This article describes the algorithms implemented in the Essie search engine that is currently serving several Web sites at the National Library of Medicine. Essie is a phrase-based search engine with term and concept query expansion and probabilistic relevancy ranking. Essie's design is motivated by an observation that query terms are often conceptually related to terms in a document, without ...

Swoogle is a system that helps knowledge engineers and software agents find knowledge on the web encoded in the semantic web languages RDF and OWL. Based on the search mechanisms provided in the previous version, we propose a novel semantic web navigation model and refine mechanisms for ranking the semantic web at various granularities. Although the semantic web is materialized on the Web, it i...

In this paper, we describe our approach for information retrieval for question answering (IR4QA) of NTCIR-8 tasks. For improving information retrieval performance, we focus mostly on the document re-ranking technique, which locates between the first retrieval documents and query expansion. In this paper, we employ two approaches in document re-ranking. One is based on entropy clustering, a kind...

In this research, we evaluated performance analysis and ranking of insurance companies listed on the capital market of Iran using data envelopment analysis. In respect of the target the research is applied. The type of research design relying on historical data, is event following and inductive method is inference. The study is consisted of a key question and three sub-questions. This research ...

In this paper, we address the issue of answering PICO (Patient/Problem, Intervention, Comparison, Outcome) clinical queries. The contributions of this work include (1) a new document ranking model based on a prioritized aggregation operator that computes the global relevance score based on the relevance estimation of the semantic facet sub-queries and (2) leverages the importance of the facets ...

The overload of the information available on the web, held with the diversity of the user information needs and the ambiguity of their queries have led the researchers to develop personalized search tools that return only documents that meet the user profile representing his main interests and needs. We present in this paper a personalized document ranking model based on an extended graph-based...

This paper describes a novel approach of improving multi-document summarization based on cross-document information extraction (IE). We describe a method to automatically incorporate IE results into sentence ranking. Experiments have shown our integration methods can significantly improve a high-performing multi-document summarization system, according to the ROUGE-2 and ROUGE-SU4 metrics (7.38...

This paper describes a method for language independent extractive summarization that relies on iterative graph-based ranking algorithms. Through evaluations performed on a single-document summarization task for English and Portuguese, we show that the method performs equally well regardless of the language. Moreover, we show how a metasummarizer relying on a layered application of techniques fo...

Search engines are among the most important applications or services on the web. Most existing successful search engines use global ranking algorithms to generate the ranking of documents crawled in their databases. However, global ranking of documents has two potential problems: high computation cost and potentially poor rankings. Both of the problems are related to the centralized computation...

Web searchers typically fail to view search results beyond the first page nor fully examine those results presented to them. In this article we describe an approach that encourages a deeper examination of the contents of the document set retrieved in response to a searcher’s query. The approach shifts the focus of perusal and interaction away from potentially uninformative document surrogates (...