PIEKM : ML -based Procedural Information Extraction and Knowledge Management System for Materials Science Literature (2022)

Abstract: The published materials science literature contains abundant description information about synthesis procedures that can help discover new material areas, deepen the study of materials synthesis, and accelerate its automated planning. Nevertheless, this information is expressed in unstructured text, and manually processing and assimilating useful information is expensive and time-consuming for researchers. To address this challenge, we develop a Machine Learning-based procedural information extraction and knowledge management system (PIEKM) that extracts procedural information recipe steps, figures, and tables from materials science articles, and provides information retrieval capability and the statistics visualization functionality. Our system aims to help researchers to gain insights and quickly understand the connections among massive data. Moreover, we demonstrate that the machine learning-based system performs well in low-resource scenarios (i.e., limited annotated data) for domain adaption.

## introduction the procedural information in materials science literature aims to help researchers reproduce experiments and gain insights to speed up the process of new materials synthesis development @xcite . it takes the form of recipes (e.g., figure 4 ) and is normally defined as a series of actions and their corresponding conditions and results. such information contains imperatives, action verbs, steps of operations, and constructions @xcite . this information can be commonly found in method sections of materials science research literature. however, a great amount of scientific literature is published every year by the growing materials science research community. these well-established works provide a foundation to enlighten researchers and explore new materials development simultaneously. acquiring valuable information from the year-overyear increasing scientific literature efficiently and effectively remains one of the great challenges @xcite . the existing scholarly literature search engines, such as google scholar and semantic scholar, provide a good service to discover the relevant publications, but they cannot directly deliver the recipe steps of the experiments that are included in the literature. therefore, an intelligent system that provides the functions of procedural information searching and viewing, visualization, and analysis is highly demanded. information extraction (ie) which is a sub-area of natural language processing (nlp) provides an efficient way to automatically extract structured information from large unstructured text data. likewise, in the materials science domain, ie has been applied to similar tasks, such as experimental steps classification with unsupervised approaches of probabilistic methods for inorganic materials @xcite and named entity recognition in materials science domain @xcite . one of the biggest challenges of extracting information in materials science articles is that the annotated datasets are insufficient @xcite , which can be overcome by machine learning, particularly transfer learning, with pre-trained models obtained from other large training datasets @xcite . transfer learning can help with domain adaptation in materials science. we use transfer learning through finetuning a pre-trained language model with datasets in the materials science domain for chemical entity extraction. the trained model is integrated into the piekm system and performs well. this solves real cases where the number of training data in the materials science domain is very small for information extraction. this paper presents piekm, a prototype of a machine learning-based procedural information extraction and knowledge management system based on materials scientific literature. the goal of piekm is to demonstrate procedural information extraction and information retrieval capabilities. three crucial contributions of piekm are summarized as follows: ## system architecture in this section, we describe the detailed architecture of piekm system. the proposed system consists of three modules: (a) information processing, (b) user interface, and (c) query processing and information storage. figure 1 shows the architecture of piekm system. figure 2 shows the home page of piekm system. the details of each module are introduced as follows. (a) information processing module: this module processes the information from the digital scientific literature. we focus on portable document format (pdf) digital scientific literature in the piekm system. the input corpus of digital scientific literature has been segmented into text and non-text (figures, tables) parts (section 3.1). then the procedural information (section 3.2) and name entities (section 3.3) are extracted from these texts. the extracted figures and tables are stored in the corresponding folders. the rest of extracted text information is stored in as a semi-structured format in the database for quick query response. the piekm system is deployed by flask foot_0 framework and written in python. we use mongodb foot_1 to store the information data and respond to queries. the plotly foot_2 and dash foot_3 are used for interactive visualizations. ## information processing in this section, we present the implementation details of the information processing module in piekm system. this module serves as a pipeline including free-text extraction, procedural information extraction, and chemical entity recognition. the details of each stage are described as follows. ## query processing and information storage we use the model that is fine-tuned from section 3.3 to extract the key information from the title of the paper. the key information in our system could be considered into two types: material and morphology. for example, copper and gold are the type of material; nanocube and nanowire are the type of morphology. we integrate all this information and store it into the database as a query feature for users. ## demonstration the demonstration covers all of the features of piekm system. figure 2 shows the home page of piekm system. it visualizes the overview of the association between the number of articles within the database of nanomaterial composition and the corresponding morphology. the user can click the material name to see the number of relevant articles across different morphology. the relevant literature can be searched by material or morphology name, and the search result page will show a preview figure browser that offers all different options of material or morphology names to select and provides all figures included in the relevant articles (figure 3 ). in addition, the chemical entities, recipe, and the full content of extracted literature can be displayed after clicking the title on the top of each figure on the browser (figure 4 ). 6 conclusion this work presents a machine learning-based procedural information extraction and knowledge management system, namely piekm, for the materials science domain. piekm system integrates multiple functionalities, such as procedural information