Paper Info
Title
Backward Chaining Ontology Reasoning Systems with Custom Rules.
Abstract
In the semantic web, content is tagged with \"meaning\" or \"semantics\" to facilitate machine processing and web searching. In general, question answering systems that are built on top of reasoning and inference face a number of difficult issues. In this paper, we analyze scalability issues faced by a question answering system used by a knowledge base with science information that has been harvested from the web. Using this system, we will be able to answer questions that contain qualitative descriptors such as \"groundbreaking\", \"top researcher\", and \"tenurable at university x\". This question answering system has been built using ontologies, reasoning systems and custom based rules for the reasoning system. Furthermore, we evaluated the performance of our optimized backward chaining engine on supporting custom rules and designed the experimental environment including scalable datasets, rule sets, query sets and metrics and compared the experimental results with other in-memory ontology reasoning systems. The results show that our developed backward chaining ontology reasoning system has better scalability than in-memory reasoning systems.
Year
DOI
Venue
2016
10.1145/2872518.2890521
WWW (Companion Volume)
Field
DocType
Citations 
Data mining,Forward chaining,Knowledge representation and reasoning,Semantic reasoner,Information retrieval,Computer science,Model-based reasoning,Backward chaining,Inference engine,Opportunistic reasoning,Reasoning system
Conference
0
PageRank 
References 
Authors
0.34
14
5
Name
Order
Citations
PageRank
Hui Shi141.64
Kurt Maly2567139.93
Dazhi Chong332.51
Gongjun Yan475543.39
Wu He543338.40