Open AccessSchema and content aware classification for predicting the sources containing an answer overcorpusand knowledge graphs
Author(s) -
Somayeh Asadifar,
Mohsen Kahani,
Saeedeh Shekarpour
Publication year - 2022
Publication title -
peerj. computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.846
Subject(s) - computer science , scalability , schema (genetic algorithms) , heuristics , selection (genetic algorithm) , information retrieval , data source , multi source , source code , metadata , source text , open source , question answering , prioritization , natural language processing , artificial intelligence , data mining , database , world wide web , programming language , statistics , mathematics , software , operating system , management science , economics
Today, several attempts to manage question answering (QA) have been made in three separate areas: (1) knowledge-based (KB), (2) text-based and (3) hybrid, which takes advantage of both prior areas in extracting the response. On the other hand, in question answering on a large number of sources, source prediction to ensure scalability is very important. In this paper, a method for source prediction is presented in hybrid QA, involving several KB sources and a text source. In a few hybrid methods for source selection, including only one KB source in addition to the textual source, prioritization or heuristics have been used that have not been evaluated so far. Most methods available in source selection services are based on general metadata or triple instances. These methods are not suitable due to the unstructured source in hybrid QA. In this research, we need data details to predict the source. In addition, unlike KB federated methods that are based on triple instances, we use the behind idea of mediated schema to ensure data integration and scalability. Results from evaluations that consider word, triple, and question level information, show that the proposed approach performs well against a few benchmarks. In addition, the comparison of the proposed method with the existing approaches in hybrid and KB source prediction and also QA tasks has shown a significant reduction in response time and increased accuracy.