Constructing Procedural Graphs with Multiple Dependency Relations: A New Dataset and Baseline (2023)

Abstract: Current structured and semi-structured knowledge bases mainly focus on representing descriptive knowledge but ignore another commonsense knowledge (Procedural Knowledge). To structure the procedural knowledge, existing methods are proposed to automatically generate flow graphs from procedural documents. They focus on extracting sequential dependency between sentences but neglect another two important dependencies (i.e., inclusion dependency and constraint dependency) in procedural documents. In our paper, we explore a problem of automatically generating procedural graph with multiple dependency relations to extend the flow graph constructed by existing methods and propose a procedural graph construction method with syntactic information and discourse structures. A new dataset (WHPG) is built and extensive experiments are conducted to evaluate the effectiveness of our proposed model.

## introduction many well-known structured knowledge bases (e.g., wikidata 1 ) and semi-structured knowledge bases (e.g., wikipedia 2 ) have been built and assist many applications to achieve remarkable performance, such as the question-answering (qa) @xcite , information retrieval @xcite ) and recommendation systems @xcite . they focus on representing the descriptive knowledge i.e. the knowledge of attributes or features of things @xcite , but lack another kind of commonsense knowledge-procedural knowledge. specifically, the knowledge which is in the form of procedures or sequences of actions to achieve particular goals is called as procedural knowledge, such as cooking recipes and maintenance manuals. generally, most procedural knowledge is expressed in unstructured texts (e.g., websites or books of cooking recipes). to extract the structured procedural knowledge, existing methods @xcite @xcite are designed to transform the unstructured procedural documents into flow graphs (or workflows) which can effectively present the main operations and their ordering relations expressed in procedural documents. however, they only focus on extracting the sequential dependency (i.e., the dependency relation "next" in figure 1 ) between steps (operational sentences) in a procedural document, which is insufficient in real-world scenarios. as shown in figure 1 , sentences s2 and s3 are the sub-actions of sentence s1, which provide more fine-grained operational statements to finish operation s1. there is another kind of dependencyinclusion dependency between sentences s1 and s2 (or between s1 and s3 ). nevertheless, the flow graphs constructed by current methods @xcite ignore the inclusion dependencies among sentences and wrongly connect sentences s1 and s2 as a "next" relation, as shown in figure 1 . furthermore, declarative (or descriptive) sentences commonly appear in real-world procedural documents, which state the constraints (e.g, reasons, conditions and effects) of doing things. current researches have shown that declarative sentences in procedural documents can provide important clues for the procedural semantic understanding and reasoning @xcite in many downstream tasks such as operation diagnosis @xcite and technical maintenance @xcite . however, current knowledge structure methods @xcite simply transform the declarative sentences into an information flow in a flow graph (e.g., @xmath0, which neglects the constraint dependency between operational and declarative sentences. as shown in figure 1 , the declarative sentences s7 and s8 respectively describe the effect constraint and condition constraint for the execution of operational sentence s6. based on the above motivations, we explore a problem of automatically constructing a procedural graph with multiple dependency relations between sentences in a procedural document. according to our observation, the syntactic structures of sentences can provide obvious features for identifying sentence types and then assist to detect the dependency relations between sentences. as shown in figure 2 , the syntactic pattern "verb(vb) obj -→noun(nn)" is a strong indicator for classifying sentences s2 and s3 into operation types. meanwhile, the sentence type prediction can further benefit the dependency relation detection between sentences. for example, the constraint dependency cannot exist between two sentences with an operation type. moreover, inspired by researches in discourse parsing @xcite , we observe that the contextual dependency structures (which is called as dis-course structures) can provide features to recognize the dependency relations between sentences. as shown in figure 1 , the dependency relation between s1 and s3 can be inferred as sub-action according to their contextual dependency structure in our paper, we design a procedural graph construction method to detect the multiple dependency relations between sentences in procedural documents by utilizing syntactic information and discourse structures. specifically, a gcn-based syntactic structure encoder with multi-query attention is proposed to capture the syntactic features of sentences and improve the ability to distinguish between operational and declarative sentences. moreover, a structure-aware edge encoder is designed to assist the inference of dependencies between sentences by infusing the contextual structure features in procedural documents. furthermore, due to the lack of dependencies between sentences in existing procedural text datasets, a new dataset whpg is built based on the wikihow foot_0 database. to summarize, the main contributions of this paper are listed as follows: extensive experiments are conducted on two public datasets from different domains and our created dataset to evaluate the effectiveness of our model in automatically generating the procedural graph. ## related work many prominent knowledge bases such as wiki-data @xcite , wikipedia @xcite and freebase @xcite mainly focus on representing descriptive knowledge (i.e., the knowledge of attributes or features of things @xcite ). but they do not sufficiently cover procedural knowledge (i.e., the knowledge of procedures or sequences of actions for achieving the particular goals (georgeff and lansky, 1986)). recently, to obtain the structured procedural knowledge, two categories of methods (i.e., entitylevel and sentence-level based methods) are proposed. specifically, the entity-level based methods @xcite @xcite @xcite @xcite aim to extract the predefined entities and their relations from unstructured procedural texts (e.g., cooking recipes). however, they require large-scale fine-grained annotated data for each domain and lack domain generalization ability. to alleviate these issues, sentence-level based methods @xcite are designed, aiming to construct the flow graphs at sentence-level for procedural documents. however, they only focus on extracting the action flows with sequential dependencies, which is limited in real-world scenarios. in practice, both the inclusion dependency and constraint dependency are common in procedural texts and benefit the procedural text understanding and reasoning @xcite . thus, our paper explores a problem of automatically generating a procedural graph with dependency relations from a procedural document. up to the present, several public entity-level procedural text datasets @xcite @xcite and a sentence-level dataset ctfw (which is not publicly available due to ethical considerations) @xcite are built. nevertheless, existing public datasets do not annotate the dependency relations between sentences in a procedural document. thus, a new dataset whpg based on the wikihow knowledge base is built and will be publicly available for evaluation in future research. ## experiment we firstly introduce the construction of the new dataset whpg and then analyze the experimental results in detail. ## conclusion in this paper, we explore a problem of automatically generating procedural graphs with multiple dependency relations for procedural documents. existing procedural knowledge structured methods mainly focus on constructing action flows with the sequential dependency from procedural texts but neglect another two important dependencies: inclusion dependency and constraint dependency which are helpful for the procedural text understanding and reasoning. to solve this problem, we build a new procedural text dataset with multiple dependency relations and propose a procedural graph construction method by utilizing syntactic and discourse structure features. extensive experiments are conducted and evaluate the effectiveness of our proposed model.