Conventional Search Systems Research Articles

Information retrieval on oncology knowledge base using recursive paraphrase lattice

For annotation in cancer genomic medicine, oncologists have to refer to various knowledge bases worldwide and retrieve all information (e.g., drugs, clinical trials, and academic papers) related to a gene variant. However, oncologists find it difficult to search these knowledge bases comprehensively because there are multiple paraphrases containing abbreviations and foreign languages in their terminologies including diseases, drugs, and genes. In this paper, we propose a novel search method considering deep paraphrases, which helps oncologists retrieve essential annotation resources swiftly and effortlessly. Our method recursively finds paraphrases based on paraphrase corpora, expands a source document, and finally generates a paraphrase lattice. The proposed method also feedbacks beneficial information regarding the paraphrases applied for a search, which is useful for selecting search results and considering a query for the succeeding search. The results of an experiment demonstrated that our method could retrieve important annotation information that could not be retrieved using a conventional search system and simple paraphrasing. Additionally, annotation experts evaluated our method and found it to be practical.

Satellite Image Database Search Engine which Allows Fuzzy Expression of Geophysical Parameters of Queries

Satellite image database search engine which allows fuzzy expression of geophysical parameters of queries is proposed. A search engine based on knowledge based system which allows fuzzy expression of queries is proposed. A prototype system is created and tested. Whereas conventional search systems had to know in advance the functions of the search system, information about search keys, etc., the search engine proposed here guided the search conditions in a conversational form, by allowing ambiguous expressions (six adverb language hedges) at that time, the user is released from such annoyance. To make this possible, a membership function for each attribute information is defined, and a search condition refinement by fuzzy logic is introduced. The results show that the system accepts a fuzzy expression of query as well as a comprehensive dialogues between users and the system.

An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics

The purpose of this research work is to explore various limitations of conventional search and page ranking systems in an E-Commerce environment. The key objective is to assist customers in making an online purchase decision by providing personalized page ranking order of E-Commerce web links in response to E-Commerce query by analyzing the customer preferences and browsing behavior. This research work first employs an orderly and category wise literature review. The findings reveal that conventional search systems have not evolved to support big data analysis as required by modern E-Commerce environment. This work aims to develop and implement second-generation HDFS- MapReduce based innovative page ranking algorithm, i.e. Relevancy Vector (RV) algorithm. This research equips the customer with a robust metasearch tool, i.e. IMSS-AE to easily understand personalized search requirements and purchase preferences of customer. The proposed approach can well satisfy all critical parameters such as scalability, partial failure support, extensibility as expected from next-generation big data processing systems. An extensive and comprehensive experimental evaluation shows the efficiency and effectiveness of proposed RV page ranking algorithm and IMSS-AE tool over and above other popular search engines.

BOSS: context-enhanced search for biomedical objects.

BackgroundThere exist many academic search solutions and most of them can be put on either ends of spectrum: general-purpose search and domain-specific "deep" search systems. The general-purpose search systems, such as PubMed, offer flexible query interface, but churn out a list of matching documents that users have to go through the results in order to find the answers to their queries. On the other hand, the "deep" search systems, such as PPI Finder and iHOP, return the precompiled results in a structured way. Their results, however, are often found only within some predefined contexts. In order to alleviate these problems, we introduce a new search engine, BOSS, Biomedical Object Search System.MethodsUnlike the conventional search systems, BOSS indexes segments, rather than documents. A segment refers to a Maximal Coherent Semantic Unit (MCSU) such as phrase, clause or sentence that is semantically coherent in the given context (e.g., biomedical objects or their relations). For a user query, BOSS finds all matching segments, identifies the objects appearing in those segments, and aggregates the segments for each object. Finally, it returns the ranked list of the objects along with their matching segments.ResultsThe working prototype of BOSS is available at http://boss.korea.ac.kr. The current version of BOSS has indexed abstracts of more than 20 million articles published during last 16 years from 1996 to 2011 across all science disciplines.ConclusionBOSS fills the gap between either ends of the spectrum by allowing users to pose context-free queries and by returning a structured set of results. Furthermore, BOSS exhibits the characteristic of good scalability, just as with conventional document search engines, because it is designed to use a standard document-indexing model with minimal modifications. Considering the features, BOSS notches up the technological level of traditional solutions for search on biomedical information.