UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
Authors: Yansong Ning
Hao Liu
Hao Henry Wang
Zhenyu Zeng
Hui Xiong
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: Urban knowledge graph
Urban computing
Knowledge graph representation
TL;DR: Unified urban knowledge graph dataset for UrbanKG-enhanced urban spatiotemporal prediction is proposed.

Abstract: Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the development and operation of the smart city. As an emerging building block, multi-sourced urban data are usually integrated as urban knowledge graphs (UrbanKGs) to provide critical knowledge for urban spatiotemporal prediction models. However, existing UrbanKGs are often tailored for specific downstream prediction tasks and are not publicly available, which limits the potential advancement. This paper presents UUKG, the unified urban knowledge graph dataset for knowledge-enhanced urban spatiotemporal predictions. Specifically, we first construct UrbanKGs consisting of millions of triplets for two metropolises by connecting heterogeneous urban entities such as administrative boroughs, POIs, and road segments. 
Moreover, we conduct qualitative and quantitative analysis on constructed UrbanKGs and uncover diverse high-order structural patterns, such as hierarchies and cycles, that can be leveraged to benefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs, we implement and evaluate 15 KG embedding methods on the KG completion task and integrate the learned KG embeddings into 9 spatiotemporal models for five different USTP tasks. The extensive experimental results not only provide benchmarks of knowledge-enhanced USTP models under different task settings but also highlight the potential of state-of-the-art high-order structure-aware UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications. The dataset and source code are available at https://github.com/usail-hkust/UUKG/.

UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
Yansong Ning', Hao Liu! Ha Hao Wang',
Zhenyu Zeng', Hui Xiong
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)
2 CSE. The Hong Kong University of Science and Technology
3 Alibaba Cloud Intelligence Group
Mming0920connecee hwust edu. liuh@ust. ust.hk hk Alshenry@126.cm com zhenyu. z zzy ibaba- inc. com xi xionghui@uus @ust..kkuu..kkk hk
Abstract
Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the development and operation of the smart city. As an emerging building block, multi-sourced urban data are usually integrated as urban knowledge graphs (Ur- banKGs) to provide critical knowledge for urban spatiotemporal prediction models. However, existing UrbanKGs are often tailored for specific downstream prediction tasks and not publicly available, which limits the potential advancement. This paper presents UUKG, the unified urban knowledge graph dataset for knowledge- enhanced urban spatiotemporal predictions. Specifically, first construct Ur- GanKGs consisting of millions of triplets for two metropolises by connecting heterogeneous urban entities such administrative boroughs, POIs, and road seg- ments. Moreover, we conduct qualitative and quantitative analysis on constructed UrbanKGs and uncover diverse high-order structural patterns, such hierarchies and cycles, that can be leveraged to benefit downstream USTP tasks. To validate and facilitate the of UrbanKGs, we implement and evaluate 15 KG embedding methods on the KG completion task and integrate the learned KG embeddings into spatiotemporal models for five different USTP tasks. The extensive experimental results not only provide benchmarks of www.ldge-enhaneeee USTP models under different task settings but also highlight the potential state-of-the-ar high-order structure-aware UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications. The dataset and source code are available at http:://githubbbc wtaptthuu...omussaa Soasslll-hustUUUKGG
Introduction
Urban SpatioTeempora Prediction (USTP) aims to forecast future urban dynamics by simultaneously capturing the spatial and temporal autocorrelations from past observations. Recently, with the development of machine learning technologies and the collection of large-scale urban data, USTP has achieved remarkable progress in various urban predictive tasks, such as traffic management, pollution monitoring, and emergency response [1 USTP has become essential service of the modern smart city.
In prior literature. many efforts have been devoted to improving the USTP performance by ex- ploiting latent knowledge from diverse urban data sources [4. In particular, one commonly used approach is manually extracting features from different datasets to integrate additional information.
Corresponding author.
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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For example. Tompson et al. [5] and Chen al. [6] customize urban features from external dataset for crime prediction and bike trip prediction. However, these ap- proaches heavily rely deep understand- ing of the application domain and are labor- intensive. Recently, inspired by the suc- cess of the Knowledge Graph (KG) in nat- ural language processing tasks, the urban knowledge graph has been adopted to im- prove USTP. For instance, Wang et al. 77] construct a dedicated spatiotemporal knowledge graph by regarding trajectory and timestamp as entities to improve trajectory prediction. Liu et al. [8] construct check-in relations help mobility prediction. However, existing UrbanKGss are task-specific and none of them is publicly available, which discourages researchers from adopting it for their work and thus limits the flourishing of Knowledge-enhance urban spatiotemporal prediction.
Entty extraction
Admizistattee divison data
POT daia
Melilry
Reed Network dats
Stonge Eazzagatent
Criee
LibannCCC Conteraatinn
311 Sevice
Hktrogaooos Lriam Data Souros
Figure 1: The generation process of UUKG.
Therefore, open-sourced and multifaceted urban knowledge graph dataset compatible with various USTP tasks is necessary to be proposed. It a non non-trivial problem due to the following two challenges. (1) How to construct unified urban knowledge graphs? The urban data collected from different devices and providers describe the urban system from different aspects and granularities. They are usually disjoint datasets with different spatiotemporal ranges, which cannot be directly joint utilized for USTP. is appealing to extract and align heterogeneous urban knowledge in a unified graph organization to satisfy diverse downstream USTP requirements. (2) How preserve complicated structural urban knowledge? Existing UrbanKG-drive USTP approaches simply adopt general KG embedding methods or design complex task-specific module project symbolic entities and relations into low-dimensional embeddings, which fails to preserve unique structural patterns in the urban knowledge graph. As illustrated in Figure 2
the urban knowledge graph includes diverse structures such as hierarchy and cycle. It is crucial to capture such high-order semantic knowledge to empower downstream spatiotemporal prediction tasks.
To address the aforementioned challenges, in this study, we present an Unified Urban Knowledge Graph (UUKG) dataset for knowledge-enhanced urban spatiotemporal predictions. Figure Illustrates the workflow of UUKG construction. For a given city, we first construct an Urban Knowledge Graph (UrbanKG) from multi-sourced urban data. By extracting and organizing entities (e.g., POIs, road segments, etc.) into multi-relational heterogeneous graph, UrbanKG encodes various high- order structural patterns in unified configuration (i.e., multi-scale spatial hierarchy), which facilitates joint processing for various downstream USTP tasks. Moreover, we qualitatively and quantitatively analyze these diverse high-order structures (i.e., hierarchies and cycles in Figure 2) to guide us in using tailored KG embedding methods to derive effective and generalizable knowledge representations. By learning structure-aware embeddings of entities and relations using state-of-art non-Euclenn -un-Euclidean space embedding models (e.g., modeling hierarchies in hyperbolic space and capturing cycles in spherical space), the high-order structure information could preserved in a proper way.
Finally, we conduct comprehensive experiments to benchmark diverse urban tasks, including the UrbanKG completion task, three urban flow prediction tasks, and two urban event prediction tasks. The empirical studies not only validate the effectiveness of the unified urban knowledge graph for improving various USTP tasks, but also uncover the high-order structures within UrbanKG, with proper modeling, can further strengthen the urban spatiotemporal prediction performance.
Our contributions are summarized follows:
We propose UUKG, the first open-source UrbanKG dataset for knowledge-enhanced urban spatiotemporal predictions. As pilot study, we envision our experiences and results offering exciting opportunities to advance previous USTP methods.
We demonstrate the importance of urban high-order structure modeling for urban knowledge representation and investigate to use structure-aware KG embedding methods to capture them effectively.
Extensive experiments on KG completion and five USTP tasks demonstrate the effectiveness of the constructed UrbanKG and verify modeling high-order structures is beneficial urban spatiotemporal prediction.

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Table 1: The statistics of raw datasets.
Dataset
Description
Sumple format
New York
Chicago
Boundary Berough
I" N"New Yorek" r0.5er-40.50, 40.91, 74.25, 733.701
I
Administrative division
[" "Queens"" polygoni 40.54 -73.96 ...1]
# of Area
["Jamaica" "Queens" pobygon/40.69 (0.609 -73.82... ...1]
260
77
of Segment
[eoad id, rood viari junction, end junction. type, lin rangef [191751. "Queens Boulevard" , 59378, 4798, tertiars, Iinef40.72 -7.82,
110.199
71,578
Road network
of Category
of Junction
Uuuction id. jumction tipe, coordiuate] (59178, crossing, coordinate (4. 73.988]
62.627
37,322
5
+ of Canegory
5
6
POI
PO1 of Category
Spoi id, poi nome, poi type, coordinule]
62,450
31,573
[34672854, "Empire State Building" corporation, cordiinate -7399)]
15
15
2 UrbanKG Construction
We first introduce the datasets that will be used for urban knowledge graph construction and data preprocessing details, then we present the construction of urban knowledge graphs.
2.1 Data Collection and Preprocessing
We acquire urban knowledge for two large cities, New York and Chicago, from three data sources. Table Jablarissmmarizss the statistics of the datasets.
Administrative Division data. The administrative division describes multi-level spatial entities that the government uses to make administrative decisions, which contains rich geographical information for USTP task 9 [0]. In this work, we collect three administrative divisions, Le., city, borough, and area. We first manually define the rectangular latitude and longitude boundaries for each city (e.g.. New York and Chicago). Then we collect borough and area data from NYC Gov the official websites of New York and Chicago. Each borough record contains borough name and polygon boundaries. Each area record contains an area name, corresponding borough name, and the polygon boundaries. Example in Table iss record of "Jamaica" area in the "Queens" borough.)
LovFaam Plad CHI Gov
Road Network Data. The road network provides rich topology knowledge of the transportation system, which plays a critical role in various USTP tasks (e.g., traffic congestion prediction [1]] and traffic incident detection [2). We utilize the rectangular of each city to query the corresponding road network data (road segments and road junctions) from Open Street Map (OSM)) L. Each road segment record contains a unique id, a road name, a start road junction id, a end road junction id, and line geographical range. Each road junction record contains unique id, junction type and coordinate (latitude and longitude). Example in Table iis road record "Queens Boulevard", and it starts from junction 594778 and ends at junction 4798.
POI Data. POI data contains urban contextual semantics, which have been widely adopted as auxiliary features to enhance USTP tasks (e.g., traffic prediction [33 and crime prediction [14). We collect POI data from OSM. We utilize the boundary of each city from the administrative division data to query the corresponding POI records through the open API provided by OSM. Each POI record consists of unique id, a POI name, POI category and the coordinate. Example in Table is POI record where (40. 75, -73.99) is the location and the POI is a corporation.
Before constructing the UrbanKG, we first preprocess the raw datasets. We filter out POIs and road networks that don't 't belong to any administrative borough or area in each city. Besides, we merge minority POI categories (e.g., grandstand and canopy) to avoid potential long-tail issues. As records from different data sources are disjoint, we align each record according to the administrative areas.
2.2 Knowledge Graph Construction
Then introduce the process of constructing the urban knowledge graph (UrbanKG).
Definition UrbanKG. The UrbanKG is defined as a multi-relational graph G (E, R, F), where E, R and FF is the set of urban entities, relations and facts, respectively. In particular, facts are
https//www.nyc.govv
http://www..chcnng.goo/
hutps//www.pennteeemmppppp
3

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Table 2:
Major relations in UrbanKG.
Xelation
Symmetne
Head & Tad Eanny
Abbrev.
Kelation
Symmetne
Head & Tail Enity
Abbrev:
POI Locales Area
(POI, Area)
PLA
Jumction Belongs Road
Olunction Borought
JBR
(Road,
RLA
Horough Neaby
(Borough, Bonugh)
Junetion
(Bunction,
Neseby Area
Belongs Borough
(POI, Borough)
PBB
Category
Betongs Borough
(Road, Borough)
RBB
Koad His Category
(Road, RC)
Junction Belongs to Berough
x
Unuction, Barough)
J8B
Junction Has Junction Category
Uunction, JC)
JHC
Aren Locates Borough
(Ares, Roreughh
ALB
Eaity:
Borough
Area
POI
Homegeneous cycle
Heteerogeneoss cycle
Relation:
BNB
AL3
PLA
PHPC
Multi-levd uhan knowledge graph
Hlerarchy
Figure 2: An illustrative example of the urban knowledge graph with diverse hierarchical and eyclic structure. There are four types of entities (Borough, Area, POI and POI Category (PC) and five types of relations (BNB, ALB, ANA, PLA and PHPC).
defined as F= [H,,,, h,, E E, R), where each triplet r, t) describes that head entity is connected with tail entity t via relation r. For example, (Queen, Nearby, Brooklyn) represents the fact that Queen and Brooklyn are geographically adjacent.
The UrbanKG encodes diverse urban semantic knowledge such as the multi-level spatial adja- cency (e.g., the Hammels area in Queens borough) and ontology (e.g., category of POIs and roads). We detail the entity and relation extraction below.
2.2.1 Entity Extraction
We extract types entities for UrbanKG: (1) Administrative Borough (Borough). Borough describes high-level administrative boundaries of city. For example, New York has five boroughs: Queen, Bronx, Brooklyn, Manhattan, and Staten Island. (2) Functional Area (Area). Area refers to the subdivision of city according its function, such residential, industrial, and commercial areas. (3) Point-Of-Interest (POI). A POI is basic functional unit and venue. For example, cinemas and hospitals are two common types of POIs and they the places people often check in. (4) Road Segment (Road). Road is the key element in the city and forms the traffic network, which provides reference and basis for human mobility. (5) Road Junction (Junction). Junction where more road segments intersect. It important for the urban road network as they have a great impact on traffic capacity and safety. (6) POI Category (PC). POI category deseribes the property and function of POI, such as finance, catering and so on. We define 15 categories of POI including finance, (7) Road Category (RC). Road category describes the
parking area, shopping, catering, and features of the road segment, such as motorway, trunk and so on. We preserve the six-types of most frequent road segments including motorway (expressway or river-crossing tunnels), primary traffic road, secondary traffic road, tertiary traffic road, residential traffic road, and trunk (branch roads such expressway outbound bypass roads).
(8) Junetion Category (JC). Junction category helps distinguish several special types of road junctions, such as roundabout. We keep five types of road junction including motorway junction (road junction in expressway or river-crossing tunnel), traffic signal (road junction having traffic light), turning circle (road junction which is a roundabout), stop (road junction having stop signal) and crossing (road junetion with no special type).
We report the detailed statistics of entities in Appendix A.1
2.2.2 Relation Construction
As shown in Table 2 we define 13 relations for UrbanKG:

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Table 3: Statistics of two UrbanKGs. We report graph hyperbolicity values ò (always greater than equal to zero while lower means more hierarchical) and the number cycles.
Dotnect
# kuncy
Relation
#'rippet
# Iiain
Valid
Vese
#Cycle
Hyperbolicty
NYC
236,267
13
930,240
837.216
46,512
46,312
1,090,884 532,108
6=0
CHI
140,612
13
564,400
507.60
28,220
28.220
8-0
Geographic Inclusion. This type of re- lation describes the geographical inclusion relations between entities. We extract 5 geographical inclusion relations for Ur- banKG: (1) POI/Road/Junction Locates at Area (PLA/RIA/JLAA Through these relations, we can obtain the fact between Area and POL/Road/Junction. For example, triplet (POI 32, PLA, Area 75) indicates that POI 32 is lo- cated in Area 75. (2) POt/Road//unction Be- longs to Borough (PBB/RBB/JBB). Through these relations, we can obtain the fact between Borough and POL/Road/Junctio. (3) Area Lo- cates Borough (ALB). Through this relation, we can obtain the geographic inclusion relations between Area and Borough. For example, triplet (Area 75, ALB, Borough /) explains that Area 75 belongs to Borough 1. (4) Junction Belongs to Road (JBR). Through this relation, can describe the Junction which Road, thus could preserve the topology of the road network.
Ronoe
Brod
Figure 3: An illustrative visualization of the four UrbanKG entities in NYC and CHI.
Geographic Aadjacency. This type of relation describes the geographical adjacency relations between entities. We extract 2 relations of this type for UrbanKG: (1) Borough Neurby Borough (BNB). Through this relation, Area (ANA). Through this relation, we describe the adjacency between Areas.
obtain the adjacency fact between Boroughs. (2) Area Nearby
Category. This type of relation connects entities to the category they belong to. We extract three category relations for UrbanKG: (I) POI POI Category (PHPC). (2) Road Has Road Category (RHRC). (3) Junction Has Junction Category (JHJC). For example, triplet (POI 32, PHPC, PC 4) indicates POI 32 belongs to the category PC 4.
2.2.3 UrbanKG Management
Following the above process, we obtain UrbanKGs for two large cities as shown in Table 3 i.e. New York (NYC) and Chicago (CHI). To serve the large-scale knowledge graph with millions of triplets, we adopt Neo4j, a graph database system, to store, query, and update the UrbanKG. The data privacy issue critical in smart city applications. In this study, the dataset we use doesn't 't include any user-level information. However, recent studies prove that sensitive individual information can be inferred from a few location check-ins [15]. Thus, we round the coordinate of each record to 100 meters to avoid potential information leakage when incorporating UrbanKG with other urban pratio-temporal data.
2.3 Visualization and Structural Analysis
As shown in Figure the multi-level UrbanKG contains entities providing administrative division information (Borough & Arca), traffic topology (Road network), and urban function distribution (POI), and they together form rich semantic information (i.e., High-order hierarchy and high-order cycle) about the city.
High-order Hierarchy. Hierarchical information is ubiquitous in capturing knowledge from KG 18) since much knowledge is commonly organized hierarchically. For example, the hierarchy Figure Figure organizes urban knowledge in a hierarchical way. Embrac- ing and modeling such hierarchy empowers to uncover deeper semantics, e.g., we can infer the main function of an area from the POI quantities and categories this area. As shown in Table we also quantitatively find the hyperbolicities [19] of two UrbanKGs are zero, which means that UrbanKGs
5

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are not significantly different from a tree in terms of their geometric properties 220 21]. Thus they are suitable be represented hyperbolic space [2 19. where hierarchies can be losslessly embedded.
High-order Cycle. Cycle modeling is beneficial to improve knowledge representation [3 24 23 as implies rich semantics of graphs. We find their existence in constructed UrbanKG by analyzing the topological structure. For example, the homogeneous cycle and heterogeneous cycle in Figure 2 uncover Borough geographic semanties and the geographic semantics of Area and Borough, respectively. Moreover, Table counts the number of cycles in the two UrbanKG to further verify our analysis. Unlike hierarchy, these cycles are suitable to be modeled in spherical space 22 26
The qualitative and quantitative analysis reveal diverse high-order structural patterns in UrbanKG, and thus inspires us to leverage state-of-the afet high-order structure-aware knowledge graph embedding methods to extract tailored and comprehensive urban knowledge. For instance, UrbanKG can be embedded into hyperbolic space capture hierarchies into spherical space model cycles.
3 Structure-aware UrbanKG Embedding
This section investigates the impact of different structure-aware embedding methods (e.g., the hyperbolic space embedding module capture hierarchy) UrbanKG representation learning. We first give problem formulation of UrbanKG embedding and introduce how to utilize structure-aware embedding methods represent those high-order structures within UrbanKG.
3.1 Problem Formulation
Given the constructed UrbanKG G (E, F), we aim to learn low-dimensional entity embeddings eg and relation embeddings ER. so that diverse high-order structure knowledge can be preserved to benefit various downstream USTP tasks.
3.2 Experimental Setup
We compare the performance gap of different structure aware knowledge graph embedding methods to investigate which approaches are suitable for modeling the unique structures UrbanKG. The quality of UrbanKG embedding is evaluated using the link prediction task (27), which aims to predict the missing head or tail entity a triplet (h, r, l). For each UrbanKG Table we follow standard by adding inverse relations.
data augmentation protocol 28
Evaluation Metries. We report two popular metries: (1) Mean reciprocal rank (MRR), the mean of inverse of correct entity ranking (29). (2) Hits K 1K=,,3,10), the percentage of correct entities in top-K ranked entities [30 3T. Note we filter true triplets appearing in the training set during evaluation 30 because predicting a low rank for those triplets should not be penalized.
Methods. We utilize classical Euclidean methods as baselines and choose state-of-art structure- non- non-Euclidean approaches as backbones.
Euclidean Models: (1) TransE (30 is classical translational model; (2) DisMult 32 is matrix
factorization model; (3) ComplEx [36] is an extension of DisMult complex space; (4) RotatE 34 is rotation-based method in complex space; (5) MuRE [16] is translational distance method with a diagonal relational matrix. (6) TuckER (35) is tensor decomposition method. (7) QuatE:
36
hypercomplex space embedding method.
Non-Euclidean Models: (1) MuRP/MuRS [16] are hyperbolic or spherical model with diagonal relational matrix; (2) RotH/RefH 11 are hyperbolic embedding method with rotation or refelection.
(3) AttH [17] is hyperbolic embedding method combining rotation and reflection; (4) ConE [8] is a hyperbolic embedding method with transformation between hyperbolic cone; (5) M2GNN [26] is a GNN-based product space embedding model which can model cycles and hierarchies at the same time. (6) GIE 37 is product space model considering geometry interaction.
Implement Details. We implement all models using PyTorch. All experiments are conducted on eight NVIDIA RTX 3090 GPUs. The negative sampling size fixed to 50. We conduct hyperparameters grid search by early stop the validation sets and we report details in Appendix A.2
Due the large scale of UrbanKG, didn't perform Graph Neural Updater when reproducing.

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Table 4: Overall link prediction results on NYC and CHI dataset. We utilize E, C, S, H. P to denote the Euclidean, Complex, Spherical, Hyperbolic and Product space, respectively. Best results in each space are underlined.
Type
Space
Model
MRR
Hiceo
NYC
Hise
[its@1
MRR
Hiis 6 10
CHi
His 6
1its:
Transk
.307
.563
.470
-356
-001
Faclidean models
TuckER
.133
.316
.509
.666
535
Rocatli
.274
.363
.309
.220
3005
136
Complex
.259
.357
.305
115
385
337
Quazz:
.321
.388
.37
.282
.905
.910
422
345
MuRS
.128
.622
552
478
501
619
536
431
Muke
.345
255
RolH
Buclidean
models
ATTH
Conli:
.542
.69
445
617
535
M2GNN
.561
.338
.578
-521
540
631
571
481
GIE
.523
.665
800
523
552
.60
530
.98
Table 5: Statistics of five USTP datasets in NYC and CHI.
Dotaset
Texi
Bike
Mobility
NYC
Crime
service
Taxi
Bike
Mobility
CHI
Crise
311 service
of records
1,118.84
383,919
1.052,232
389.551
3,141,153
3,826,868
1,085 ,900
939,543
202.291
1,821,999
of vertices
260
2,500
1,600
260
260
1,500
1,000
77
77
spaa
04/01/20200 06/31/2420
0180//2021 31/12/20211
0001/2019-0631/2000
01/01/2021 33/12/2001
Time
120 minutes
3.3 Results
The embedding dimension is set to d= 32 for all methods. The result in Table indicates that almost all non- non-Euclidean (i.e., hyperbolic and spherical space) embedding methods outperform Euclidean embedding methods, aligning with their capability to explicitly represent hierarchical and cyclic structures urban knowledge graphs. Moreover, models that derive a product space (i.e., combination of hyperbolic space and spherical space) to simultaneously capture hierarchies and cycles obtain the dominant performance. This can be attributed to the fact that UrbanKGs often exhibit both high-order hierarchies and cycles, making them suitable for simultaneous modeling in hyperbolic space and spherical space. We report detailed results, analysis and implement details in Appendix A..2 A.2
Knowledge-enhanced Urban Spatio Prediction
In this section, we first give formal problem statement of USTP, then introduce how incorporate UrbanKG to enhance five USTP tasks. Finally, we extensively discuss the experimental results.
4.1 Problem Formulation
Let G = (V, E, A) denote spatial network (e.g., g., road network, sensor network, grid etc.), where V and E denote the set of vertices and edges. We use A to denote the adjacency matrix of the spatial network G. We further define the graph signal matrix x(() RNCC for G, where C denotes the dimension of feature, N |V| is the number of vertices, and x(1: represents the observations of spatio network G time step In general, the snowledgeennanceee urban spatiotemporal task aims to learn multi-step prediction function based on past observations and the UrbanKG g,
(X
x (t+1)
X(+) = f((X!! xx( ((t-++1) X....,))))))
(I)
where G is the UrbanKG we constructed, T is the input length of past observations, 7 is the future steps aim to predict.
4.2 Incorporating UrbanKG Embedding
We demonstrate the effectiveness of UrbanKG by directly concatenating the learned UrbanKG embeddings with the graph signal matrix. In this work, we consider the following five USTP tasks.
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Taxi Service Prediction. Taxi service prediction aims to predict future taxi inflow and outflow based on the historical taxi service. Following existing studies [8), we split the city into N disjoint areas, and connect the spatial network G based on area adjacency. Let Ctari denote the two- dimensional feature vector (inflow and outflow). We incorporate the UrbanKG by concatenating the area embeddings with the taxi flow features, x || denotes the concatenation operation.
G E R RNxCouue' where Claws = CArea || Ctazi and
Bike Trip Prediction. Bike trip prediction aims to predict future inflow and outflow based on the historical bike trip. We sample N roads and connect the spatial network G based road adjacency 13. Let Cbske represent the two-dimensional feature vector (inflow and outflow). The
road embedding Road is concatenated with it for UrbanKG fusion, X RNNxCuike where Coske' = @Road| Ciske-
Human mobility prediction aims to predict future human inflow and outflow based on historical human mobility. Following existing studies (39], We choose N POIs and obtain the spatial network G' based on their adjacency. Let Chuman denote the wwo-dimensional feature vector (inflow and outflow). We integrate the UrbanKG by concatenating human mobility
Human Mobility Prediction.
flow features with POI embedding, X E Rg.NxCnumon where Chuman' = EPOI || Chuman-
Crime Prediction. Crime prediction is binary prediction task based on the observation of historical crime events. Following existing studies (40), we split the city into N disjoint areas and connect the spatial network G according to area adjacency. Let Cerime denote the label of whether the crime
occurs. We concatenate the Area embedding Area with crime feature, x(9 E NCCommee where Cerime-
Cerime EArea
311 Service Prediction. 311 service prediction 41 also binary prediction task based on the
observation of historical 311 complaint events. We obtain the spatial network G in the same way as crime prediction. Let Cerevice denote the label of the 311 complaint occurrence. We concatenate the
Area embedding EArea with its feature, X (t)
E R2 RNNxC.erutee
where Cservice' = CArea || Cservice
4.3
Experimental Setup
The result in Table indicates that structure-awan non-Euclidean space embedding models can generate a better urban knowledge representation compared with Euclidean methods. To further verify modeling those high-order structures in Section 2.3 could enhance USTP tasks, we compare performance gaps for USTP tasks across different prediction time steps (3, 6, and 9) when using embeddings derived from different spaces. Next, we inject the embeddings from the space that yielded the greatest improvement into all downstream tasks and USTP models to confirm their benefits.
Data Description. The data statistics of five datasets are summarized in Table 5 and we provide the details of each dataset in Appendix A..3
Evaluation Metrics. We utilize MAE and RMSE for regression tasks for evaluation and use Micro-FI and Macro-F1 (43 for classification tasks for evaluation.
Methods. We compare our approach with the following methods: (1) Gate Recurrent Units (GRU)
44 (2) Auto-Encoder (AE) 45: (3) Long Short-Term Memory (LSTM) [46]; (4) SpatioTem-
poral Graph Convolution Network (STGCN) 47 (5) Attention Based Spatial-Temporte Graph
Convolutional Networks (ASTGCN) 48); (6) Temporal Graph Convolutional Network (TGCN) [49];
(7) Multivariate Time Graph Neural Networks (MTGNN) [50]: (8) Adaptive Graph Convolutional Recurrent Network (AGCRN) [51]; (9) Hierarchical Graph Convolution Networks (HGCN) [52]. We
incorporate UrbanKG embeddings from different spaces with USTP models as our approach, such as ASTGCN w/P and P denotes embedding from product space.
Implement Details. We split data with a ratio of 7:1:2 into training sets, validation sets and test sets. We use historical 12 time steps to predict the future 1 to 12 time steps. We provide implement details and more detailed results in Appendix A.4
4.4 Results
Comparison of UrbanKG Embedding Space Variants. As depicted Figure 4 we observe that the UrbanKG embeddings could enhance USTP tasks regardless of which spaces they come from.
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Table 6: Performance gain of product space UrbanKG embedding five spatiotemporal prediction tasks. All experimental results are the mean of independent experiments repeated and the standard deviation (Std) are reported.
Modd
Bac
NYC MoNilly
Uriee
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Additionally, hyperbolic and spherical space embeddings demonstrate greater performance improvements compared to Euclidean methods (i.e., Fuclidean and complex space). Notably, the embedding derived from the product space yields the most substantial benefit for both ur- ban flow forecasting and urban event prediction. Such result is consistent with their state-of-arr art performance in link prediction task. It indicates that explicitly representing urban structures is helpful for urban knowledge extraction and can be further explored to develop downstream tasks performances.
2.59
Tene Aky
NYC
Teeraky
Comparison of USTP Task Variants. Table shows the results of forecasting urban flow (i.e., taxi service, bike trip and mobility prediction) for future period of 30 mins and predicting ur-
6
Tine
(b) NYC Crine:
Timee
ban events (i.e., urban crime and 311 service prediction) for future period of 120 mins. As can be seen, integrating product space UrbanKG embeddings consistently improves the accuracy of five tasks. The urban knowledge graph shows great potential (2%- 10% improvement) in spatiotem- poral flow prediction tasks, and it also enhance (1 (1%-5% improvement) ) urban event prediction. It is worth mentioning that the embeddings derived from UrbanKG are task-agnostic, where the downstream task is completely unknown. Such results indicate the UrbanKG embedding successfully captures general urban knowledge which is transferable among different USTP tasks.
Figure 4: USTP performance comparison when incorporating embeddings derived by the best method of each space. (a) Performance on NYC Taxi. (b) Performance on NYC Crime.
Comparison of USTP Model Variants. It can be observed that integrating UrbanKG embeddings consistently improves the accuracy of six existing USTP models. The above results demonstrate well- extracted urban knowledge can still enhance existing USTP models (e.g., ASTGCN and HGCN) from the feature representation scenario although they may already have sufficiently complex modules.
We provide implement details in Appendix A.4 and report more detailed results in Appendix A.5
5 Related Work
Domain Knowledge Graph Construction. Knowledge graph has been widely studied in both academia and industry. In recent years, many open-source knowledge graphs have been released to
9

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improve applications in various domains. For example, CKGG [53,, OpenBG [54], BioKG [55] and recent proposed ProteinKG25 [56] are used for education, business, biomedical and protein research field respectively. However, there is still no open-source knowledge graph for urban spatiotemporal prediction tasks in the urban computing field.
Knowledge Graph Embedding. Knowledge graph embedding aims to project entities and relations into low-dimensional vectors, which has been widely used for various KG-based applications. Overall, the knowledge graph embedding method can be categorized into two classes, the Euclidean embedding method and the non- Euclidean embedding method. The Euclidean embedding method derives KG embeddings in the Euclidean space. For example, TransE [30], TransH [57], TransR [58], RESCAL [59] DistMult [32]. TuckER (35] and ConvE [60] utilize translation, matrix decomposition, or neural network to model entities and relations and derive embeddings. The non -uclldeid embedding method aims to capture more complicated structures in the latent space. For example, MuRP [6]. AttH [17], ConE [18] and UltraE [6]] model hierarchies in the hyperbolic or ultrahyperbolic space. Recently, several mixed curvature embedding methods [26 37 62 have been proposed to simultaneossly encode diverse structures (e.g., hierarchies and cycles) in a product space (i.e., products of constant-curvatue spaces). The above structure-aware non-Euclidean methods show great potential for modeling various high-order structures in UrbanKG.
Knowledge-enhanced Urban Spatiotemporal Prediction. Knowledge graph has been proven useful in various urban tasks, such as traffic flow prediction 663]
64 65 66). mobility prediction 7 67 68)
site selection [69]. city profiling 770 7T 72 73] and so on 774. Their common approach involves
manually constructing a UrbanKG, and then obtaining embeddings using classical methods like
TransE (30 or TuckER [55). Although some very recent methods 77
71 72 design task-relevant
module to capture more granular urban knowledge, their UrbanKGs are still designed for specific tasks and are publicly not available, which discourages researchers from adopting for their own work as building
UrbanKG from scratch is time-consuming and labor-intensive. In fact, several recent works 75 g are striving towards this problem. However, they only describe a UrbanKG construction scheme or system but do not offer an open-source UrbanKG.
6 Conclusion and Limitation
In this work, we proposed UUKG, a unified dataset for knowledge-enhanced urban spatiotemporal prediction. To the best of our knowledge, UUKG is the first open-source urban knowledge graph dataset compatible with various aligned USTP tasks in the urban computing field. Extensive ex- perimental results demonstrate the universal advancement of UUKG in improving diverse USTP tasks. Additionally, we also prove the benefits of adopting state-of-art art structure-aware UrbanKG embeddding methods to further leverage rich high-order structures in UUKG.
Our work provides a unified UrbanKG construction fremework but it has limitations in terms of the dataset generalizability, as all experiments were conducted in two US metropolises. In addition, we only consider concatenation operation for embedding fusion. It will be an interesting topic that how to suitably incorporate the learned embeddings further enhance the performance of the urban spatiotemporal prediction. Despite the above limitations, we hope the constructed dataset can foster more extensive urban knowledge graph research and broad smart city application.
7 Maintenance Plan and Future Work
To ensure ease of access and future maintenance, the dataset and models evaluated in our paper are hosted on websites and homepage including detailed dataset descriptions and the evaluation code. The full dataset is available at Google Drive.
storage platforms. Specifically, we have created the UUKG
As an emerging building block, urban knowledge graph provides critical knowledge for urban spatiotemporal prediction models. Inevitably, more dataset, benchmarks and tools will be needed to facilitate its potential achievement. Therefore, we plan to continuously update UUKG by adding new urban entities derived from different data sources, new downstream tasks, and more friendly data usage toolkits and model interfaces. UUKG currently only contains the urban structural dataset for five types of USTP tasks. In the future, we will derive extra multi-modal data (e.g., images, reviews) to enrich the UrbanKG and introduce more USTP tasks (e.g., trajectory prediction and site selection).
10

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Acknowledgments and Disclosure of Funding
This research was supported in part by the National Natural Science Foundation of China under Grant No.62102110 the Alibaba Innovative Research Program (AIR). We appreciate the guidance provided by the team Mr. Wang Qing, who the Regional General Manager of Alibaba Cloud, in the Chongqing City Brain project.
References
[i] Fangzhou Sun, Abhishek Dubey, and Jules White. Dxnat- deep neural networks explaining non- recurring traffic congestion. In 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017.
Boston, MA, USA, December 11-14, 2017, pages
[2] Weiyu Cheng, Yanyan Shen, Yanmin Zhu, and Linpeng Huang. A neural attention model for urban air quality inference: Learning the weights of monitoring stations. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, AAAI 2018, USA, February 2-7, 2018, pages 2151-2158, 2018.
[3] Quanjun Chen, Xuan Song, Harutoshi Yamada, and Ryosuke Shibasaki. Learning deep representation from big and heterogeneous data for traffic accident inference. In Proceedings of the Thirtieth AAAI Conference Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA. pages 338 344, 2016.
[4] Xu Geng, Yilun Jin, Zhengfei Zheng, Yu Yang, Yexin Li, Han Tian, Peibo Duan, Leye Wang. Jiannong Cao, Hai Yang, et al. Citynet: A multi-city multi-modal dataset for smart city applications. arXiv preprint arXiv:2 106. 15802, 2021.
[5] Lisa Tompson, Shane D. Johnson, Matthew P. J. Ashby, Chloe Perkins, and Phil Edwards. Uk open source crime data: accuracy and possibilities for research. Cartography and Geographic Information Science, pages 111 97, 2015.
[6] Longbiao Chen, Daqing Zhang, Gang Pan, Xiacjuan Ma, Dingqi Yang, Kostadin Kushlev, Wangsheng Zhang, and Shijian Li. Bike sharing station placement leveraging heterogencous urban open data. In Pruceedings of the 2015 ACM International Joint Conference Pervasive and Ubiquitous Computing, UbiComp 2015, Osaka, Japan, September 7-11, 2015, pages 571-575, 2015.
[7] Huandong Wang, Qiaohong Yu, Yu Liu, Depeng Jin, and Yong Li. Spatio tempiol urban knowledge graph enabled mobility prediction. Proc. ACM Interact. Mob. Wearable Ubiqqiioou Technol., pages 184:1-184:24 2021.
[8] Yu Liu, Jingtao Ding, and Yong Li. Developing knowledge graph hased system for urhan computing. In Proceedings the 1st ACM SIGSPATIAL International Workshop Geospatial Knowledge Graphs, pages 3-7, 2022.
[9] Jintao Ke, Hai Yang. Hongyu Zheng, Xiqun Chen, Yitian Jia, Pinghua Gong, and Jieping Ye, Hexagon- based convolutional neural network for supply-demane forecasting of ride-sourcing services. IEEE Trans. Intell. Transp. Syst., pages 4160-4173, 2019.
[10] Junbo Zhang. Yu Zheng, Dekang Qi. Ruiyuan Li, and Xiuwen Yi. Dnn-based prediction model for spatio-temporal data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2016, Burlingame, California, USA, October 31 -ovember 3, 2016, pages 92:1-92:4, 2016.
[11] Honglei Ren, You Song, Jingwen Wang, Yucheng Hu, and Jinzhi Lei. deep learning approach to the citywide traffic accident risk prediction. In 21st International Conference Intelligent Transportation Systems, ITSC 2018, Maui, HI, USA, November 4-7, 2018, pages 3346-3351, 2017.
[12] Xiaolei Ma, Haiyang Yu, Yunpeng Wang, and Yinhai Wang. Large-scale transportation network congestion evolution prediction using deep learning theory. PloS one, page e01 19044, 2015.
[13] Mingqi Lv, Zhaoxiong Hong, Ling Chen, Tieming Chen, Tiantian Zhu, and Shouling Ji. Temporal multi- graph convolutional network for traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, pages 3337-3348, 2020.
[14] Hongjian Wang, Daniel Kifer, Corina Graif, and Zhenhui Jessie Li. Crime rate inference with big data. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Son Francisco, CA, USA, August 13-17, 2016, pages 635 644, 2016.

---

[15] Shuai Xu, Xiaoming Fu, Dechang Pi, and Zhuo Ma. Inferring individual human mobility from sparse check-in data: lempporal-cottxx-awarre approach. IEEE Transactions on Computational Social Systems. pages 1-12, 2022.
[16] Ivana Balazevic, Carl Allen, and Timothy Hospedales. Multi-relational poincaré graph embeddings. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 146554475, 2019.
[17] Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, and Christopher Ré. Low-dimensional hyperbolic knowledge graph embeddings. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 6901-6914, 2020.
[18] Yushi Bai, Zhitao Ying, Hongyu Ren, and Jure Leskovec. Modeling heterogeneous hierarchies with selatfon-peciffic hyperbolic cones. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurlPS 2021, December 6-14, 2021, virtual,
pages 12316-12327, 2021.
[19] Ines Chami, Zhitao Ying. Christopher Ré, and Jure Leskovec. Hyperbolic graph convolutional neural networks. In Advances in neural information processing systems, 2019.
[20] Cornelia Drutu and Michael Kapovich. Geometric group theory, volume 63. 2018.
[21] Mikhael Gromov. Hyperbolic groups. 1987.
[22] Frederic Sala, Christopher De Sa, Albert Gu, and Christopher Ré. Representation tradeoff's for hyperbolic embeddings. In Proceedings of the 35th International Conference on Machine Learning, ICML. 2018, Stockholmsmässsan, Stockholm, Sweden, July 10-15, 2018, pages 4457-4466, 2018
[23] Han Xiao, Minlie Huang, and Xiaoyan Zhu. From one point a manifold: Knowledge graph embedding for precise link prediction. arXiv preprint arXiv:1512.0479 2015.
[24] Yu Meng, Jiaxin Huang, Guangyuan Wang. Chao Zhang. Honglei Zhuang, Lance Kaplan, and Jiawei Han. Spherical text embedding. In Advances in neural information processing systems, 2019.
[25] Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein. Understanding over-squashing and bottlenecks graphs via curvature. arXiv preprint arXiv:2 14522, 2021.
[26] Shen Wang, Xiaokai Wei, Cicero Nogueira Nogueira dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew multi-relations graph neural network
Arnold, Bing Xiang. Philip S Yu, and Isabel F Cruz. for knowledge graph completion. In WWW '2:: The Web Conference 2021, Virtual Event / Ljubljana. Slovenia, April 19-23, 2021, pages 1761-1771, 2021.
[27] Antoine Bordes, Jason Weston, Ronan Collobert, and Yoshua Bengio. Learning structured embeddings of knowledge bases. In Proceedings of the Twenry-Fii FFitt AAAI Conference on Artificial Intelligence, AAAI 20 San Francisco, California, USA, August 7-11, 2011, 2011.
[28] Timothée Lacroix. Nicolas Usunier, and Guillaume Obozinski. Canonical tensor decomposition for knowledge base completion. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässsan, Stockholm, Sweden, July 10-15, 2018, pages 2869-2878, 2018
[29] Rakesh Agrawal. Sreenivas Gollapudi, Alan Halverson, and Samuel leong. Diversifying search results. In Proceedings of the Second International Conference on Web Search and Web Data Mining, WSDM 2009, Barcelona, Spain, February 9-11, 2009, pages 54,, 2009.
[30] Antoine Bordes, Nicolas Usunier, Alberto Garcia- Duran, Jason Weston, and Oksana Yakhnenko. Translat- ing embeddings for modeling nulti-relational data. In Advances in Neural Information Processing Systems 26: Annual Conference Neural Information Processing Systems 2013, December 5-8, 2013, Lake Tahoe, Nevoda, United States, pages 2787-2795, 2013.
[31] Zixuan Yuan, Hao Liu, Junming Liu, Yanchi Liu, Yang Yang, Renjun Hu, and Hui Xiong. Incremental spatio-temporal graph learning for online query-poi matching. In Proceedings of the Web Conference 2021, pages 1586-1597 2021.
[32] Bishan Yang. Wen- tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint 112.:24 2014.
12

---

[33] Théo Trouillon. Johannes Welbl. Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. Complex embeddings for simple link prediction. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1071-0 2016.
[34] Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. Rotate: knowledge graph embedding by relational rotation in complex space. arXiv preprint Dediv:190..1111. 2019.
[35] Ivana Balažević, Carl Allen, and Timothy M Hospedales. Tucker: Tensor factorization for knowledge
graph completion. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IICNE 2019, Hong Kong, China, November 3-7, 2019, pages 5184-5193, 2019.
[36] Zongshheng Cao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. Dual quaternion knowledge graph embeddings. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 6894 6902, 2021.
[37] Zongsheng Cao. Qianqian Xu, Zhiyong Yang. Xiaochun Cao, and Qingming Huang. Geometry interaction knowledge graph embeddings. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Virtual, February 22- March 1, 2022, pages 5521-5529, 2022.
[38] Noel Cressie and Christopher K Wikle. Statistics for spatio-temporal data. John Wiley & Sons, 2015.
[39] Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. Deepmove: predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 World Wide Web Conference, WWW 2018, Lyon, France, April 23-27, 2018, pages 1459-1468, 2018.
[40] Chao Huang, Junbo Zhang, Yu Zheng, and Nitesh V Chawla. Deeperime: Attentive hierarchical recurrent networks for crime prediction. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018, pages 1423-1432, 2018.
[41] Scott L Minkoff. Nyc 311: A tract-level analysis of beizzn-goveementt contacting in new york city. Urban Affairs Review, (122)22112266, 2016.
[42] Cort Willmott and Kenji Matsuura. Advantages of the mean absolute error (mac) over the root mean square error (rmse) in assessing average model performance. Climate research, pages 79-82, 2005.
[43] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855-864, 2016.
[44] Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. On the properties of neural machine translation: decoder approaches. In Proceedings of ESST 2014, Eighth Workshop Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 25 October 2014, pages 103-111, 2014.
[45] Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang. Transforming auto-encoders. In Artificial Neural Networks and Machine Learning- ICANN 2011 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I, pages 44-51, 2011.
[46] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation. ((8):173-1788) 1997.
[47] Bing Yu, Haoteng Yin. and Zhanxing Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the Sweent-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pages 5634-3640, 2017.
[48] Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. Attention based apatial-tempora graph convolutional networks for traffic flow forecasting. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, Honolulu, Hawaii, USA, January 27 February 1, 2019, pages 922-929, 2019.
[49] Ling Zhao, Yujiao Song. Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. T-gen: A temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst., pages 3848-3858 2019.
[50] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang. Xiaojun Chang, and Chengqi Zhang. Connecting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 753-763. 763, 2020.
13

---

[51] Lei Bai, Lina Yao, Can Li, Xianzhi Wang. and Can Wang. Adaptive graph convolutional recurrent network for traflic forecasting. volume 33, pages 17804-17815, 2020.
[52] Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, and Baocai Yin. Hierarchical graph convolution network for traffic forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 1599 2021.
[53] Yulin Shen, Ziheng Chen, Gong Cheng, and Yuzhong Qu. Ckgg: A chinese knowledge graph for high- school geogruphy education and beyond. In The Semantic Web ISWC 2021 20th International Semantic Web Conference, ISWC 2021, Virtual Event, October 24-28, 2021, Proceedings, pages 129-445, 2021.
[54] Shumin Deng, Hui Chen, Zhoubo Li, Feiyu Xiong, Qiang Chen, Mosha Chen, Xiangwen Liu, Jiaoyan Chen, Jeff Z Pan, Huajun Chen, et al. Construction and applications of open business knowledge graph. arXiv preprint arXiv:2209. 15214, 2022.
[55] Brian Walsh, Sameh K Mohamed, and Vit Nováček. Biokg: A knowledge graph for relational learning on biological data. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Viriual Event, Ireland, October 19-23, 2020, pages 3173-3180) 2020.
[56] Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong, Shumin Deng, Jiazhang Lian, Qiang Zhang, and Huajun Chen. Ontoprotein: Protein pretraining with gene ontology embedding. arXiv preprint or Xiv:2201. 11 147, 2022.
[57] Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen, Knowledge graph embedding by translating on hyperplanes. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada, pages 1 1112-1119, 2014.
[58] Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan 7hu. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Junuary 25-30. 2015, Austin, Texus, USA. pages 2181-2187, 2015.
[59] Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. A three-way model for collective learning on multi-relational data, In Proceedings of the 28th International Conference on Machine Learning, ICML 201 Bellevue, Washington, USA, June 28- July 2, 2011, pages 809- 816, 2011.
[60] Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2d knowledge graph embeddings. In Proceedings of the Shirty-Seconde AAAI Conference on Artificial Intelligence, AAAI 2018, New Orleans, Louisiana, USA, February 2-7, 2018, pages 1811-1818, 2018
[61] Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, and Steffen Staab, Ultrahyperbolic knowledge graph embeddings. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14- 18, 2022, pages 2130-2139, 2022.
[62] Roshni G. Iyer. Yunsheng Bai, Wei Wang. and Yizhou Sun. Dual-geometric space embedding model for two-view knowledge graphs. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA. August 14 18, 2022, pages 376-686.77 2022.
[63] Chengbiao Yang and Guilin Qi. An urban traffic knowledge graph-driven spatial-temporal graph convolu- tional network for traffic flow prediction. In Proceedings of the I I1th International Joint Conference on Knowledge Graphs, pages 1 110111,, 2022.
[64] Jiyuan Tan, Qianqian Qiu, Weiwei Guo, and Tingshuai Li. Research on the construction of a knowledge graph and knowledge reasoning model in the field of urban traffic. Sustainability. 13(6):3191, 2021.
[65] Jia Liu, Tianrui Li, Shenggong Ji, Peng Xie, Shengdong Du, Fei Teng, and Junbo Zhang. Urban flow pattern mining based on multi-source heterogeneous data fusion and knowledge graph emhedding. IEFE Transactions on Knowledge and Data Engineering, 2021.
[66] Ling Zhao, Hanhan Deng. Linyao Qiu, Sumin Li, Zhixiang Hou, Hai Sun, and Yun Chen, Urban multi- source spatio-temporal data analysis aware knowledge graph embedding. Symmetry, 12(2):199, 2020.
[67] Chenyi Zhuang, Nicholas Jing Yuan, Ruihua Song, Xing Xie, and Qiang Ma. Understanding people lifestyles: Construction of urban movement knowledge graph from gps trajectory. In Proceedings of the Twenty-Sixve International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia. August 19-25, 2017, pages 0616-3623, 2017.
14

---

[68] Haoran Xin, Xinjiang Lu. Tong Xu, Hao Liu, Jingjing Gu. Dejing Dou, and Hui Xiong. Out-of-town recommendation with travel intention modeling. In Proceedings of the AAAI Conference Artificial Intelligence, volume 35, pages 4529-4536, 2021.
[69] Yu Liu, Jingtao Ding, and Yong Li. Knowledge-drivee site selection via urban knowledge graph. arXiv preprint urXiv:2111.0077, 2021.
[70] Zhilun Zhou, Yu Liu, Jingtao Ding, Depeng Jin, and Yong Li. Hierarchical knowledge graph learning enabled socioeconomic indicator prediction in location- social network. Proceedings of the ACM Web Conference, pages 122 132, 2023.
[71] Liu, Zhillnn Zhou, Yong Li, Depeng Jin. Urban knowledge graph aided mobile user profiling, ACM Trans. Knowl. Discov. Data, 1(1), 2023.
[72] Liu, Xin Zhang, Jingtao Ding, Yanxin Xi, and Yong Li. Knowledge-infunee infused contrastive learning for urban imagery-basee socioeconomic prediction. In Proceedings of the ACM Web Conference, pages 4150-41 1.60, 2023.
[73] Weijia Zhang, Hao Liu, Lijun Zha, Hengshu Zhu, Ji Liu, Dejing Dou, and Hui Xiong. Mugrep: A multi-task hierarchical graph representation learning framework for real estate appraisal. In Proceedings of the 27th ACM SIGKDD Conference Knowledge Discovery & Data Mining, pages 3937 3937-3947, 2021.
[74] Qingyan Zhu, Yize Chen, Hao Wang, Zhenyu Zeng. and Hao Liu. A knowledge-enhaneed framework for imitative transportation trajectory generation. In 2022 IEEE International Conference on Data Mining (ICDM), pages 823-832. IEEE, 2022.
[75] Yu Liu, Jingtao Ding. Yanjie Fu, and Yong Li. Urbankg: An urban knowledge graph system. ACM Transactions on Intelligent Systems and Technology. 14(4):1-25, 2023.
[76] Diederik P Kingma and Jimmy Ba. Adam: method for stochastic optimization. arXiv preprint arXiv: (et1:1412.6980 69800 2014.
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Reviews:
[{"review_id": "mkfXKVMMWO", "reviewer": "Reviewer_QJBt", "summary": "", "questions": "", "limitations": "It is worth noting a potential area for improvement regarding the authors' claim of the dataset being \"unified.\" As the dataset includes only two US cities, it would be better to acknowledge the limitation that the findings may not generalize to cities in other countries or of different scales.", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "The authors have effectively elucidated the significance of such a dataset in advancing research in this domain. The technical construction is robust.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "c6gcUpDRsh", "reviewer": "Reviewer_SDLE", "summary": "", "questions": "", "limitations": "1. The setting in Sec. 4.2 is a little bit confusing. Do you concatenate the learned KG embeddings for inputs only?\n2. It is suggested that the authors add std for Table 6 and Figure 4 to better understand the variance.", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. This paper presents the first open-sourced Urban Knowledge Graph for spatial-temporal prediction.\n2. The experiments show that adding the learned knowledge graph embeddings for downstream tasks (e.g., bike flow prediction, crime prediction) could improve model's performance.\n3. The writing is clear in general. ", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "54KiGoxRN0", "reviewer": "Reviewer_fgmx", "summary": "", "questions": "", "limitations": "The paper presents great efforts in constructing the Urban KGs. However, the good quality of the proposed dataset is essential for researchers to present a fair and valuable evaluation of their methods. In other words, the novel benchmark dataset is even more important than a novel method. It will guide the direction and quality of new models in this area since the researchers will regard it as the benchmark and use it for evaluation. From this point of view, the following 4 points can be improved:\n\n1. Lack of comparison between the proposed Urban KGs with existing Urban KGs, such as [1].\n\n     [1] Liu, Yu, et al. \"UrbanKG: An Urban Knowledge Graph System.\" ACM Transactions on Intelligent Systems and Technology 14.4 (2023): 1-25.\n\n2. More baseline models should be compared, especially for existing hyper-graph models for urban computing, such as [2][3][4][5].\n\n     [2] Li, Zhonghang, et al. \"Spatial-temporal hypergraph self-supervised learning for crime prediction.\" 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022.\n\n     [3] Zhang, Qianru, et al. \"Automated Spatio-Temporal Graph Contrastive Learning.\" Proceedings of the ACM Web Conference 2023.\n\n     [4] Sun, Yanfeng, et al. \"Dual dynamic spatial-temporal graph convolution network for traffic prediction.\" IEEE Transactions on Intelligent Transportation Systems 23.12 (2022): 23680-23693.\n\n     [5] Wang, Chenyu, et al. \"Hagen: Homophily-aware graph convolutional recurrent network for crime forecasting.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. No. 4. 2022.\n\n3. The datasets split the train : valid: test as 18:1:1 for both NYC and CHI. I am wondering why the authors select this ratio. Meanwhile, the link prediction results are still not that good with such a split ratio. Does it indicate the dataset is not of good quality? Besides, more current KGE models should be evaluated, such as CoMPGCN [2]. More compared models can be found in related surveys [6][7][8].\n\n     [6] Vashishth, Shikhar, et al. \"Composition-based multi-relational graph convolutional networks.\" arXiv preprint arXiv:1911.03082 (2019).\n\n     [7] Ji, Shaoxiong, et al. \"A survey on knowledge graphs: Representation, acquisition, and applications.\" IEEE transactions on neural networks and learning systems 33.2 (2021): 494-514.\n\n     [8] Liang, Ke, et al. \"Reasoning over different types of knowledge graphs: Static, temporal and multi-modal.\" arXiv preprint arXiv:2212.05767 (2022).\n\n4. Missing some related works. As for the KG construction, more information can be referred to in [9]. Some GNN based KGE models are not mentioned (refering to [7][8])\n\n     [9] Zhong, Lingfeng, et al. \"A comprehensive survey on automatic knowledge graph construction.\" arXiv preprint arXiv:2302.05019 (2023).", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. Clear paper structure, and the paper is easy to follow.\n\n2. Open source datasets.\n\n3. The dataset is about a novel topic that may contribute to the practical scenarios of KGs.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "95mP7pcqvb", "reviewer": "Reviewer_E5fz", "summary": "", "questions": "", "limitations": "The authors adequately don’t address the limitations. This paper should consider more operations such as addition, multiplication, etc. to integrate UrbanKG embedding into the Urban spatiotemporal prediction.", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. This paper qualitatively and quantitatively analyzes the diverse high-order structures ((i.e., hierarchies and cycles) in UrbanKG dataset.\n2. The evaluation experiments that include implementation of KG embedding methods and integrating KG embeddings into spatiotemporal models, which shows the importance of urban high-order structure modeling. \n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "FjYgS9v9yW", "reviewer": "Reviewer_6kYe", "summary": "", "questions": "", "limitations": "Yes.", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. The paper is easy to follow and well-written. The idea of using urban knowledge graphs to improve downstream tasks is interesting.\n2. Extensive knowledge-graph embedding methods are implemented. The empirical results show that product space-based methods obtain dominant performance.\n3. The improvements of incorporating learned KG embeddings are significant on five downstream USTP tasks, which validates the effectiveness of the embeddings and opens new research opportunities.\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: All the reviewers have appreciated the work and given good scores. I too concur with them and recommend acceptance.
Decision: Accept (Poster)