Scientific Literature Search Engine
Optimization
Scientific and research literature is known for its highly complicated language, terms, and content. A person with zero prior knowledge about the content of these papers will be vexed attempting to decode the highly sophisticated ideas and technical jargon used in them. Advances in modern AI & Deep Learning systems have given us a chance to build an efficient search module and a NLP-based data extraction for interpreting and decoding scientific literature with ease & intelligence.
With these ideas in mind, we have built a powerful Artificial Intelligence based research tool that helps researchers find relevant papers without digging so much through irrelevant information.
India
Client
Research Enrichment Tool
OBJECTIVE
The main objective of the use case is to accelerate scientific breakthroughs by using AI to help scholars locate and understand research without pouring through highly complex and irrelevant information via an AI-based literature search functionality.
CLIENT
Our client is an India-based startup whose goal is to create a more open, collaborative, and democratized environment for research using Artificial Intelligence. With eight years of experience in their respective research fields, they envisioned their product “Co-pilot” which can accelerate scientific search breakthroughs by using AI to help scholars locate and understand research.
We were tasked by the clients to optimize their existing Scientific literature search engine process, improve the research understanding with AI enabled document understanding, and finally improve the relevancy of search responses. The relevancy of the search results are measured through the rate of successful click-throughs.
CHALLENGE
After foraying into the vast world of technical and scientific literature, we faced numerous challenges while developing our research tool.
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High reiteration frequency - One of the major problems faced by us is the reiteration of less contextual query results from the research paper. This could be highly debilitating for the both the researches and commoners alike.
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Documentation/Knowledge Complexity - The content and knowledge complexity of most of the research papers can be very hard to simplify and summarize.
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Search Ranking Configuration - Since our application is one of a kind; the ability to rank the searches for our tool is very limited at the outset.
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Building a coherent knowledge graph - For consistent and exact results, a proprietary knowledge graph must be built from scratch for the high-level query functions of our research tool.
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Lack of relevant results during search - When you have a plain text query and millions of academic papers, and your goal is to search and retrieve papers which contain fitting relevancy, context, number of citations & references, recentness of the papers, and query relevancy.
SOLUTION
After meticulous research & development we have developed two important modules that could foster the efficient and intelligent function of our research tool. Our in-house Scientific Literature Search Engine and its Data Acquisition Pipeline boasts accurate content retrieval and analysis for research papers of any complexity.
By leveraging an expansive knowledge graph for a natural-language processing based researcher query processing and expansion, we can boost the contextual and conceptual natures of these queries. These contextualized search queries are then fed through Elasticsearch to create a more coherent full-text search functionality. We have indexed over ~270 Million research papers of varying fields and disciplines to facilitate accurate search functionality. Based on the query, a set of top 100 papers which match the search query contextually are filtered. These are then funneled into a machine-learning (LightGBM) based reranker for more acceptability and click-through rates.
By engendering efficient and intensive functionalities like this within our Scientific Literature Search Engine retrieval and extraction of papers is less tedious and more accurate.
To create our expansive knowledge graph, we modeled an in-house Data Acquisition pipeline. By analyzing and parsing through multiple research paper sources and citations, we can acquire contextual data for our knowledge graph. The data is then formalized and structured into an XML document for use. The unified XML extract is then fed through an array of Natural Language Processing modules to extract important entities, build more expansive queries, embed the text within the documentations, and other intelligent functions. This builds a more systemic and all-purpose knowledge graph from which more accurate and contextual queries can be fed into our elasticsearch function.
RESULT
Transparency in science and technology is a must to help us understand the benefits and disadvantages of these progressive developments. Open-source information and a democratic process of research would help us in tackling misinformation, misguided scientific procedures, and negative societal impact.
Our Search Engine coupled with its Acquisition Pipeline can get us closer to simplifying these complex ideas for common folk and furnishing a culture of open research. It also helps the research to extract what they need in a logical and empirical manner; this quickens the passage to progress and development.