51 research outputs found
Joint event extraction based on hierarchical event schemas from framenet
Event extraction is useful for many practical applications, such as news summarization and information retrieval. However, the popular automatic context extraction (ACE) event extraction program only defines very limited and coarse event schemas, which may not be suitable for practical applications. FrameNet is a linguistic corpus that defines complete semantic frames and frame-to-frame relations. As frames in FrameNet share highly similar structures with event schemas in ACE and many frames actually express events, we propose to redefine the event schemas based on FrameNet. Specifically, we extract frames expressing event information from FrameNet and leverage the frame-to-frame relations to build a hierarchy of event schemas that are more fine-grained and have much wider coverage than ACE. Based on the new event schemas, we propose a joint event extraction approach that leverages the hierarchical structure of event schemas and frame-to-frame relations in FrameNet. The extensive experiments have verified the advantages of our hierarchical event schemas and the effectiveness of our event extraction model. We further apply the results of our event extraction model on news summarization. The results show that the summarization approach based on our event extraction model achieves significant better performance than several state-of-the-art summarization approaches, which also demonstrates that the hierarchical event schemas and event extraction model are promising to be used in the practical applications
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
Undesired-Resonance Analysis and Modeling of Differential Signals Due to Narrow Ground Lines Without Stitching Vias
Undesired Resonances on High-Speed Differential Signals Are Studied in This Paper, which is Caused by the Adjacent Narrow Ground Line Without Stitching Vias. Due to Space Limitations in the High-Speed Channel Layouts of Certain Package Applications, the Ground (GND) Line is Often Narrow and Has Insufficient Stitching Vias, Potentially Causing Undesired Resonance in High-Speed Differential Signals. in This Study, These Undesired Resonances Were Investigated using 3D Simulations, revealing that They Can Be Modeled as Parallel-Coupled Half-Wavelength Resonance. the Resonance Frequency of the Parallel-Coupled Half-Wavelength Resonance Structure Can Be Predicted Well using the Formula based on the GND Line Length. Moreover, Three Potential Solutions to Undesired Resonance Are Proposed, Providing a Practical Guide for GND Line Routing in Specific Applications
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
Content-Structural Relation Inference in Knowledge Base
Relation inference between concepts in knowledge base has been extensively studied in recent years. Previous methods mostly apply the relations in the knowledge base, without fully utilizing the contents, i.e., the attributes of concepts in knowledge base. In this paper, we propose a content-structural relation inference method (CSRI) which integrates the content and structural information between concepts for relation inference. Experiments on data sets show that CSRI obtains 15% improvement compared with the state-of-the-art methods
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