4 research outputs found
Wiki-MetaSemantik: A Wikipedia-derived Query Expansion Approach based on Network Properties
This paper discusses the use of Wikipedia for building semantic ontologies to
do Query Expansion (QE) in order to improve the search results of search
engines. In this technique, selecting related Wikipedia concepts becomes
important. We propose the use of network properties (degree, closeness, and
pageRank) to build an ontology graph of user query concepts which is derived
directly from Wikipedia structures. The resulting expansion system is called
Wiki-MetaSemantik. We tested this system against other online thesauruses and
ontology based QE in both individual and meta-search engines setups. Despite
that our system has to build a Wikipedia ontology graph in order to do its
work, the technique turns out to work very fast (1:281) compared to another
ontology QE baseline (Wikipedia Persian ontology QE). It has thus the potential
to be utilized online. Furthermore, it shows significant improvement in
accuracy. Wiki-MetaSemantik also shows better performance in a meta-search
engine (MSE) set up rather than in an individual search engine set up
Spatial and temporal-based query disambiguation for improving web search
Queries submitted to search engines are ambiguous in nature due to users’ irrelevant input which poses real challenges to web search engines both towards understanding a query and giving results. A lot of irrelevant and ambiguous information creates disappointment among users. Thus, this research proposes an ambiguity evolvement process followed by an integrated use of spatial and temporal features to alleviate the search results imprecision. To enhance the effectiveness of web information retrieval the study develops an enhanced Adaptive Disambiguation Approach for web search queries to overcome the problems caused by ambiguous queries. A query classification method was used to filter search results to overcome the imprecision. An algorithm was utilized for finding the similarity of the search results based on spatial and temporal features. Users’ selection based on web results facilitated recording of implicit feedback which was then utilized for web search improvement. Performance evaluation was conducted on data sets GISQC_DS, AMBIENT and MORESQUE comprising of ambiguous queries to certify the effectiveness of the proposed approach in comparison to a well-known temporal evaluation and two-box search methods. The implemented prototype is focused on ambiguous queries to be classified by spatial or temporal features. Spatial queries focus on targeting the location information whereas temporal queries target time in years. In conclusion, the study used search results in the context of Spatial Information Retrieval (S-IR) along with temporal information. Experiments results show that the use of spatial and temporal features in combination can significantly improve the performance in terms of precision (92%), accuracy (93%), recall (95%), and f-measure (93%). Moreover, the use of implicit feedback has a significant impact on the search results which has been demonstrated through experimental evaluation.SHAHID KAMA