# Conditional Knowledge Extraction Using Contextual Information Enhancement
## 3 Related Work
### Conditional Knowledge Graph  
In recent years, knowledge graphs have gradually become a core technology driving the development of artificial intelligence and play a crucial role in various applications such as question and answer [14] and information retrieval [15]. This paper observed the presence of conditional sentences in a certain percentage of sentences in a large amount of scientific text data. The construction of traditional knowledge graphs relies only on the plain text extracted from the text and lacks correspondence between the extracted information. As a result, important conditional information is lost, which limits the expressive power of knowledge graphs and potentially affects the exploration of downstream tasks, such as information retrieval. Conditional sentences are widely available in the scientific literature. Most existing knowledge graphs ignore the impact of conditionals. To address this problem, [PERSON] et al. [16] proposed a model for representing and constructing scientific conditional knowledge graphs, but it needs to handle the overlapping tuple problem. As far as we know, [PERSON] et al. [17] were the first to attempt to extract conditional information from text. They proposed a MIMO model to extract factual and conditional triplets but ignored several problems. MIMO has two modules: (1) a multi-input module that harnesses recent NLP development to process the text for input sequences from multiple tasks and feeds them into a multi-head encoder-decoder model with multi-input gates; (2) a multi-output module that generates multiple tuple tag sequences for fact and condition tuples, which consists of a relation name-tagging layer and a tuple-completion tagging layer. However, MIMO cannot clearly distinguish the inter-constraint relations between triplets and does not consider the logical relations between conditional and factual triplets. [PERSON] et al. [18] proposed a new conditional knowledge graph representation based on [PERSON]'s, which is in the form of nested hierarchical triplets, and also designed a text structure hierarchy analysis module to derive the hierarchical structure of conditional sentences. However, that article focuses on the task of extracting triplets, whereas this paper focuses on the task of matching between conditional phrases and triplets. Moreover, this paper is an innovation in pre-training and model task optimization, which differs from its innovation points. [PERSON]'s article does not show good results on complex sentences, for example: \"If symptomatic treatment fails, pharyngeal airway obstruction is possible, and tonsillectomy may be required.\" This article would make \"If symptomatic treatment fails\" a condition for \"pharyngeal airway obstruction is possible,\" and \"a tonsillectomy may be necessary\" incorrectly as a result of \"If symptomatic treatment fails\" and \"pharyngeal airway obstruction is possible\" in juxtagosition, thus losing the \"If symptomatic treatment fails\" as the \"a tonsillectomy may be necessary\" conditional relation.  
In summary, while these studies have progressed in the conditional knowledge graph, they lack a general framework for matching conditional phrases with relation triplets. Furthermore, certain domains have a high proportion of texts with conditional phrases, necessitating the creation of an efficient model to map these associations.