OpenRaiser/Intern-Atlas huggingface dataset:
Dage: Dag query answering via relational combinator with logical constraints
Authors: Yunjie He, Bo Xiong, Daniel Hernández, Yuqicheng Zhu, Evgeny Kharlamov, Steffen Staab
Venue: Conference WWW 2025
Year: 2025

Predicting answers to queries over knowledge graphs is called a complex reasoning task because answering a query requires subdividing it into subqueries. Existing query embedding methods use this decomposition to compute the embedding of a query as the combination of the embedding of the subqueries. This requirement limits the answerable queries to queries having a single free variable and being decomposable, which are called tree-form queries and correspond to the $\mathcal{SROI}^-$ description logic. In this paper, we define a more general set of queries, called DAG queries and formulated in the $\mathcal{ALCOIR}$ description logic, propose a query embedding method for them, called DAGE, and a new benchmark to evaluate query embeddings on them. Given the computational graph of a DAG query, DAGE combines the possibly multiple paths between two nodes into a single path with a trainable operator that represents the intersection of relations and learns DAG-DL from tautologies. We show that it is possible to implement DAGE on top of existing query embedding methods, and we empirically measure the improvement of our method over the results of vanilla methods evaluated in tree-form queries that approximate the DAG queries of our proposed benchmark.
From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing
Authors: Hamed Zamani, Mostafa Dehghani, W. Bruce Croft, Erik G. Learned-Miller, Jaap Kamps
Venue: Conference CIKM 2018
Year: 2018

The availability of massive data and computing power allowing for effective data driven neural approaches is having a major impact on machine learning and information retrieval research, but these models have a basic problem with efficiency. Current neural ranking models are implemented as multistage rankers: for efficiency reasons, the neural model only re-ranks the top ranked documents retrieved by a first-stage efficient ranker in response to a given query. Neural ranking models learn dense representations causing essentially every query term to match every document term, making it highly inefficient or intractable to rank the whole collection. The reliance on a first stage ranker creates a dual problem: First, the interaction and combination effects are not well understood. Second, the first stage ranker serves as a "gate-keeper" or filter, effectively blocking the potential of neural models to uncover new relevant documents. In this work, we propose a standalone neural ranking model (SNRM) by introducing a sparsity property to learn a latent sparse representation for each query and document. This representation captures the semantic relationship between the query and documents, but is also sparse enough to enable constructing an inverted index for the whole collection. We parameterize the sparsity of the model to yield a retrieval model as efficient as conventional term based models. Our model gains in efficiency without loss of effectiveness: it not only outperforms the existing term matching baselines, but also performs similarly to the recent re-ranking based neural models with dense representations. Our model can also take advantage of pseudo-relevance feedback for further improvements. More generally, our results demonstrate the importance of sparsity in neural IR models and show that dense representations can be pruned effectively, giving new insights about essential semantic features and their distributions.
Can we soft prompt LLMs for graph learning tasks?
Authors: Zheyuan Liu, Xiaoxin He, Yijun Tian, Nitesh V. Chawla
Venue: Conference WWW 2024
Year: 2024

Graph plays an important role in representing complex relationships in real-world applications such as social networks, biological data and citation networks. In recent years, Large Language Models (LLMs) have achieved tremendous success in various domains, which makes applying LLMs to graphs particularly appealing. However, directly applying LLMs to graph modalities presents unique challenges due to the discrepancy and mismatch between the graph and text modalities. Hence, to further investigate LLMs' potential for comprehending graph information, we introduce GraphPrompter, a novel framework designed to align graph information with LLMs via soft prompts. Specifically, GraphPrompter consists of two main components: a graph neural network to encode complex graph information and an LLM that effectively processes textual information. Comprehensive experiments on various benchmark datasets under node classification and link prediction tasks demonstrate the effectiveness of our proposed method. The GraphPrompter framework unveils the substantial capabilities of LLMs as predictors in graph-related tasks, enabling researchers to utilize LLMs across a spectrum of real-world graph scenarios more effectively.
Training mlps on graphs without supervision
Authors: Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye
Venue: Conference WSDM 2025
Year: 2025

Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive applications, such as real-time financial fraud detection. To address this limitation, recent studies have proposed distilling knowledge from teacher GNNs into student Multi-Layer Perceptrons (MLPs) trained on node content, aiming to accelerate inference. However, these approaches often inadequately explore structural information when inferring unseen nodes. To this end, we introduce SimMLP, a Self-supervised framework for learning MLPs on graphs, designed to fully integrate rich structural information into MLPs. Notably, SimMLP is the first MLP-learning method that can achieve equivalence to GNNs in the optimal case. The key idea is to employ self-supervised learning to align the representations encoded by graph context-aware GNNs and neighborhood dependency-free MLPs, thereby fully integrating the structural information into MLPs. We provide a comprehensive theoretical analysis, demonstrating the equivalence between SimMLP and GNNs based on mutual information and inductive bias, highlighting SimMLP's advanced structural learning capabilities. Additionally, we conduct extensive experiments on 20 benchmark datasets, covering node classification, link prediction, and graph classification, to showcase SimMLP's superiority over state-of-the-art baselines, particularly in scenarios involving unseen nodes (e.g., inductive and cold-start node classification) where structural insights are crucial. Our codes are available at: https://github.com/Zehong-Wang/SimMLP.
Citygpt: Empowering urban spatial cognition of large language models
Authors: Jie Feng, Tianhui Liu, Yuwei Du, Siqi Guo, Yuming Lin, Yong Li
Venue: Conference KDD 2025
Year: 2025

Large language models(LLMs), with their powerful language generation and reasoning capabilities, have already achieved notable success in many domains, e.g., math and code generation. However, they often fall short when tackling real-life geospatial tasks within urban environments. This limitation stems from a lack of physical world knowledge and relevant data during training. To address this gap, we propose CityGPT, a systematic framework designed to enhance LLMs' understanding of urban space and improve their ability to solve the related urban tasks by integrating a city-scale 'world model' into the model. Firstly, we construct a diverse instruction tuning dataset, CityInstruction, for injecting urban knowledge into LLMs and effectively boosting their spatial reasoning capabilities. Using a combination of CityInstruction and open source general instruction data, we introduce a novel and easy-to-use self-weighted fine-tuning method (SWFT) to train various LLMs (including ChatGLM3-6B, Llama3-8B, and Qwen2.5-7B) to enhance their urban spatial capabilities without compromising, or even improving, their general abilities. Finally, to validate the effectiveness of our proposed framework, we develop a comprehensive text-based spatial benchmark CityEval for evaluating the performance of LLMs across a wide range of urban scenarios and geospatial tasks. Extensive evaluation results demonstrate that smaller LLMs trained with CityInstruction by SWFT method can achieve performance that is competitive with, and in some cases superior to, proprietary LLMs when assessed using CityEval. Our work highlights the potential for integrating spatial knowledge into LLMs, thereby expanding their spatial cognition abilities and applicability to the real-world physical environments. The dataset, benchmark, and source code are open-sourced and can be accessed through https://github.com/tsinghua-fib-lab/CityGPT.
Large language models for next point-of-interest recommendation
Authors: Peibo Li, Maarten de Rijke, Hao Xue, Shuang Ao, Yang Song, Flora D. Salim
Venue: Conference SIGIR 2024
Year: 2024

The next Point of Interest (POI) recommendation task is to predict users' immediate next POI visit given their historical data. Location-Based Social Network (LBSN) data, which is often used for the next POI recommendation task, comes with challenges. One frequently disregarded challenge is how to effectively use the abundant contextual information present in LBSN data. Previous methods are limited by their numerical nature and fail to address this challenge. In this paper, we propose a framework that uses pretrained Large Language Models (LLMs) to tackle this challenge. Our framework allows us to preserve heterogeneous LBSN data in its original format, hence avoiding the loss of contextual information. Furthermore, our framework is capable of comprehending the inherent meaning of contextual information due to the inclusion of commonsense knowledge. In experiments, we test our framework on three real-world LBSN datasets. Our results show that the proposed framework outperforms the state-of-the-art models in all three datasets. Our analysis demonstrates the effectiveness of the proposed framework in using contextual information as well as alleviating the commonly encountered cold-start and short trajectory problems.
Non-homophilic graph pre-training and prompt learning
Authors: Xingtong Yu, Jie Zhang, Yuan Fang, Renhe Jiang
Venue: Conference KDD 2025
Year: 2025

Graphs are ubiquitous for modeling complex relationships between objects across various fields. Graph neural networks (GNNs) have become a mainstream technique for graph-based applications, but their performance heavily relies on abundant labeled data. To reduce labeling requirement, pre-training and prompt learning has become a popular alternative. However, most existing prompt methods do not differentiate homophilic and heterophilic characteristics of real-world graphs. In particular, many real-world graphs are non-homophilic, not strictly or uniformly homophilic with mixing homophilic and heterophilic patterns, exhibiting varying non-homophilic characteristics across graphs and nodes. In this paper, we propose ProNoG, a novel pre-training and prompt learning framework for such non-homophilic graphs. First, we analyze existing graph pre-training methods, providing theoretical insights into the choice of pre-training tasks. Second, recognizing that each node exhibits unique non-homophilic characteristics, we propose a conditional network to characterize the node-specific patterns in downstream tasks. Finally, we thoroughly evaluate and analyze ProNoG through extensive experiments on ten public datasets.
Extractive snippet generation for arguments
Authors: Milad Alshomary, Nick Düsterhus, Henning Wachsmuth
Venue: Conference SIGIR 2020
Year: 2020

Snippets are used in web search to help users assess the relevance of retrieved results to their query. Recently, specialized search engines have arisen that retrieve pro and con arguments on controversial issues. We argue that standard snippet generation is insufficient to represent the core reasoning of an argument. In this paper, we introduce the task of generating a snippet that represents the main claim and reason of an argument. We propose a query-independent extractive summarization approach to this task that uses a variant of PageRank to assess the importance of sentences based on their context and argumentativeness. In both automatic and manual evaluation, our approach outperforms strong baselines.
Multi-modal supplementary-complementary summarization using multi-objective optimization
Authors: Anubhav Jangra, Sriparna Saha 0001, Adam Jatowt, Mohammed Hasanuzzaman
Venue: Conference SIGIR 2021
Year: 2021

Large amounts of multi-modal information online make it difficult for users to obtain proper insights. In this paper, we introduce and formally define the concepts of supplementary and complementary multi-modal summaries in the context of the overlap of information covered by different modalities in the summary output. A new problem statement of combined complementary and supplementary multi-modal summarization (CCS-MMS) is formulated. The problem is then solved in several steps by utilizing the concepts of multi-objective optimization by devising a novel unsupervised framework. An existing multi-modal summarization data set is further extended by adding outputs in different modalities to establish the efficacy of the proposed technique. The results obtained by the proposed approach are compared with several strong baselines; ablation experiments are also conducted to empirically justify the proposed techniques. Furthermore, the proposed model is evaluated separately for different modalities quantitatively and qualitatively, demonstrating the superiority of our approach.
Uncommonsense: Informative negative knowledge about everyday concepts
Authors: Hiba Arnaout, Simon Razniewski, Gerhard Weikum, Jeff Z. Pan
Venue: Conference CIKM 2022
Year: 2022

Commonsense knowledge about everyday concepts is an important asset for AI applications, such as question answering and chatbots. Recently, we have seen an increasing interest in the construction of structured commonsense knowledge bases (CSKBs). An important part of human commonsense is about properties that do not apply to concepts, yet existing CSKBs only store positive statements. Moreover, since CSKBs operate under the open-world assumption, absent statements are considered to have unknown truth rather than being invalid. This paper presents the UNCOMMONSENSE framework for materializing informative negative commonsense statements. Given a target concept, comparable concepts are identified in the CSKB, for which a local closed-world assumption is postulated. This way, positive statements about comparable concepts that are absent for the target concept become seeds for negative statement candidates. The large set of candidates is then scrutinized, pruned and ranked by informativeness. Intrinsic and extrinsic evaluations show that our method significantly outperforms the state-of-the-art. A large dataset of informative negations is released as a resource for future research.
Filling in scenes by propagating probabilities through layers and into appearance models
Authors: Brendan J. Frey
Venue: Conference CVPR 2000
Year: 2000

Inferring the identities and positions of multiple occluding objects in a noisy image is a difficult problem, even when the shapes and appearances of the allowable objects are known. Methods that detect and analyze shape features, occlusion boundaries and optical flow break down when the image is noisy. In situations where we know the boundaries and appearances of the allowable objects, a brute force method can be used to perform MAP inference. If there are K possible objects (including translations, etc.) in up to L layers, the number of possible configurations of the scene is K/sup L/, so exact inference is intractable for large numbers of objects and reasonably large numbers of layers. We construct a Bayesian network that describes the occlusion process and we use iterative probability propagation to approximately recover the identities and positions of the objects in the scene in time that is linear in K and L. Although iterative probability propagation is an approximate inference technique, it was recently used to obtain the world record in error-correcting decoding. Experiments show that when one explanation of the scene is most probable, the algorithm finds the solution. For a small problem, we show that as the number of iterations increases, iterative probability propagation performs better than a greedy technique and becomes closer to the exact MAP algorithm. Quite surprisingly, we also find that when the order of occlusion is ambiguous, the output of the algorithm may oscillate between plausible interpretations of the scene.
On representation and matching of multi-coloured objects
Authors: Jiri Matas, Radek Marik, Josef Kittler
Venue: Conference ICCV 1995
Year: 1995

A new representation for objects with multiple colours-the colour adjacency graph (CAG)-is proposed. Each node of the CAG represents a single chromatic component of the image defined as a set of pixels forming a unimodal cluster in the chromatic scattergram. Edges encode information about adjacency of colour components and their reflectance ratio. The CAG is related to both the histogram and region adjacency graph representations. It is shown to be preserving and combining the best features of these two approaches while avoiding their drawbacks. The proposed approach is tested on a range of difficult object recognition and localisation problems involving complex imagery of non-rigid 3D objects under varied viewing conditions with excellent results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Learning to cluster using local neighborhood structure
Authors: Rómer Rosales, Kannan Achan, Brendan J. Frey
Venue: Conference ICML 2004
Year: 2004

This paper introduces an approach for clustering/classification which is based on the use of local, high-order structure present in the data. For some problems, this local structure might be more relevant for classification than other measures of point similarity used by popular unsupervised and semi-supervised clustering methods. Under this approach, changes in the class label are associated to changes in the local properties of the data. Using this idea, we also pursue to learn how to cluster given examples of clustered data (including from different datasets). We make these concepts formal by presenting a probability model that captures their fundamentals and show that in this setting, learning to cluster is a well defined and tractable task. Based on probabilistic inference methods, we then present an algorithm for computing the posterior probability distribution of class labels for each data point. Experiments in the domain of spatial grouping and functional gene classification are used to illustrate and test these concepts.
Fast particle smoothing: If I had a million particles
Authors: Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang
Venue: Conference ICML 2006
Year: 2006

We propose efficient particle smoothing methods for generalized state-spaces models. Particle smoothing is an expensive O(N2) algorithm, where N is the number of particles. We overcome this problem by integrating dual tree recursions and fast multipole techniques with forward-backward smoothers, a new generalized two-filter smoother and a maximum a posteriori (MAP) smoother. Our experiments show that these improvements can substantially increase the practicality of particle smoothing.
Statistical entity-topic models
Authors: David Newman, Chaitanya Chemudugunta, Padhraic Smyth
Venue: Conference KDD 2006
Year: 2006

The primary purpose of news articles is to convey information about who, what, when and where. But learning and summarizing these relationships for collections of thousands to millions of articles is difficult. While statistical topic models have been highly successful at topically summarizing huge collections of text documents, they do not explicitly address the textual interactions between who/where, i.e. named entities (persons, organizations, locations) and what, i.e. the topics. We present new graphical models that directly learn the relationship between topics discussed in news articles and entities mentioned in each article. We show how these entity-topic models, through a better understanding of the entity-topic relationships, are better at making predictions about entities.
Modeling relational events via latent classes
Authors: Christopher DuBois, Padhraic Smyth
Venue: Conference KDD 2010
Year: 2010

Many social networks can be characterized by a sequence of dyadic interactions between individuals. Techniques for analyzing such events are of increasing interest. In this paper, we describe a generative model for dyadic events, where each event arises from one of C latent classes, and the properties of the event (sender, recipient, and type) are chosen from distributions over these entities conditioned on the chosen class. We present two algorithms for inference in this model: an expectation-maximization algorithm as well as a Markov chain Monte Carlo procedure based on collapsed Gibbs sampling. To analyze the model's predictive accuracy, the algorithms are applied to multiple real-world data sets involving email communication, international political events, and animal behavior data.
Suggesting friends using the implicit social graph
Authors: Maayan Roth, Assaf Ben-David, David Deutscher, Guy Flysher, Ilan Horn, Ari Leichtberg, Naty Leiser, Yossi Matias, Ron Merom
Venue: Conference KDD 2010
Year: 2010

Although users of online communication tools rarely categorize their contacts into groups such as "family", "co-workers", or "jogging buddies", they nonetheless implicitly cluster contacts, by virtue of their interactions with them, forming implicit groups. In this paper, we describe the implicit social graph which is formed by users' interactions with contacts and groups of contacts, and which is distinct from explicit social graphs in which users explicitly add other individuals as their "friends". We introduce an interaction-based metric for estimating a user's affinity to his contacts and groups. We then describe a novel friend suggestion algorithm that uses a user's implicit social graph to generate a friend group, given a small seed set of contacts which the user has already labeled as friends. We show experimental results that demonstrate the importance of both implicit group relationships and interaction-based affinity ranking in suggesting friends. Finally, we discuss two applications of the Friend Suggest algorithm that have been released as Gmail Labs features.
Unifying the error-correcting and output-code adaboost within the margin framework
Authors: Yijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu 0001
Venue: Conference ICML 2005
Year: 2005

In this paper, we present a new interpretation of AdaBoost.ECC and AdaBoost.OC. We show that AdaBoost.ECC performs stage-wise functional gradient descent on a cost function, defined in the domain of margin values, and that AdaBoost.OC is a shrinkage version of AdaBoost.ECC. These findings strictly explain some properties of the two algorithms. The gradient-minimization formulation of AdaBoost.ECC allows us to derive a new algorithm, referred to as AdaBoost.SECC, by explicitly exploiting shrinkage as regularization in AdaBoost.ECC. Experiments on diverse databases confirm our theoretical findings. Empirical results show that AdaBoost.SECC performs significantly better than AdaBoost.ECC and AdaBoost.OC.