Abstract
Search engine based on knowledge graphs has become increasingly popular and demanding. One of the most popular aspects is being relations between entities. What is needed is a smarter and apt enterprise search which helps to provide all the right answers using knowledge graph and NLP. Fact-finding signifies that apart from getting a document returned by search engine, with the in-place knowledge graph, it helps in finding facts on user-specific query when it finds a match in the knowledge graph. In this paper, we present an approach using scalable, open-source approach which takes unstructured data and helps in creating a search platform that refines large text for search and a knowledge graph with question–answer system. It helps in getting related document(s) based on the searched query. The complete application can be used and integrated into many use-cases since search is the integral part of most (almost all) applications.
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Sharma, S. (2022). Fact-Finding Knowledge-Aware Search Engine. In: Sharma, N., Chakrabarti, A., Balas, V.E., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. Lecture Notes on Data Engineering and Communications Technologies, vol 71. Springer, Singapore. https://doi.org/10.1007/978-981-16-2937-2_17
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DOI: https://doi.org/10.1007/978-981-16-2937-2_17
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