dataset, is the performance without the textual encoding module better than TransFIR? This is difficult to determine from the figure. If the performance without the textual encoding module is better than TransFIR, what could explain this result?\n2. How does TRANSFIR fundamentally differ from existing similarity-based models such as MGESL[3]? Would including MGESL[3] in the experimental comparison change the relative performance ranking of TRANSFIR?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces the concept of semantic similarity transfer, providing an effective solution to prevent representation collapse.\n2. Through empirical research, the paper demonstrates the widespread presence of emerging entities in Temporal Knowledge Graphs (TKGs), with approximately 25% of entities being new. The study also shows that existing methods experience a significant performance degradation when handling these emerging entities. This provides strong theoretical and experimental support for the proposed TRANSFIR framework.", "weaknesses": "1. The evaluation could be more comprehensive. It only includes one large-model-based method, whereas other relevant approaches like ICL [1] and PPT [2] are not considered.\n2. Unclear novelty over existing similarity-based approaches. The main innovation of the proposed TRANSFIR framework lies in leveraging the behavioral evolution patterns of similar entities to assist in predicting emerging entities. However, similar approaches already exist — for example, MGESL[3] also considers the similarity between entities and analyzes the behavioral evolution patterns of semantically related entities. Moreover, MGESL discusses both settings where candidate entities are known and unknown.\n\n    [1] Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, and Jay Pujara. 2023. Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 544–557, Singapore. Association for Computational Linguistics.\n\n    [2] Wenjie Xu, Ben Liu, Miao Peng, Xu Jia, and Min Peng. 2023. Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7790–7803, Toronto, Canada. Association for Computational Linguistics.\n\n    [3] Shi Mingcong, Chunjiang Zhu, Detian Zhang, Shiting Wen, and Li Qing. 2024. Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5232–5243, Miami, Florida, USA. Association for Computational Linguistics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lId3a9vCiL", "reviewer": "Reviewer_HZS2", "summary": "This paper introduces TransFIR, a transferable inductive reasoning framework for Temporal Knowledge Graphs that enables effective reasoning about emerging entities by leveraging historical interaction patterns of semantically similar known entities via a codebook-based semantic clustering approach, achieving significant performance gains over baselines in predicting facts involving new entities.", "questions": "See Weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Intuitive experimental results clarify the motivation, making the paper easier to understand.\n\n2. A novel codebook-based approach is proposed to address emerging entities in temporal knowledge graphs.\n\n3. Experimental results comprehensively and clearly demonstrate the effectiveness of the proposed method.", "weaknesses": "1. The related work section omits some recent inductive reasoning methods for temporal knowledge graphs.\n\n2. Lines 157–158 state, “after training, emerging entities deviate sharply from known entities in the embedding space.” Since emerging entities rarely appear in the training set and are updated less frequently, this phenomenon is unsurprising.\n\n3. With BERT-encoded and frozen entity embeddings, the method likely relies on BERT’s semantic encoding to address emerging entities. Ablation results on ICEWS18 in Figure 5 support this. It is recommended to provide additional experiments to further assess the impact of LM on performance.\n\n4. The method depends on having a reliable textual description for each entity to generate initial BERT embeddings . In domains where such text is unavailable, noisy, or ambiguous, the quality of the codebook clustering could degrade significantly, weakening the entire framework.\n\n5. The complexity analysis shows a time complexity of $O(n_t L(k^2d + kd^2))$ for the IC encoder, which could become a bottleneck for graphs with very long interaction histories.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GaOZHeNgt9", "reviewer": "Reviewer_SgQU", "summary": "This paper proposes a novel framework tailored to the unseen entity link prediction in temporal knowledge graphs. The authors observed that entities sharing similar semantics often have comparable interaction histories and interaction patterns. Inspired by this, the authors propose the TransFIR framework that uses the semantically similar known entities to augment the unseen entity reasoning, where a codebook-based classifier is used to map entities to semantic clusters, and the semantics of unseen entities will be augmented by other entities within the cluster. Extensive experimental results showcasing the effectiveness of the proposed method.", "questions": "None", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1. The paper is well-written and easy to follow\n\nS2. Learning the reasoning strategy for emerging entities is a challenging and valuable direction in the field of temporal knowledge graphs.\n\nS3. Technical details of the proposed framework are well-motivated and justified.\n\nS4. Extensive experimental results are provided, offering a comprehensive understanding of the model performance.", "weaknesses": "W1. The current framework assumes static cluster assignments for entities after training. However, in reality, entity semantics often evolve over time, leading to potential shifts in their associated clusters. This inherent limitation is likely to impair the model's performance in long-term prediction scenarios, where semantic changes can become more pronounced.\n\nW2. Under the open-world assumption, emerging entities may belong to entirely new categories that exhibit no discernible similarities to existing ones. It is therefore worthwhile to examine how the framework performs in handling such entities.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)