{'fraction_non_alphanumeric': 0.03144171779141104, 'fraction_numerical': 0.011503067484662576, 'mean_word_length': 5.098130841121495, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Comparison of Local Analysis, Global Analysis and Ontology-based Query Expansion Strategies for Bio-medical Literature Search

In this paper we present the design and evaluation of our biomedical literature searching approaches using the TREC 2004 ad hoc retrieval task in the genomics track. The main approach taken in our system is to expand queries by exploiting the three widely used strategies -local analysis, global analysis, and ontology-based term re-weighting across various search engines. The experimental results show that (1) ontology-based term re-weighting provides the best results among the three query expansion strategies, (2) expanding the initial query with more precise ontology-based term enhances LSI based local analysis substantially, and (3) including context to term re-weighting and LSI further improves the precision. Experimental results also show that the ontology-based term re-weighting with LUCENE or LEMUR search engines increases the average precision by up to 20.3% or 12.1%, respectively, compared to that of the baseline runs. In addition, the LSI-based local analysis increases the average precision by 9.2% with LEMUR search engine. We believe the approaches of the term re-weighting and LSI-based local analysis may be exploited in other bio-medical domains.