Metadata-Based Semantic Query in Multilingual Databases

2018 ◽  
pp. 249-255 ◽  
Author(s):  
Ch. V. S. Satyamurty ◽  
J. V. R. Murthy ◽  
M. Raghava
Keyword(s):  
Semantic Query

scholarly journals A hybrid semantic query expansion approach for Arabic information retrieval

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hiba ALMarwi ◽  
Mossa Ghurab ◽  
Ibrahim Al-Baltah
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Semantic Query ◽  
Arabic Information Retrieval

Automatic Ontology Learning from Multiple Knowledge Sources of Text

2018 ◽  
Vol 14 (2) ◽  
pp. 1-21 ◽  
Author(s):  
B Sathiya ◽  
T.V. Geetha
Keyword(s):  
Knowledge Base ◽  
Web Pages ◽  
Knowledge Sources ◽  
Statistical Measure ◽  
Ontology Learning ◽  
Web Page ◽  
Semantic Query ◽  
Probabilistic Knowledge ◽  
Discovery Algorithms ◽  
Different Sources

The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.

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