A Relation Aware Search Engine for Materials Science

Knowledge of material properties, microstructure, underlying material composition, and manufacturing process parameters that the material has undergone is of significant interest to materials scientists and engineers. A large amount of information of this nature is available in publications in the form of experimental measurements, simulation results, etc. However, getting to the right information of this kind that is relevant for a given problem on hand is a non-trivial task. First, an engineer has to go through a large collection of documents to select the right ones. Then, the engineer has to scan through these selected documents to extract relevant pieces of information. Our goal is to help automate some of these steps. Traditional search engines are not of much help here, as they are keyword centric and weak on relation processing. In this paper, we present a domain-specific search engine that processes relations to significantly improve search accuracy. The engine preprocesses material publication repositories to extract entities such as material compositions, material properties, manufacturing processes, process parameters, and their values and builds an index using these entities and values. The engine then uses this index to process user queries to retrieve relevant publication fragments. It provides a domain-specific query language with relational and logical operators to compose complex queries. We have conducted an experiment on a small library of publications on steel on which searches such as "get the list of publications which have carbon composition between 0.2 and 0.3 and on which tempering is carried out for about 30 to 40 min" are performed. We compare the results of our search engine with the results of a keyword-based search engine.