Enhancing search results with semantic annotation using augmented browsing

Authors:
Hong-Jie Dai;Wei-Chi Tsai;Richard Tzong-Han Tsai;Wen-Lian Hsu
Affiliations:
Department of Computer Science, National Tsing-Hua University and Intelligent Agent System Lab., Institute of Information Science, Academica Sinica, Taiwan, Republic of China;Department of Computer Science and Engineering, Yuan Ze University, Taiwan, Republic of China;Department of Computer Science and Engineering, Yuan Ze University, Taiwan, Republic of China;Department of Computer Science, National Tsing-Hua University and Intelligent Agent System Lab., Institute of Information Science, Academica Sinica, Taiwan, Republic of China
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Year:
2011

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Abstract

In this paper, we describe how we integrated an artificial intelligence (AI) system into the PubMed search website using augmented browsing technology. Our system dynamically enriches the PubMed search results displayed in a user's browser with semantic annotation provided by several natural language processing (NLP) subsystems, including a sentence splitter, a part-of-speech tagger, a named entity recognizer, a section categorizer and a gene normalizer (GN). After our system is installed, the PubMed search results page is modified on the fly to categorize sections and provide additional information on gene and gene products indentified by our NLP subsystems. In addition, GN involves three main steps: candidate ID matching, false positive filtering and disambiguation, which are highly dependent on each other. We propose a joint model using a Markov logic network (MLN) to model the dependencies found in GN. The experimental results show that our joint model outperforms a baseline system that executes the three steps separately. The developed system is available at https://sites.google.com/site/pubmedannotationtool 4ijcai/home.