Digital healthcare systems have enabled the collection of mass healthcare data in electronic healthcare records (EHRs), allowing artificial intelligence solutions for various healthcare prediction tasks. However, existing studies often focus on isolated components of EHR data, limiting their predictive performance and interpretability. To address this gap, we propose ProtoEHR, an interpretable hierarchical prototype learning framework that fully exploits the rich, multi-level structure of EHR data to enhance healthcare predictions. More specifically, ProtoEHR models relationships within and across three hierarchical levels of EHRs: medical codes, hospital visits, and patients. We first leverage large language models to extract semantic relationships among medical codes and construct a medical knowledge graph as the knowledge source. Building on this, we design a hierarchical representation learning framework that captures contextualized representations across three levels, while incorporating prototype information within each level to capture intrinsic similarities and improve generalization. To perform a comprehensive assessment, we evaluate ProtoEHR in two public datasets on five clinically significant tasks, including prediction of mortality, prediction of readmission, prediction of length of stay, drug recommendation, and prediction of phenotype. The results demonstrate the ability of ProtoEHR to make accurate, robust, and interpretable predictions compared to baselines in the literature. Furthermore, ProtoEHR offers interpretable insights on code, visit, and patient levels to aid in healthcare prediction.

Survival analysis, which estimates the probability of event occurrence over time from censored data, is fundamental in numerous real-world applications, particularly in high-stakes domains such as healthcare and risk assessment. Despite advances in numerous survival models, quantifying the uncertainty of predictions from these models remains underexplored and challenging. The lack of reliable uncertainty quantification limits the interpretability and trustworthiness of survival models, hindering their adoption in clinical decision-making and other sensitive applications. To bridge this gap, in this work, we introduce SurvUnc, a novel meta-model based framework for post-hoc uncertainty quantification for survival models. SurvUnc introduces an anchor-based learning strategy that integrates concordance knowledge into meta-model optimization, leveraging pairwise ranking performance to estimate uncertainty effectively. Notably, our framework is model-agnostic, ensuring compatibility with any survival model without requiring modifications to its architecture or access to its internal parameters. Especially, we design a comprehensive evaluation pipeline tailored to this critical yet overlooked problem. Through extensive experiments on four publicly available benchmarking datasets and five representative survival models, we demonstrate the superiority of SurvUnc across multiple evaluation scenarios, including selective prediction, misprediction detection, and out-of-domain detection. Our results highlight the effectiveness of SurvUnc in enhancing model interpretability and reliability, paving the way for more trustworthy survival predictions in real-world

The binary relational knowledge base (KB, a.k.a. knowledge graph), representing real-world knowledge with binary relations and entities, has been an important research topic in artificial intelligence, while, considerable knowledge also involves beyond-binary relations. Recently, the area proposes to model n-ary relational KBs with both binary and beyond-binary relations included. However, most current models are extended from translational distance and neural network models in binary relational KBs, which suffer from weak expressiveness and high complexity, respectively. To overcome such issues, in this work, we propose a novel two-step modeling framework, GETD, generalizing the powerful tensor decomposition technique from binary relational KBs to the n-ary case. For n-ary relational KBs with single-arity relations, the GETD framework introduces Tucker decomposition and Tensor Ring decomposition for expressive and efficient modeling. Furthermore, the framework is technically extended for the representation of n-ary relational KBs with mixed-arity relations. The existing negative sampling technique is also generalized to the n-ary case for GETD. In addition, we theoretically prove that the GETD framework is fully expressive to completely represent any KBs. Empirical results on two representative datasets show that the proposed framework significantly outperforms the state-of-the-art methods, achieving 11–26% and 4–7% improvements on Hits@10 for the single-arity and the mixed-arity cases, respectively.

As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business. However, there is no publicly available dataset so far and most existing approaches are limited to an extremely small scope of brands, which seriously hinders the research on site recommendation. Therefore, we collect, construct and release an open comprehensive dataset, namely OpenSiteRec, to facilitate and promote the research on site recommendation. Specifically, OpenSiteRec leverages a heterogeneous graph schema to represent various types of real-world entities and relations in four international metropolises. To evaluate the performance of the existing general methods on the site recommendation task, we conduct benchmarking experiments of several representative recommendation models on OpenSiteRec. Furthermore, we also highlight the potential application directions to demonstrate the wide applicability of OpenSiteRec. We believe that our OpenSiteRec dataset is significant and anticipated to encourage the development of advanced methods for site recommendation. OpenSiteRec is available online at https://OpenSiteRec.github.io/.

Understanding and accurately predicting cellular traffic data is vital for communication operators and device users, as it facilitates efficient resource allocation and ensures superior service quality. However, large-scale cellular traffic data forecasting remains challenging due to intricate temporal variations and complex spatial relationships. This article proposes a Knowledge Graph Driven Decomposition Approach (KGDA) for precise cellular traffic prediction. The KGDA breaks down the impact of static environmental factors and dynamic autocorrelations of cellular traffic time series, enabling the capture of overall traffic changes and understanding of traffic dependence on past values. Specifically, we propose an urban knowledge graph to capture the static environmental context of base stations, mapping these entities into the same latent space while retaining static environmental knowledge. The cellular traffic is divided into a regular pattern and fluctuating residual components, with the KGDA comprising four modules: a Knowledge Graph Representation Learning model, a traffic regular pattern prediction module, a traffic residual dynamic prediction module, and an attentional fusion module. The first leverages graph neural networks to extract spatial contexts and predict regular patterns, the second utilizes the Bi-directional Long Short-Term Memory (Bi-LSTM) model to capture autocorrelations of traffic time series, and the final module integrates the patterns and residuals to produce the final prediction result. Comprehensive experiments demonstrate that our proposed model outperforms state-of-the-art models by more than 10% in forecasting cellular traffic.

Nowadays, the explosive growth of personalized web applications and the rapid development of artificial intelligence technology have flourished the recent research on mobile user profiling, i.e., inferring the user profile from mobile behavioral data. Particularly, existing studies mainly follow the data-driven paradigm to develop feature engineering and representation learning on such data, which however suffer from the robustness issue, i.e., generalizing poorly across datasets and profiles without considering semantic knowledge therein. In comparison, the rising knowledge-driven paradigm built upon the knowledge graph (KG) offers a potential solution to mitigate such weakness. Therefore, in this article, we propose a Knowledge Graph aided framework for Mobile User Profiling (KG-MUP). Specifically, to distil semantic knowledge among data, we firstly construct an urban knowledge graph (UrbanKG) with domain entities like users, regions, point of interests (POIs), and so on. identified, as well as semantic relations for home, workplace, spatiality, and so on. extracted. Moreover, we leverage tensor decomposition and graph neural network to obtain knowledgeable user representations from UrbanKG. In addition, we introduce several customized features to quantify individual mobility characteristics for mobile user profiling. Extensive experiments on three real-world mobility datasets demonstrate that KG-MUP achieves state-of-the-art performance on user profile inference tasks. Moreover, further results also reveal the importance of various semantic knowledge to user profile inference, which provides meaningful insights on user modeling with mobile behavioral data.

With the rapid development of location acquisition technologies, massive mobile trajectories have been collected and made available to us, which support a fantastic way of understanding and modeling individuals’ mobility. However, existing data-driven methods either fail to capture the long-range dependency or suffer from a high computational cost. To overcome these issues, we propose a knowledge-driven framework for mobility prediction, which leverages knowledge graphs (KG) to formulate the mobility prediction task into the KG completion problem through integrating the structured “knowledge” from the mobility data. However, most related mobility prediction works only focus on the structured information encoded in existing triples, which ignores the rich semantic information of relation paths composed of multiple relation triples. In this article, we apply a dedicated module to extract the supplementary semantic structure of paths in KG, which contributes to the interpretability and accuracy of our model. Specifically, the extracted rules are applied to capture the dependencies between relational facts. Moreover, by incorporating user information in the entity-relation space with the corresponding hyperplane, our method could capture diverse user mobility patterns and model the personal characteristics of users to improve the accuracy of mobility prediction. Extensive evaluations illustrate that our proposed model beats state-of-the-art mobility prediction algorithms, which verifies the superiority of utilizing logical rules and user hyperplanes. Our implementation code is available at https://github.com/tsinghua-fib-lab/RulekG-MobiPre.git

Accurately predicting base station traffic volumes and understanding mobile traffic patterns is essential for smart city development, enabling efficient resource allocation and ensuring high-quality communication services. However, existing works have limitations in capturing spatial information, though the surrounding environment plays a critical role in mobile traffic prediction. In this paper, we utilize a spatial knowledge graph to represent spatial information and add important urban components to augment it making it a more effective tool for capturing environmental information. we further propose a multi-relational knowledge graph convolutional network model for mobile traffic prediction, which consists of three parts. The environmental context modelling captures spatial information from the augmented spatial knowledge graph using tucker decomposition and relational graph convolutional network. The semantic relationship modelling extracts semantic relationships between base stations and employs transformer and causal convolution to capture temporal features. The inter-attentional fusion modelling utilizes the self-attention mechanism to further capture base station relationships and predict future traffic volumes. Extensive experiments demonstrate that our proposed model significantly outperforms the state-of-the-art models by over 10% in mobile traffic prediction. The code is available at https://github.com/tsinghua-fiblab/Mobile-Traffic-Prediction-sigspatial23

Built environment supports all the daily activities and shapes our health. Leveraging informative street view imagery, previous research has established the profound correlation between the built environment and chronic, non-communicable diseases; however, predicting the exposure risk of infectious diseases remains largely unexplored. The person-to-person contacts and interactions contribute to the complexity of infectious disease, which is inherently different from non-communicable diseases. Besides, the complex relationships between street view imagery and epidemic exposure also hinder accurate predictions. To address these problems, we construct a regional mobility graph informed by the gravity model, based on which we propose a transmission-aware graph neural network (GNN) to capture disease transmission patterns arising from human mobility. Experiments show that the proposed model significantly outperforms baseline models by 8.54% in weighted F1, shedding light on a low-cost, scalable approach to assess epidemic exposure risks from street view imagery.

Site selection determines optimal locations for new stores, which is of crucial importance for business success and urban development. Especially, the wide application of artificial intelligence with multi-source urban data makes intelligent site selection promising. Nevertheless, existing data-driven approaches heavily rely on feature engineering, which cannot take the complex relationships as well as the diverse influences of various semantics among data into consideration. Further, most approaches fail to reveal underlying factors for site decisions. To get rid of the dilemma, in this work, leveraging the knowledge graph (KG) technique, we propose a knowledge-driven model for site selection, short for KnowSite. Specifically, by empowering rich semantics in KG, we firstly construct an urban KG (UrbanKG) for site selection knowledge discovery with cities' key elements and complex relationships captured. Based on UrbanKG, we apply pre-training for semantic representations, and then design a generalized encoder-decoder structure for site decisions. KnowSite designs a graph neural network based encoder to adaptively model diverse influences, and further builds a relation path based decoder revealing the reasons behind site decisions. Extensive experiments on two datasets demonstrate that KnowSite outperforms representative baselines by more than 9% on precision. Moreover, KnowSite provides intuitive and convincing explanations for site decisions and sheds light on the site selection understanding.

Although generative AI has been successful in many areas, its ability to model geospatial data is still underexplored. Urban flow, a typical kind of geospatial data, is critical for a wide range of applications from public safety and traffic management to urban planning. Existing studies mostly focus on predictive modeling of urban flow that predicts the future flow based on historical flow data, which may be unavailable in data-sparse areas or newly planned regions. Some other studies aim to predict OD flow among regions but they fail to model dynamic changes of urban flow over time. In this work, we study a new problem of urban flow generation that generates dynamic urban flow for regions without historical flow data. To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions. We first construct an urban knowledge graph (UKG) to model the urban environment and relationships between regions, based on which we design a knowledge-enhanced spatio-temporal diffusion model (KSTDiff) to generate urban flow for each region. Specifically, to accurately generate urban flow for regions with different flow volumes, we design a novel diffusion process guided by a volume estimator, which is learnable and customized for each region. Moreover, we propose a knowledge-enhanced denoising network to capture the spatio-temporal dependencies of urban flow as well as the impact of urban environment in the denoising process. Extensive experiments on four real-world datasets validate the superiority of our model over state-of-the-art baselines in urban flow generation. Further in-depth studies demonstrate the utility of generated urban flow data and the ability of our model for long-term flow generation and urban flow prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KSTDiff-Urban-flow-generation.

Monitoring sustainable development goals requires accurate and timely socioeconomic statistics, while ubiquitous and frequently-updated urban imagery in web like satellite/street view images has emerged as an important source for socioeconomic prediction. Especially, recent studies turn to self-supervised contrastive learning with manually designed similarity metrics for urban imagery representation learning and further socioeconomic prediction, which however suffers from effectiveness and robustness issues. To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction. Specifically, we firstly introduce knowledge graph (KG) to effectively model the urban knowledge in spatiality, mobility, etc., and then build neural network based encoders to learn representations of an urban image in associated semantic and visual spaces, respectively. Finally, we design a cross-modality based contrastive learning framework with a novel image-KG contrastive loss, which maximizes the mutual information between semantic and visual representations for knowledge infusion. Extensive experiments of applying the learnt visual representations for socioeconomic prediction on three datasets demonstrate the superior performance of KnowCL with over 30% improvements on R2 compared with baselines. Especially, our proposed KnowCL model can apply to both satellite and street imagery with both effectiveness and transferability achieved, which provides insights into urban imagery-based socioeconomic prediction.

Socioeconomic indicators reflect location status from various aspects such as demographics, economy, crime and land usage, which play an important role in the understanding of location-based social networks (LBSNs). Especially, several existing works leverage multi-source data for socioeconomic indicator prediction in LBSNs, which however fail to capture semantic information as well as distil comprehensive knowledge therein. On the other hand, knowledge graph (KG), which distils semantic knowledge from multi-source data, has been popular in recent LBSN research, which inspires us to introduce KG for socioeconomic indicator prediction in LBSNs. Specifically, we first construct a location-based KG (LBKG) to integrate various kinds of knowledge from heterogeneous LBSN data, including locations and other related elements like point of interests (POIs), business areas as well as various relationships between them, such as spatial proximity and functional similarity. Then we propose a hierarchical KG learning model to capture both global knowledge from LBKG and domain knowledge from several sub-KGs. Extensive experiments on three datasets demonstrate our model’s superiority over state-of-the-art methods in socioeconomic indicators prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KG-socioeconomic-indicator-prediction.

Every day, our living city produces a tremendous amount of spatial-temporal data, involved with multiple sources from the individual scale to the city scale. Undoubtedly, such massive urban data can be explored for a better city and better life, as what the urban computing community has been dedicating in recent years. Nevertheless, existing studies are still facing the challenges of data fusion for the urban data as well as the knowledge distillation for specific applications. Moreover, there is a lack of full-featured and user-friendly platforms for both researchers and developers in the urban computing scenario. Therefore, in this article, we present UrbanKG, an urban knowledge graph system to incorporate a knowledge graph with urban computing. Specifically, the system introduces a complete scheme to construct a knowledge graph for urban data fusion. Built upon the data layer, the system further develops the multiple layers of construction, storage, algorithm, operation, and applications, which achieve knowledge distillation and support various functions to the users. We perform representative use cases and demonstrate the system capability of boosting performance in various downstream applications, indicating a promising research direction for knowledge-driven urban computing.

N-ary relational knowledge bases (KBs) represent knowledge with binary and beyond-binary relational facts. Especially, in an n-ary relational fact, the involved entities play different roles, e.g., the ternary relation PlayCharacterIn consists of three roles, Actor, Character and Movie. However, existing approaches are often directly extended from binary relational KBs, i.e., knowledge graphs, while missing the important semantic property of role. Therefore, we start from the role level, and propose a Role-Aware Modeling, RAM for short, for facts in n-ary relational KBs. RAM explores a latent space that contains basis vectors, and represents roles by linear combinations of these vectors. This way encourages semantically related roles to have close representations. RAM further introduces a pattern matrix that captures the compatibility between the role and all involved entities. To this end, it presents a multilinear scoring function to measure the plausibility of a fact composed by certain roles and entities. We show that RAM achieves both theoretical full expressiveness and computation efficiency, which also provides an elegant generalization for approaches in binary relational KBs. Experiments demonstrate that RAM outperforms representative baselines on both n-ary and binary relational datasets.

With the rapid development of knowledge bases (KBs), link prediction task, which completes KBs with missing facts, has been broadly studied in especially binary relational KBs (a.k.a knowledge graph) with powerful tensor decomposition related methods. However, the ubiquitous n-ary relational KBs with higher-arity relational facts are paid less attention, in which existing translation based and neural network based approaches have weak expressiveness and high complexity in modeling various relations. Tensor decomposition has not been considered for n-ary relational KBs, while directly extending tensor decomposition related methods of binary relational KBs to the n-ary case does not yield satisfactory results due to exponential model complexity and their strong assumptions on binary relations. To generalize tensor decomposition for n-ary relational KBs, in this work, we propose GETD, a generalized model based on Tucker decomposition and Tensor Ring decomposition. The existing negative sampling technique is also generalized to the n-ary case for GETD. In addition, we theoretically prove that GETD is fully expressive to completely represent any KBs. Extensive evaluations on two representative n-ary relational KB datasets demonstrate the superior performance of GETD, significantly improving the state-of-the-art methods by over 15%. Moreover, GETD further obtains the state-of-the-art results on the benchmark binary relational KB datasets.