Constrained Tuple Extraction with Interaction-Aware Network (2023)

Abstract: Tuples extraction is a fundamental task for information extraction and knowledge graph construction. The extracted tuples are usually represented as knowledge triples consisting of subject, relation, and object. In practice, however, the validity of knowledge triples is associated with and changes with the spatial, temporal, or other kinds of constraints. Motivated by this observation, this paper proposes a constrained tuple extraction (CTE) task to guarantee the validity of knowledge tuples. Formally, the CTE task is to extract constrained tuples from unstructured text, which adds constraints to conventional triples. To this end, we propose an interaction-aware network. Combinatorial interactions among context-specific external features and distinct-granularity internal features are exploited to effectively mine the potential constraints. Moreover, we have built a new dataset containing totally 1,748,826 constrained tuples for training and 3656 ones for evaluation. Experiments on our dataset and the public CaRB dataset demonstrate the superiority of the proposed model. The constructed dataset and the codes are publicly available.

## introduction tuples extraction task aims to extract knowledge tuples from unstructured texts, which is a fundamental task for information extraction, knowledge graph construction, and so on @xcite @xcite . the extracted knowledge tuples are mainly represented in the form of (subject, relation, object) @xcite , and are usually acquired through named entity recognition (ner), relation extraction (re) @xcite @xcite , and open information extraction (open ie) @xcite . with the rise of large-scale pretraining methods (e.g. bert @xcite ), the quality of knowledge triples has improved significantly. however, current knowledge triples lack constraints for their authenticity. in practice, constraints are ubiquitous in numerous domains, such as spatial, temporal, conditional and environmental ones. constraints are essential supplements to knowledge triples, and play an instructional role. our deep investigation on literature indicates that this topic has not gained enough attention. some works utilize temporal knowledge tuples to reflect temporal dynamics @xcite . however, only temporal constraints are not sufficient and general enough to guarantee the validity of knowledge triples. for instance, let us consider the following sentence: the constrained tuples are extracted via open ie approaches, similar to the works in @xcite ; @xcite . this extraction approach is more general and doesn't require predefined entity and relation types. ## related work the goal of the tuples extraction task is to extract knowledge tuples from unstructured texts @xcite @xcite . the extracted knowledge tuples are mainly acquired through relational triple extraction @xcite @xcite and open information extraction @xcite .