57 research outputs found
Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction
Temporal relation prediction in incomplete temporal knowledge graphs (TKGs)
is a popular temporal knowledge graph completion (TKGC) problem in both
transductive and inductive settings. Traditional embedding-based TKGC models
(TKGE) rely on structured connections and can only handle a fixed set of
entities, i.e., the transductive setting. In the inductive setting where test
TKGs contain emerging entities, the latest methods are based on symbolic rules
or pre-trained language models (PLMs). However, they suffer from being
inflexible and not time-specific, respectively. In this work, we extend the
fully-inductive setting, where entities in the training and test sets are
totally disjoint, into TKGs and take a further step towards a more flexible and
time-sensitive temporal relation prediction approach SST-BERT, incorporating
Structured Sentences with Time-enhanced BERT. Our model can obtain the entity
history and implicitly learn rules in the semantic space by encoding structured
sentences, solving the problem of inflexibility. We propose to use a time
masking MLM task to pre-train BERT in a corpus rich in temporal tokens
specially generated for TKGs, enhancing the time sensitivity of SST-BERT. To
compute the probability of occurrence of a target quadruple, we aggregate all
its structured sentences from both temporal and semantic perspectives into a
score. Experiments on the transductive datasets and newly generated
fully-inductive benchmarks show that SST-BERT successfully improves over
state-of-the-art baselines
Multiobjective Optimization of PID Controller of PMSM
PID controller is used in most of the current-speed closed-loop control of permanent magnet synchronous motors (PMSM) servo system. However, Kp, Ki, and Kd of PID are difficult to tune due to the multiple objectives. In order to obtain the optimal PID parameters, we adopt a NSGA-II to optimize the PID parameters in this paper. According to the practical requirement, several objective functions are defined. NSGA-II can search the optimal parameters according to the objective functions with better robustness. This approach provides a more theoretical basis for the optimization of PID parameters than the aggregation function method. The simulation results indicate that the system is valid, and the NSGA-II can obtain the Pareto front of PID parameters
Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method
The development of knowledge graph (KG) applications has led to a rising need
for entity alignment (EA) between heterogeneous KGs that are extracted from
various sources. Recently, graph neural networks (GNNs) have been widely
adopted in EA tasks due to GNNs' impressive ability to capture structure
information. However, we have observed that the oversimplified settings of the
existing common EA datasets are distant from real-world scenarios, which
obstructs a full understanding of the advancements achieved by recent methods.
This phenomenon makes us ponder: Do existing GNN-based EA methods really make
great progress?
In this paper, to study the performance of EA methods in realistic settings,
we focus on the alignment of highly heterogeneous KGs (HHKGs) (e.g., event KGs
and general KGs) which are different with regard to the scale and structure,
and share fewer overlapping entities. First, we sweep the unreasonable
settings, and propose two new HHKG datasets that closely mimic real-world EA
scenarios. Then, based on the proposed datasets, we conduct extensive
experiments to evaluate previous representative EA methods, and reveal
interesting findings about the progress of GNN-based EA methods. We find that
the structural information becomes difficult to exploit but still valuable in
aligning HHKGs. This phenomenon leads to inferior performance of existing EA
methods, especially GNN-based methods. Our findings shed light on the potential
problems resulting from an impulsive application of GNN-based methods as a
panacea for all EA datasets. Finally, we introduce a simple but effective
method: Simple-HHEA, which comprehensively utilizes entity name, structure, and
temporal information. Experiment results show Simple-HHEA outperforms previous
models on HHKG datasets.Comment: 11 pages, 6 figure
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified
knowledge. They are widely used in various intelligent applications. In this
article, we provide a comprehensive survey on the evolution of various types of
knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs)
and techniques for knowledge extraction and reasoning. Furthermore, we
introduce the practical applications of different types of KGs, including a
case study in financial analysis. Finally, we propose our perspective on the
future directions of knowledge engineering, including the potential of
combining the power of knowledge graphs and large language models (LLMs), and
the evolution of knowledge extraction, reasoning, and representation
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