232 research outputs found
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
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/.Comment: NeurIPS 2023 Track on Datasets and Benchmark
Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting
Knowledge Graph Completion (KGC) often requires both KG structural and
textual information to be effective. Pre-trained Language Models (PLMs) have
been used to learn the textual information, usually under the fine-tune
paradigm for the KGC task. However, the fine-tuned PLMs often overwhelmingly
focus on the textual information and overlook structural knowledge. To tackle
this issue, this paper proposes CSProm-KG (Conditional Soft Prompts for KGC)
which maintains a balance between structural information and textual knowledge.
CSProm-KG only tunes the parameters of Conditional Soft Prompts that are
generated by the entities and relations representations. We verify the
effectiveness of CSProm-KG on three popular static KGC benchmarks WN18RR,
FB15K-237 and Wikidata5M, and two temporal KGC benchmarks ICEWS14 and
ICEWS05-15. CSProm-KG outperforms competitive baseline models and sets new
state-of-the-art on these benchmarks. We conduct further analysis to show (i)
the effectiveness of our proposed components, (ii) the efficiency of CSProm-KG,
and (iii) the flexibility of CSProm-KG.Comment: Accepted by ACL 2023 Findings, Long Pape
Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion
Knowledge Graph Completion (KGC) has been recently extended to multiple
knowledge graph (KG) structures, initiating new research directions, e.g.
static KGC, temporal KGC and few-shot KGC. Previous works often design KGC
models closely coupled with specific graph structures, which inevitably results
in two drawbacks: 1) structure-specific KGC models are mutually incompatible;
2) existing KGC methods are not adaptable to emerging KGs. In this paper, we
propose KG-S2S, a Seq2Seq generative framework that could tackle different
verbalizable graph structures by unifying the representation of KG facts into
"flat" text, regardless of their original form. To remedy the KG structure
information loss from the "flat" text, we further improve the input
representations of entities and relations, and the inference algorithm in
KG-S2S. Experiments on five benchmarks show that KG-S2S outperforms many
competitive baselines, setting new state-of-the-art performance. Finally, we
analyze KG-S2S's ability on the different relations and the Non-entity
Generations.Comment: COLING 2022 Main Conferenc
Relphormer: Relational Graph Transformer for Knowledge Graph Representations
Transformers have achieved remarkable performance in widespread fields,
including natural language processing, computer vision and graph mining.
However, vanilla Transformer architectures have not yielded promising
improvements in the Knowledge Graph (KG) representations, where the
translational distance paradigm dominates this area. Note that vanilla
Transformer architectures struggle to capture the intrinsically heterogeneous
structural and semantic information of knowledge graphs. To this end, we
propose a new variant of Transformer for knowledge graph representations dubbed
Relphormer. Specifically, we introduce Triple2Seq which can dynamically sample
contextualized sub-graph sequences as the input to alleviate the heterogeneity
issue. We propose a novel structure-enhanced self-attention mechanism to encode
the relational information and keep the semantic information within entities
and relations. Moreover, we utilize masked knowledge modeling for general
knowledge graph representation learning, which can be applied to various
KG-based tasks including knowledge graph completion, question answering, and
recommendation. Experimental results on six datasets show that Relphormer can
obtain better performance compared with baselines. Code is available in
https://github.com/zjunlp/Relphormer.Comment: Work in progres
Refined Core Relaxation for Core-Guided MaxSAT Solving
Maximum satisfiability (MaxSAT) is a viable approach to solving NP-hard optimization problems. In the realm of core-guided MaxSAT solving - one of the most effective MaxSAT solving paradigms today - algorithmic variants employing so-called soft cardinality constraints have proven very effective. In this work, we propose to combine weight-aware core extraction (WCE) - a recently proposed approach that enables relaxing multiple cores instead of a single one during iterations of core-guided search - with a novel form of structure sharing in the cardinality-based core relaxation steps performed in core-guided MaxSAT solvers. In particular, the proposed form of structure sharing is enabled by WCE, which has so-far not been widely integrated to MaxSAT solvers, and allows for introducing fewer variables and clauses during the MaxSAT solving process. Our results show that the proposed techniques allow for avoiding potential overheads in the context of soft cardinality constraint based core-guided MaxSAT solving both in theory and in practice. In particular, the combination of WCE and structure sharing improves the runtime performance of a state-of-the-art core-guided MaxSAT solver implementing the central OLL algorithm
Minimizing and balancing envy among agents using Ordered Weighted Average
International audienceIn the problem of fair resource allocation, envy freeness is one of the most interesting fairness criterion as it ensures that no agent prefers the bundle of another agent. However, when considering indivisible goods, an envy-free allocation may not exist. In this paper, we investigate a new relaxation of envy freeness consisting in minimizing the Ordered Weighted Average (OWA) of the envy vector. The idea is to choose the allocation that is fair in the sense of the distribution of the envy among agents. The OWA aggregator is a well-known tool to express fairness in multiagent optimization. In this paper, we focus on fair OWA operators where the weights of the OWA are decreasing. When an envy-free allocation exists, minimizing OWA will return this allocation. However, when no envy-free allocation exists, one may wonder how fair min OWA allocations are. After some definitions and description of the model, we show how to formulate the computation of such a min OWA allocation as a Mixed Integer Program. Then, we investigate the link between the min OWA allocation and other well-known fairness measures such as max min share and envy freeness up to one good or to any good
To Share or not to Share? The Single Agent in a Team Decision Problem
This paper defines the "Single Agent in a Team Decision" (SATD) problem. SATD differs from prior multi-agent communication problems in the assumptions it makes about teammates' knowledge of each other's plans and possible observations. The paper proposes a novel integrated logical-decision-theoretic approach to solving SATD problems, called MDP-PRT. Evaluation of MDP-PRT shows that it outperforms a previously proposed communication mechanism that did not consider the timing of communication and compares favorably with a coordinated Dec-POMDP solution that uses knowledge about all possible observations.Engineering and Applied Science
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