A Relation Aware Search Engine for Materials Science
A Relation Aware Search Engine for Materials Science
复制标题
材料科学的关系感知搜索引擎
DOI：
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发表时间：
2018
影响因子：
3.3
通讯作者：
S. Reddy
中科院分区：
文献类型：
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作者：
Sapan Shah;D. Vora;B. Gautham;S. Reddy
材料科学家和工程师对材料特性、微观结构、基础材料成分和材料经历的制造工艺参数非常感兴趣。出版物中以实验测量、模拟结果等形式提供了大量此类性质的信息。然而，获取与当前给定问题相关的此类正确信息并非易事。首先，工程师必须浏览大量文档才能选择正确的文档。然后，工程师必须扫描这些选定的文档以提取相关信息。我们的目标是帮助自动化其中一些步骤。传统的搜索引擎在这里没有太大帮助，因为它们以关键字为中心并且在关系处理方面较弱。在本文中，我们提出了一种特定领域的搜索引擎，可以处理关系以显着提高搜索准确性。该引擎对材料发布存储库进行预处理，以提取材料成分、材料特性、制造工艺、工艺参数及其值等实体，并使用这些实体和值构建索引。然后，引擎使用该索引来处理用户查询以检索相关的出版物片段。它提供了一种具有关系和逻辑运算符的特定于域的查询语言来组成复杂的查询。我们在一个小型的钢铁出版物库上进行了一项实验，在该图书馆中进行诸如“获取碳成分在0.2至0.3之间且回火时间约为30至40分钟的出版物列表”之类的搜索。我们将搜索引擎的结果与基于关键字的搜索引擎的结果进行比较。
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.