Open AccessSemantic index for keyword search over tagged data
Author(s) -
Ying Lou,
Feng Zhong,
Jinxiang Zhang,
Yubo Peng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/3/032041
Subject(s) - computer science , information retrieval , keyword search , semantics (computer science) , index (typography) , inverted index , semantic search , quality (philosophy) , keyword density , data mining , search engine , search engine indexing , world wide web , philosophy , epistemology , programming language
The index is crucial for information retrieval efficiency. Different with text data, tagged data contained rich semantics, which is useful to promote the quality of search results. It is observed that most existing indexes for keyword search do not consider semantics of tags. After an analysis of tagged data, we proposed the concept of result entity basing on the theory of relational database. We present a formula to quantify semantics of tags and then introduce a novel semantic index for keyword search. Experimental results demonstrated that our approach can help to reduce the size of the keyword inverted list in tagged document dramatically and improve the retrieval quality.