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REVIEW 1 major objections 2 minor 43 references

Query entity structure and semantic types allow GNNs to reason from both entity and relation in knowledge graph completion.

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Q-GNN improves KGC by guiding GNN reasoning with query entity structural context and LLM-derived semantic types in addition to the query relation.

T0 review reviewed 2026-06-28 challenge →

load-bearing objection Q-GNN adds query-entity structural encoding and LLM type injection to GNN-based KGC, but the type component rests on an untested assumption. the 1 major comments →

arxiv 2606.05639 v1 pith:445M2O7S submitted 2026-06-04 cs.LG

Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion

Dongxiao He , Ruqiong Zhang , Zhizhi Yu , Ling Ding , Di Jin , Guangquan Xu , Zhiyong Feng This is my paper
classification cs.LG
keywords knowledge graph completiongraph neural networksquery entitysemantic typeslarge language modelsattention mechanismsmessage passing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Current GNN methods for knowledge graph completion center subgraphs on the query but only let the query relation guide message passing, leaving the query entity's own information unused except as an anchor point. The paper adds two channels for that entity information: a context encoder that captures local structure and relation patterns to modulate the messages, and semantic types produced by a large language model that enter the attention calculation and the final scoring function. If both channels work, the reasoning process draws guidance from the full query definition rather than the relation alone. This addresses a gap because every query is jointly defined by an entity and a relation, so entity signals should matter for accurate prediction of missing links.

Core claim

The paper claims that incorporating query entity information from structural context, encoded by a dedicated context encoder to modulate messages, and from semantic type inferred by a large language model, incorporated into attention computation and final scoring, enables the reasoning process to be guided by both the query relation and the query entity.

What carries the argument

Query-conditioned GNN that modulates messages with entity structural context and augments attention and scoring with LLM-inferred entity semantic types.

Load-bearing premise

The semantic type of the entity inferred by a large language model provides accurate and useful prior constraints that improve attention and scoring without introducing systematic errors.

What would settle it

Training and evaluating the model after replacing the LLM-inferred types with random or fixed incorrect types and checking whether accuracy on benchmark datasets falls back to the level of relation-only baselines would test the necessity of the type component.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Message passing is conditioned on both the query relation and the query entity's local structure.
  • Attention weights and scoring functions receive type-level prior constraints from the query entity.
  • The model achieves improved results on standard knowledge graph completion benchmarks.
  • Reasoning becomes sensitive to the full definition of the query rather than the relation in isolation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar conditioning on both endpoints could improve performance in other graph reasoning settings where queries are asymmetric.
  • The approach implies that external semantic priors from language models can substitute for missing type information in knowledge graphs.
  • If the type inference step proves unreliable on certain entity classes, the gains may concentrate only on well-typed subsets of the data.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 2 minor

Summary. The manuscript proposes Q-GNN, a query-conditioned GNN for knowledge graph completion. Prior GNN methods for KGC extract query-centered subgraphs but condition reasoning only on the query relation, treating the query entity merely as a structural anchor. Q-GNN adds two forms of query-entity conditioning: (1) a dedicated context encoder that captures neighboring structure and relation patterns around the entity and modulates messages, and (2) semantic entity types inferred by an LLM that are injected into the attention mechanism and final scoring function to supply type-level prior constraints. The authors claim that the combination enables reasoning guided by both the query relation and the query entity, and report improved results on standard KGC benchmarks.

Significance. If the empirical gains are robust and the LLM-derived types function as reliable priors rather than noise, the work would constitute a meaningful architectural extension of query-centered GNNs for KGC by explicitly leveraging entity-specific information. The structural-context component is a natural modulation step; the type-awareness component is the distinctive addition. Credit is due for identifying the under-use of query-entity signals in existing methods.

major comments (1)
  1. [Abstract / §3] The claim that LLM-inferred types supply useful prior constraints (Abstract; §3) is load-bearing for the central thesis that query-entity information improves reasoning. The manuscript provides no validation of type-inference quality (accuracy vs. KG schema, inter-annotator agreement, or error analysis), no description of the prompting template or LLM choice, and no ablation isolating the type component from the context encoder. Without such evidence it is impossible to determine whether reported gains arise from accurate priors or whether LLM errors are simply mitigated by other modules.
minor comments (2)
  1. [§3] Notation for the context encoder and type-injection modules should be introduced with explicit equations rather than prose descriptions alone.
  2. [§4] The experimental section would benefit from a table reporting the exact number of parameters and inference-time cost relative to the strongest baselines.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comment on validation of the LLM-inferred types below.

read point-by-point responses

  1. Referee: [Abstract / §3] The claim that LLM-inferred types supply useful prior constraints (Abstract; §3) is load-bearing for the central thesis that query-entity information improves reasoning. The manuscript provides no validation of type-inference quality (accuracy vs. KG schema, inter-annotator agreement, or error analysis), no description of the prompting template or LLM choice, and no ablation isolating the type component from the context encoder. Without such evidence it is impossible to determine whether reported gains arise from accurate priors or whether LLM errors are simply mitigated by other modules.

    Authors: We agree that the current manuscript lacks sufficient documentation and validation for the LLM-derived types, which weakens support for their role as reliable priors. In the revision we will add: (1) the specific LLM and full prompting template used for type inference; (2) quantitative validation of inference quality against KG schema types (where schema information exists) together with a qualitative error analysis; (3) an ablation that removes only the type-awareness component while retaining the structural context encoder, to isolate its contribution. Inter-annotator agreement is not applicable because the types are produced by a single LLM run rather than multiple human annotators. These additions will allow readers to assess whether the reported gains derive from accurate type priors. revision: yes

Circularity Check

0 steps flagged

No circularity: architectural description is self-contained

full rationale

The paper proposes an architectural extension to query-centered GNNs for KGC by adding a context encoder for structural neighborhood patterns around the query entity and LLM-inferred entity types for attention and scoring modulation. No equations, derivations, or self-citations are presented in the provided text that reduce any prediction, attention weight, or scoring function to a fitted parameter or self-referential definition by construction. The central claim rests on the joint guidance from relation, structural context, and type priors, validated experimentally on benchmarks rather than tautological redefinition of inputs. This is the normal case of a non-circular model architecture paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, background axioms, or newly postulated entities.

reviewed 2026-06-28 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion." pith.science (2026). https://pith.science/paper/445M2O7S

@misc{pith2026260605639,
  author       = {Pith},
  title        = {Pith review of: Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/445M2O7S}},
  note         = {Machine review of arXiv:2606.05639}
}
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read the original abstract

Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications. Recently, Graph Neural Network (GNN)-based methods have achieved remarkable success by performing message passing over query-centered local subgraphs. However, in practice, a query is jointly defined by both the entity and the relation, with both carrying information indispensable for reasoning, yet these methods rely solely on the query relation as the guiding signal, while the information inherent in the query entity is not leveraged to guide inference - the entity serves merely as a structural anchor for subgraph extraction. To this end, we incorporate query entity information into the reasoning process from two perspectives: the first is structural context, i.e., the neighboring structure and relation patterns around the entity, which is encoded by a dedicated context encoder and used to modulate messages; the second is semantic type of the entity, inferred by a large language model, which is incorporated into attention computation and final scoring to provide type-level prior constraints. Together, these two sources of information enable the reasoning process to be guided by both the query relation and the query entity. Experimental results on standard benchmarks demonstrate the effectiveness of the proposed Q-GNN.

Figures

Figures reproduced from arXiv: 2606.05639 by Di Jin, Dongxiao He, Guangquan Xu, Ling Ding, Ruqiong Zhang, Zhiyong Feng, Zhizhi Yu.

Figure 1
Figure 1. Figure 1: Local contexts of two differ￾ent entities. While these methods achieve promising results, they largely follow a common paradigm in which the query relation guides message passing, whereas the query entity itself is treated only as a structural anchor for subgraph extraction. In fact, a query is jointly defined by both the entity and the relation, and the query entity also carries information valuable for g… view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of Q-GNN. Given a query (eq, rq, ?), the Query Context Encoder aggregates information toward the query entity (blue node) via reverse message passing (tail→head). The resulting context representa￾tion heq , combined with the query relation, modulates messages during forward message passing (head→tail) in the Query-Oriented Encoder. Candidates are then scored by the Type-Specific Decoder. to as… view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

43 extracted references

  1. [1]

    Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery

    Aditya Malusare, Vineet Punyamoorty, and Vaneet Aggarwal. Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery. InIEEE Transactions on Artificial Intelligence (IEEE TAI), 2025

    2025

  2. [2]

    Knowledge enhanced representation learning for drug discovery

    Thanh Lam Hoang, Marco Luca Sbodio, Marcos Martínez Galindo, Mykhaylo Zayats, Raúl Fernández-Díaz, Victor Valls, Gabriele Picco, Cesar Berrospi, and Vanessa López. Knowledge enhanced representation learning for drug discovery. InThirty-Eighth AAAI Conference on Artificial Intelligence, pages 10544–10552, 2024

    2024

  3. [3]

    Reckg: Knowledge graph for recommender systems

    Junhyuk Kwon, Seokho Ahn, and Young-Duk Seo. Reckg: Knowledge graph for recommender systems. InProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, pages 600–607, 2024

    2024

  4. [4]

    Bridging the user-side knowledge gap in knowledge-aware recommendations with large language models

    Zheng Hu, Zhe Li, Ziyun Jiao, Satoshi Nakagawa, Jiawen Deng, Shimin Cai, Tao Zhou, and Fuji Ren. Bridging the user-side knowledge gap in knowledge-aware recommendations with large language models. InAAAI-25, pages 11799–11807, 2025

    2025

  5. [5]

    D-RAG: Differentiable retrieval-augmented generation for knowledge graph question answering

    Guangze Gao, Zixuan Li, Chunfeng Yuan, Jiawei Li, Wu Jianzhuo, Yuehao Zhang, Xiaolong Jin, Bing Li, and Weiming Hu. D-RAG: Differentiable retrieval-augmented generation for knowledge graph question answering. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 35398–35417, 2025

    2025

  6. [6]

    Reasoning with trees: Faithful question answering over knowledge graph

    Tiesunlong Shen, Jin Wang, Xuejie Zhang, and Erik Cambria. Reasoning with trees: Faithful question answering over knowledge graph. InProceedings of the 31st International Conference on Computational Linguistics, pages 3138–3157, 2025

    2025

  7. [7]

    Talukdar

    Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Talukdar. Composition-based multi-relational graph convolutional networks. In8th International Conference on Learning Representations, 2020

    2020

  8. [8]

    RAGAT: relation aware graph attention network for knowledge graph completion.IEEE Access, 9:20840–20849, 2021

    Xiyang Liu, Huobin Tan, Qinghong Chen, and Guangyan Lin. RAGAT: relation aware graph attention network for knowledge graph completion.IEEE Access, 9:20840–20849, 2021

    2021

  9. [9]

    Knowledge graph reasoning with relational digraph

    Yongqi Zhang and Quanming Yao. Knowledge graph reasoning with relational digraph. In WWW ’22: The ACM Web Conference 2022, pages 912–924, 2022

    2022

  10. [10]

    Diffusione: Reasoning on knowledge graphs via diffusion-based graph neural networks

    Zongsheng Cao, Jing Li, Zigan Wang, and Jinliang Li. Diffusione: Reasoning on knowledge graphs via diffusion-based graph neural networks. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 222–230, 2024

    2024

  11. [11]

    DRUM: end-to-end differentiable rule mining on knowledge graphs

    Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, and Daisy Zhe Wang. DRUM: end-to-end differentiable rule mining on knowledge graphs. InAdvances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, pages 15321–15331, 2019

    2019

  12. [12]

    Dynamically pruned message passing networks for large-scale knowledge graph reasoning

    Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, and Zhi-Hong Deng. Dynamically pruned message passing networks for large-scale knowledge graph reasoning. In 8th International Conference on Learning Representations, 2020

    2020

  13. [13]

    Courville

    Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron C. Courville. Film: Visual reasoning with a general conditioning layer. InProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pages 3942–3951, 2018

    2018

  14. [14]

    Anchoring path for inductive relation prediction in knowledge graphs

    Zhixiang Su, Di Wang, Chunyan Miao, and Lizhen Cui. Anchoring path for inductive relation prediction in knowledge graphs. InThirty-Eighth AAAI Conference on Artificial Intelligence, pages 9011–9018, 2024

    2024

  15. [15]

    Canonical tensor decomposition for knowledge base completion

    Timothée Lacroix, Nicolas Usunier, and Guillaume Obozinski. Canonical tensor decomposition for knowledge base completion. InProceedings of the 35th International Conference on Machine Learning, volume 80 ofProceedings of Machine Learning Research, pages 2869–2878, 2018. 10

    2018

  16. [16]

    Translating embeddings for modeling multi-relational data

    Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. InAdvances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013, pages 2787–2795, 2013

    2013

  17. [17]

    Fan Yang, Zhilin Yang, and William W. Cohen. Differentiable learning of logical rules for knowledge base reasoning. InAdvances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pages 2319–2328, 2017

    2017

  18. [18]

    Kingma and Jimmy Ba

    Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In3rd International Conference on Learning Representations, 2015

    2015

  19. [19]

    Convolutional 2d knowledge graph embeddings

    Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2d knowledge graph embeddings. InProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pages 1811–1818, 2018

    2018

  20. [20]

    Observed versus latent features for knowledge base and text inference

    Kristina Toutanova and Danqi Chen. Observed versus latent features for knowledge base and text inference. InProceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, pages 57–66, 2015

    2015

  21. [21]

    DeepPath: A reinforcement learning method for knowledge graph reasoning

    Wenhan Xiong, Thien Hoang, and William Yang Wang. DeepPath: A reinforcement learning method for knowledge graph reasoning. InProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 564–573, 2017

    2017

  22. [22]

    Statistical predicate invention

    Stanley Kok and Pedro Domingos. Statistical predicate invention. InProceedings of the 24th International Conference on Machine Learning, ICML ’07, page 433–440, 2007

    2007

  23. [23]

    Rotate: Knowledge graph em- bedding by relational rotation in complex space

    Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. Rotate: Knowledge graph em- bedding by relational rotation in complex space. In7th International Conference on Learning Representations, 2019

    2019

  24. [24]

    House: Knowledge graph embedding with householder parameterization

    Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, and Qi Zhang. House: Knowledge graph embedding with householder parameterization. InInternational Conference on Machine Learning, volume 162 ofProceedings of Machine Learning Research, pages 13209–13224, 2022

    2022

  25. [25]

    Xiou Ge, Yun-Cheng Wang, Bin Wang, and C.-C. Jay Kuo. Compounding geometric operations for knowledge graph completion. InProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6947–6965, 2023

    2023

  26. [26]

    Multisource hierarchical neural network for knowledge graph embedding.Expert Syst

    Dan Jiang, Ronggui Wang, Lixia Xue, and Juan Yang. Multisource hierarchical neural network for knowledge graph embedding.Expert Syst. Appl., 237(Part B):121446, 2024

    2024

  27. [27]

    Meng Qu, Jun-Kun Chen, Louis-Pascal A. C. Xhonneux, Yoshua Bengio, and Jian Tang. Rnnlogic: Learning logic rules for reasoning on knowledge graphs. In9th International Conference on Learning Representations, 2021

    2021

  28. [28]

    A unified joint approach with topological context learning and rule augmentation for knowledge graph com- pletion

    Jingtao Guo, Chunxia Zhang, Lingxi Li, Xiaojun Xue, and Zhendong Niu. A unified joint approach with topological context learning and rule augmentation for knowledge graph com- pletion. InFindings of the Association for Computational Linguistics, pages 13686–13696, 2024

    2024

  29. [29]

    Mixed geometry message and trainable convolutional attention network for knowledge graph completion

    Bin Shang, Yinliang Zhao, Jun Liu, and Di Wang. Mixed geometry message and trainable convolutional attention network for knowledge graph completion. InThirty-Eighth AAAI Conference on Artificial Intelligence, pages 8966–8974, 2024

    2024

  30. [30]

    Multi-relational semantic awareness for knowledge graph completion

    Jiawei Song, Zongtao Duan, Jianrong Cao, and Yun Lin. Multi-relational semantic awareness for knowledge graph completion. In2024 9th International Conference on Computer and Communication Systems (ICCCS), pages 107–113, 2024

    2024

  31. [31]

    Higher-order logical knowl- edge representation learning

    Suixue Wang, Weiliang Huo, Shilin Zhang, and Qingchen Zhang. Higher-order logical knowl- edge representation learning. InProceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, pages 3398–3406, 2025. 11

    2025

  32. [32]

    Teru, Etienne G

    Komal K. Teru, Etienne G. Denis, and William L. Hamilton. Inductive relation prediction by subgraph reasoning. InProceedings of the 37th International Conference on Machine Learning, volume 119 ofProceedings of Machine Learning Research, pages 9448–9457, 2020

    2020

  33. [33]

    Communicative message passing for inductive relation reasoning

    Sijie Mai, Shuangjia Zheng, Yuedong Yang, and Haifeng Hu 0001. Communicative message passing for inductive relation reasoning. InThirty-Fifth AAAI Conference on Artificial Intelli- gence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelli...

    2021

  34. [34]

    Neural bellman-ford networks: A general graph neural network framework for link prediction

    Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang. Neural bellman-ford networks: A general graph neural network framework for link prediction. InAdvances in Neural Information Processing Systems, pages 29476–29490, 2021

    2021

  35. [35]

    Embedding entities and relations for learning and inference in knowledge bases

    Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. In3rd International Conference on Learning Representations, 2015

    2015

  36. [36]

    Hospedales

    Ivana Balazevic, Carl Allen, and Timothy M. Hospedales. Tucker: Tensor factorization for knowledge graph completion. InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 5184–5193, 2019

    2019

  37. [37]

    Complex embeddings for simple link prediction

    Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. Complex embeddings for simple link prediction. InProceedings of the 33nd International Conference on Machine Learning, volume 48 ofJMLR Workshop and Conference Proceedings, pages 2071–2080, 2016

    2071

  38. [38]

    Deeppath: A reinforcement learning method for knowledge graph reasoning

    Wenhan Xiong, Thien Hoang, and William Yang Wang. Deeppath: A reinforcement learning method for knowledge graph reasoning. InProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 564–573, 2017

    2017

  39. [39]

    Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning

    Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning. In6th International Conference on Learning Representations, 2018

    2018

  40. [40]

    Learning to walk with dual agents for knowledge graph reasoning

    Denghui Zhang, Zixuan Yuan, Hao Liu, Xiaodong Lin, and Hui Xiong. Learning to walk with dual agents for knowledge graph reasoning. InThirty-Sixth AAAI Conference on Artificial Intelligence, pages 5932–5941, 2022

    2022

  41. [41]

    Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling

    Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. Modeling relational data with graph convolutional networks. InThe Semantic Web - 15th International Conference, volume 10843 ofLecture Notes in Computer Science, pages 593–607, 2018

    2018

  42. [42]

    Pairnorm: Tackling oversmoothing in gnns

    Lingxiao Zhao and Leman Akoglu. Pairnorm: Tackling oversmoothing in gnns. In8th International Conference on Learning Representations, 2020

    2020

  43. [43]

    type": "<TYPE_NAME>

    Qimai Li, Zhichao Han, and Xiao-Ming Wu. Deeper insights into graph convolutional networks for semi-supervised learning. InProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pages 3538–3545, 2018. 12 A Entity Type Annotation We perform entity type annotation by calling DeepSeek-V3 through the API. Specifically, we customize the s...

    2018

This paper was first reviewed by grok-4.3 on June 28, 2026.