KWSSWS-IA Approach: An Intelligent Assistant for Scientific PDF Retrieval Using Semantic Analysis and AI-Driven Summarization

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

In the age of excessive information, traditional web search engines frequently fall short in providing accurate, contextually relevant results for scientific research. Furthermore, in addition to metadata-based retrieval, this study presents an enhanced version of the Keyword Search System on Web Searching (KWSSWS), which has evolved into an Intelligent Assistant (KWSSWS-IA) capable of deep semantic analysis of full-text PDF documents. The proposed system uses transformer-based artificial intelligence (AI) models related to natural language processing (NLP) to obtain and analyze internal document components, including title, abstract, keywords, and conclusions. It automatically generates brief, accurate summaries which is associated with ranks results according to scientific relevance rather than keyword frequency, in addition to recognizing significant scientific entities like authors, publication year, journal, and domain-specific terms KWSSWS-IA presents the following results on a single screen: download link, AI-generated summary, keyword match supplied by the user, publication year, title, and authors, and source journal. Therefore, ordinary search interfaces just offer hyperlinks. Experimental evaluation reveals significant improvements in precision, recall, and utilizer efficiency compared to standard Google Scholar searches. In addition, researchers who remain short on time and seeking high-quality, domain-relevant literature may find the strategy particularly useful.