197 research outputs found
Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths
ATOMIC is a large-scale commonsense knowledge graph (CSKG) containing
everyday if-then knowledge triplets, i.e., {head event, relation, tail event}.
The one-hop annotation manner made ATOMIC a set of independent bipartite
graphs, which ignored the numerous links between events in different bipartite
graphs and consequently caused shortages in knowledge coverage and multi-hop
paths. In this work, we aim to construct Dense-ATOMIC with high knowledge
coverage and massive multi-hop paths. The events in ATOMIC are normalized to a
consistent pattern at first. We then propose a CSKG completion method called
Rel-CSKGC to predict the relation given the head event and the tail event of a
triplet, and train a CSKG completion model based on existing triplets in
ATOMIC. We finally utilize the model to complete the missing links in ATOMIC
and accordingly construct Dense-ATOMIC. Both automatic and human evaluation on
an annotated subgraph of ATOMIC demonstrate the advantage of Rel-CSKGC over
strong baselines. We further conduct extensive evaluations on Dense-ATOMIC in
terms of statistics, human evaluation, and simple downstream tasks, all proving
Dense-ATOMIC's advantages in Knowledge Coverage and Multi-hop Paths. Both the
source code of Rel-CSKGC and Dense-ATOMIC are publicly available on
https://github.com/NUSTM/Dense-ATOMIC.Comment: Accepted by ACL 2023 Main Conferenc
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation
Continuous-time dynamic graph modeling is a crucial task for many real-world
applications, such as financial risk management and fraud detection. Though
existing dynamic graph modeling methods have achieved satisfactory results,
they still suffer from three key limitations, hindering their scalability and
further applicability. i) Indiscriminate updating. For incoming edges, existing
methods would indiscriminately deal with them, which may lead to more time
consumption and unexpected noisy information. ii) Ineffective node-wise
long-term modeling. They heavily rely on recurrent neural networks (RNNs) as a
backbone, which has been demonstrated to be incapable of fully capturing
node-wise long-term dependencies in event sequences. iii) Neglect of
re-occurrence patterns. Dynamic graphs involve the repeated occurrence of
neighbors that indicates their importance, which is disappointedly neglected by
existing methods. In this paper, we present iLoRE, a novel dynamic graph
modeling method with instant node-wise Long-term modeling and Re-occurrence
preservation. To overcome the indiscriminate updating issue, we introduce the
Adaptive Short-term Updater module that will automatically discard the useless
or noisy edges, ensuring iLoRE's effectiveness and instant ability. We further
propose the Long-term Updater to realize more effective node-wise long-term
modeling, where we innovatively propose the Identity Attention mechanism to
empower a Transformer-based updater, bypassing the limited effectiveness of
typical RNN-dominated designs. Finally, the crucial re-occurrence patterns are
also encoded into a graph module for informative representation learning, which
will further improve the expressiveness of our method. Our experimental results
on real-world datasets demonstrate the effectiveness of our iLoRE for dynamic
graph modeling
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