470 research outputs found
Contrastive Triple Extraction with Generative Transformer
Triple extraction is an essential task in information extraction for natural
language processing and knowledge graph construction. In this paper, we revisit
the end-to-end triple extraction task for sequence generation. Since generative
triple extraction may struggle to capture long-term dependencies and generate
unfaithful triples, we introduce a novel model, contrastive triple extraction
with a generative transformer. Specifically, we introduce a single shared
transformer module for encoder-decoder-based generation. To generate faithful
results, we propose a novel triplet contrastive training object. Moreover, we
introduce two mechanisms to further improve model performance (i.e., batch-wise
dynamic attention-masking and triple-wise calibration). Experimental results on
three datasets (i.e., NYT, WebNLG, and MIE) show that our approach achieves
better performance than that of baselines.Comment: Accepted by AAAI 202
A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents
The relation triples extraction method based on table filling can address the
issues of relation overlap and bias propagation. However, most of them only
establish separate table features for each relationship, which ignores the
implicit relationship between different entity pairs and different relationship
features. Therefore, a feature reasoning relational triple extraction method
based on table filling for technological patents is proposed to explore the
integration of entity recognition and entity relationship, and to extract
entity relationship triples from multi-source scientific and technological
patents data. Compared with the previous methods, the method we proposed for
relational triple extraction has the following advantages: 1) The table filling
method that saves more running space enhances the speed and efficiency of the
model. 2) Based on the features of existing token pairs and table relations,
reasoning the implicit relationship features, and improve the accuracy of
triple extraction. On five benchmark datasets, we evaluated the model we
suggested. The result suggest that our model is advanced and effective, and it
performed well on most of these datasets
CodeKGC: Code Language Model for Generative Knowledge Graph Construction
Current generative knowledge graph construction approaches usually fail to
capture structural knowledge by simply flattening natural language into
serialized texts or a specification language. However, large generative
language model trained on structured data such as code has demonstrated
impressive capability in understanding natural language for structural
prediction and reasoning tasks. Intuitively, we address the task of generative
knowledge graph construction with code language model: given a code-format
natural language input, the target is to generate triples which can be
represented as code completion tasks. Specifically, we develop schema-aware
prompts that effectively utilize the semantic structure within the knowledge
graph. As code inherently possesses structure, such as class and function
definitions, it serves as a useful model for prior semantic structural
knowledge. Furthermore, we employ a rationale-enhanced generation method to
boost the performance. Rationales provide intermediate steps, thereby improving
knowledge extraction abilities. Experimental results indicate that the proposed
approach can obtain better performance on benchmark datasets compared with
baselines. Code and datasets are available in
https://github.com/zjunlp/DeepKE/tree/main/example/llm.Comment: Work in progres
Mutually Guided Few-shot Learning for Relational Triple Extraction
Knowledge graphs (KGs), containing many entity-relation-entity triples,
provide rich information for downstream applications. Although extracting
triples from unstructured texts has been widely explored, most of them require
a large number of labeled instances. The performance will drop dramatically
when only few labeled data are available. To tackle this problem, we propose
the Mutually Guided Few-shot learning framework for Relational Triple
Extraction (MG-FTE). Specifically, our method consists of an entity-guided
relation proto-decoder to classify the relations firstly and a relation-guided
entity proto-decoder to extract entities based on the classified relations. To
draw the connection between entity and relation, we design a proto-level fusion
module to boost the performance of both entity extraction and relation
classification. Moreover, a new cross-domain few-shot triple extraction task is
introduced. Extensive experiments show that our method outperforms many
state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single-domain) and
20.5 F1 score on FewRel 2.0 (cross-domain).Comment: Accepted by ICASSP 202
Extracting Relational Triples Based on Graph Recursive Neural Network via Dynamic Feedback Forest Algorithm
Extracting relational triples (subject, predicate, object) from text enables
the transformation of unstructured text data into structured knowledge. The
named entity recognition (NER) and the relation extraction (RE) are two
foundational subtasks in this knowledge generation pipeline. The integration of
subtasks poses a considerable challenge due to their disparate nature. This
paper presents a novel approach that converts the triple extraction task into a
graph labeling problem, capitalizing on the structural information of
dependency parsing and graph recursive neural networks (GRNNs). To integrate
subtasks, this paper proposes a dynamic feedback forest algorithm that connects
the representations of subtasks by inference operations during model training.
Experimental results demonstrate the effectiveness of the proposed method
INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion
Graph-to-text (G2T) generation and text-to-graph (T2G) triple extraction are
two essential tasks for constructing and applying knowledge graphs. Existing
unsupervised approaches turn out to be suitable candidates for jointly learning
the two tasks due to their avoidance of using graph-text parallel data.
However, they are composed of multiple modules and still require both entity
information and relation type in the training process. To this end, we propose
INFINITY, a simple yet effective unsupervised approach that does not require
external annotation tools or additional parallel information. It achieves fully
unsupervised graph-text mutual conversion for the first time. Specifically,
INFINITY treats both G2T and T2G as a bidirectional sequence generation task by
fine-tuning only one pretrained seq2seq model. A novel back-translation-based
framework is then designed to automatically generate continuous synthetic
parallel data. To obtain reasonable graph sequences with structural information
from source texts, INFINITY employs reward-based training loss by leveraging
the advantage of reward augmented maximum likelihood. As a fully unsupervised
framework, INFINITY is empirically verified to outperform state-of-the-art
baselines for G2T and T2G tasks
Generative Knowledge Graph Construction: A Review
Generative Knowledge Graph Construction (KGC) refers to those methods that
leverage the sequence-to-sequence framework for building knowledge graphs,
which is flexible and can be adapted to widespread tasks. In this study, we
summarize the recent compelling progress in generative knowledge graph
construction. We present the advantages and weaknesses of each paradigm in
terms of different generation targets and provide theoretical insight and
empirical analysis. Based on the review, we suggest promising research
directions for the future. Our contributions are threefold: (1) We present a
detailed, complete taxonomy for the generative KGC methods; (2) We provide a
theoretical and empirical analysis of the generative KGC methods; (3) We
propose several research directions that can be developed in the future.Comment: Accepted to EMNLP 2022 (oral) and a public repository is available in
https://github.com/zjunlp/Generative_KG_Construction_Paper
- …