Semantic Based Query Expansion Research Articles

Arabic text semantic-based query expansion

Arabic text semantic-based query expansion

Arabic text semantic-based query expansion

Arabic text semantic-based query expansion

Semantic approaches for query expansion

To increase the Information Retrieval System’s efficiency, there is a requirement to expand the native user query. There are many approaches to enhance the user query in which the primary method consists of semantic-based query expansion (QE). In a semantic-based QE approach, relevant documents are retrieved by considering all the similar terms of a given user query. The semantic-based QE approach helps in dealing with the limitation of low recall and low precision value of the Information retrieval system and deals with ambiguity and vagueness. Query Expansion techniques contain the semantic concepts that are relevant to semantic computing, computational intelligence, and information retrieval area. Computational intelligence technique is required during automatic query expansion for advanced information processing. This paper presents a comprehensive survey of the methods proposed by several researchers considering semantic-based query expansion. It also discusses the merits and demerits of each technique in a detailed manner. This paper presents the view of several semantic query expansion core techniques. This paper also discusses the various keyholes present in today’s era.

Semantic Query Expansion based on Entity Association in Medical Question Answering

Query expansion technology is a common method to solve the semantic deviation problem between questions and answers in the question answering system. In medical question answering field, the current semantic-based query expansion method has the following two shortcomings. (1) In the stage of obtaining candidate extended terms, only the medical entity’s concept in the query is used, but the medical entity’s association between questions and answers is ignored (2) During the extended terms filtering stage, the interference of negative medical entities to the mutual information is ignored. Given the above-mentioned deficiencies, this paper proposes a semantic query expansion method based on entity association in medical question answering. This method first combines the query intent with the inference association between medical entities to obtain candidate extended terms from the medical knowledge graph. Then filter candidate extended terms using the selection method based on negative medical entity recognition and mutual information and thus get the final extended terms according to the co-occurrence association between medical entities. Experimental results show that our proposed method outperforms the query expansion method based on the thesaurus which commonly used in the current medical question and answering system.

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion.

Computing Term-Concept Association in Semantic-Based Query Expansion

PDF HTML阅读 XML下载 导出引用 引用提醒 基于查询采样的高维数据混合索引 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Natural Science Foundation of China under Grant No.60403018 (国家自然科学基金); the National Basic Research Program of China under Grant No.2005CB321905 (国家重点基础研究发展计划(973)); the Natural Science Foundation of Shanghai of China under Grant No.04ZR14011 (上海市自然科学基金); the College Cooperation Plan of AMD (AMD大学合作计划) High Dimensional Hybrid Index Based on Query Sampling Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为了改进高维数据库查询的效率,通常需要根据数据分布来选择合适的索引策略.然而,经典的分布模型难以解决实际应用中图像、视频等高维数据复杂的分布估计问题.提出一种基于查询采样进行数据分布估计的方法,并在此基础上提出了一种支持最近邻查询的混合索引,即针对多媒体数据分布的不均匀性,自适应地对不同分布的数据使用不同的索引结构,建立统一的索引结构.为了实现混合索引,采用构造性方法:首先通过聚类分解分割数据并建立树状索引;然后使用查询采样算法,对数据实际分布进行估计;最后根据数据分布的特性,把稀疏数据从树状索引中剪裁出来,进行基于顺序扫描策略的索引,而分布比较密集的数据仍然保留在树状索引中.在4个真实的图像数据集上进行了充分的实验,结果显示,该索引方法明显优于iDistance,M-Tree等度量空间索引,在维数达到336时,查询效率仍高于顺序扫描.实验结果显示,该查询采样算法在采样数据量仅为(N为数据量)的情况下即可获得满足索引需要的分布估计结果. Abstract:In order to improve the query answering of high-dimensional database, data distribution is necessary to select appropriate indexing strategy. However, traditional data distribution models can not estimate the accurate data distribution in the complex real multimedia data of image and video. This paper presents a method to estimate the accurate data distribution based on query sampling, and proposes a novel hybrid index to speed up processing of high-dimensional K-nearest neighbor (KNN) queries. The proposed hybrid index improves the query efficiency by adaptively selecting different index strategies for the data with different distribution. In the first step, the cluster analysis and cluster splitting methods are applied to construct a tree-based index, and then the relationship between data distribution and index performance is derived by sampling. At last some tree branches with sparse data are extracted for linear scan, while the aggregate data remains in the tree. Extensive experiments on four real image data sets show that the proposed hybrid index structure performs better than iDistance, M-Tree and linear scan, and scales better with dimensions. The index is still faster than linear scan when the dimension reaches 336. The experiments also show that the proposed query sampling algorithm can obtain the accurate data distribution when the amount of sampling is below (N is the size of data set). 参考文献 相似文献 引证文献