Knowledge Graph Embedding (KGE) techniques are crucial in learning compact
representations of entities and relations within a knowledge graph,
facilitating efficient reasoning and knowledge discovery. While existing
methods typically focus either on training KGE models solely based on graph
structure or fine-tuning pre-trained language models with classification data
in KG, KG-FIT leverages LLM-guided refinement to construct a semantically
coherent hierarchical structure of entity clusters. By incorporating this
hierarchical knowledge along with textual information during the fine-tuning
process, KG-FIT effectively captures both global semantics from the LLM and
local semantics from the KG. Extensive experiments on the benchmark datasets
FB15K-237, YAGO3-10, and PrimeKG demonstrate the superiority of KG-FIT over
state-of-the-art pre-trained language model-based methods, achieving
improvements of 14.4%, 13.5%, and 11.9% in the Hits@10 metric for the link
prediction task, respectively. Furthermore, KG-FIT yields substantial
performance gains of 12.6%, 6.7%, and 17.7% compared to the structure-based
base models upon which it is built. These results highlight the effectiveness
of KG-FIT in incorporating open-world knowledge from LLMs to significantly
enhance the expressiveness and informativeness of KG embeddings.

Knowledge Graph Embedding techniques, such as KG-FIT, effectively incorporate hierarchical entity clusters and open-world knowledge from language models to enhance the expressiveness and informativeness of Knowledge Graph embeddings.

KG-FIT: 知识图谱细调基于开放世界知识

KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge

Automatically extracting key information from scientific documents has the
potential to help scientists work more efficiently and accelerate the pace of
scientific progress. Prior work has considered extracting document-level entity
clusters and relations end-to-end from raw scientific text, which can improve
literature search and help identify methods and materials for a given problem.
Despite the importance of this task, most existing works on scientific
information extraction (SciIE) consider extraction solely based on the content
of an individual paper, without considering the paper's place in the broader
literature. In contrast to prior work, we augment our text representations by
leveraging a complementary source of document context: the citation graph of
referential links between citing and cited papers. On a test set of
English-language scientific documents, we show that simple ways of utilizing
the structure and content of the citation graph can each lead to significant
gains in different scientific information extraction tasks. When these tasks
are combined, we observe a sizable improvement in end-to-end information
extraction over the state-of-the-art, suggesting the potential for future work
along this direction. We release software tools to facilitate citation-aware
SciIE development.

本文研究了如何利用引文图来提高科学信息提取的准确性，证明结合引文图与文本提取任务能提升科学信息提取的水平。

利用引文图谱进行科学信息提取

CitationIE: Leveraging the Citation Graph for Scientific Information  Extraction

There is compelling evidence that coreference prediction would benefit from
modeling global information about entity-clusters. Yet, state-of-the-art
performance can be achieved with systems treating each mention prediction
independently, which we attribute to the inherent difficulty of crafting
informative cluster-level features. We instead propose to use recurrent neural
networks (RNNs) to learn latent, global representations of entity clusters
directly from their mentions. We show that such representations are especially
useful for the prediction of pronominal mentions, and can be incorporated into
an end-to-end coreference system that outperforms the state of the art without
requiring any additional search.

使用循环神经网络直接学习实体群集的全局表示，尤其对于代词提及的预测特别有用，可以整合到端到端的指代系统中，不需要任何其他搜索，表现优于现有技术。