Concept similarity and related categories in information retrieval using formal concept analysis

The application of formal concept analysis to the problem of information retrieval has been shown useful but has lacked any real analysis of the idea of relevance ranking of search results. SearchSleuth is a program developed to experiment with the automated local analysis of Web search using formal concept analysis. SearchSleuth extends a standard search interface to include a conceptual neighbourhood centred on a formal concept derived from the initial query. This neighbourhood of the concept derived from the search terms is decorated with its upper and lower neighbours representing more general and special concepts, respectively. SearchSleuth is in many ways an archetype of search engines based on formal concept analysis with some novel features. In SearchSleuth, the notion of related categories – which are themselves formal concepts – is also introduced. This allows the retrieval focus to shift to a new formal concept called a sibling. This movement across the concept lattice needs to relate one formal concept to another in a principled way. This paper presents the issues concerning exploring, searching, and ordering the space of related categories. The focus is on understanding the use and meaning of proximity and semantic distance in the context of information retrieval using formal concept analysis.     

相互协作中的私有信息检索方案

为了解决私有数据库信息检索面临的计算安全性和通信量之间很难达到合理的平衡状态的问题,提出一种基于任意第三方的检索方案.通过对现有的私有信息检索方案进行深入分析,找出研究的难点.基于计算不可分辨性概念以及可交换加密函数的理论,建立了一种依赖于任意第三方的私有信息检索方案.该方案充分解放了被请求方,并且可以安全高效地应用于日益频繁的相互协作查询中.对其正确性和安全性给予了分析和证明,实验结果表明了该算法的优化性.     

Fast image retrieval using color-spatial information

In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism. Received August 1, 1997 / Accepted December 9, 1997     

Peer-to-peer information retrieval using shared-content clustering

Peer-to-peer (p2p) networks are used by millions for searching and downloading content. Recently, clustering algorithms were shown to be useful for helping users find content in large networks. Yet, many of these algorithms overlook the fact that p2p networks follow graph models with a power-law node degree distribution. This paper studies the obtained clusters when applying clustering algorithms on power-law graphs and their applicability for finding content. Driven by the observed deficiencies, a simple yet efficient clustering algorithm is proposed, which targets a relaxed optimization of a minimal distance distribution of each cluster with a size balancing scheme. A comparative analysis using a song-similarity graph collected from 1.2 million Gnutella users reveals that commonly used efficiency measures often overlook search and recommendation applicability issues and provide the wrong impression that the resulting clusters are well suited for these tasks. We show that the proposed algorithm performs well on various measures that are well suited for the domain.     

基于互信息的缺陷定位方法

为充分挖掘执行结果与程序频谱的潜在关系,提出基于互信息的可疑度计算公式MIStar(mutual information star).通过分析程序在不同测试用例下的执行信息,引用互信息对传统的怀疑度公式进行优化,给每条语句赋予权重值,不断修正不确定性参数Star,获得良好的定位效果.实验结果表明,该方法较其它可疑度算法...     

Pictorial information retrieval using the random neural network

A technique is developed based on the use of a neural network model for performing information retrieval in a pictorial information system. The neural network provides autoassociative memory operation and allows the retrieval of stored symbolic images using erroneous or incomplete information as input. The network used is based on an adaptation of the random neural network model featuring positive and negative nodes and symmetrical behavior of positive and negative signals. The network architecture considered has hierarchical structure and allows two-level operation during learning and recall. An experimental software prototype, including an efficient graphical interface, has been implemented and tested. The performance of the system has been investigated through experiments under several schemes concerning storage and reconstruction of patterns. These schemes are either based on properties of the random network or constitute adaptations of known neural network techniques     

Improving legal information retrieval using an ontological framework

A variety of legal documents are increasingly being made available in electronic format. Automatic Information Search and Retrieval algorithms play a key role in enabling efficient access to such digitized documents. Although keyword-based search is the traditional method used for text retrieval, they perform poorly when literal term matching is done for query processing, due to synonymy and ambivalence of words. To overcome these drawbacks, an ontological framework to enhance the user’s query for retrieval of truly relevant legal judgments has been proposed in this paper. Ontologies ensure efficient retrieval by enabling inferences based on domain knowledge, which is gathered during the construction of the knowledge base. Empirical results demonstrate that ontology-based searches generate significantly better results than traditional search methods.     

Ontologies and reasoning techniques for (legal) intelligent information retrieval systems

An application of Narrative Knowledge Representation Language (NKRL) techniques on (declassified) ‘terrorism in Southern Philippines’ documents has been carried out in the context of the IST Parmenides project. This paper describes some aspects of this work: it is our belief, in fact, that the Knowledge Representation techniques and the Intelligent Information Retrieval tools used in this experiment can be of some interest also in an ‘Ontological Modelling of Legal Events and Legal Reasoning’ context.     

Improving Arabic information retrieval using word embedding similarities

Term mismatch is a common limitation of traditional information retrieval (IR) models where relevance scores are estimated based on exact matching of documents and queries. Typically, good IR model should consider distinct but semantically similar words in the matching process. In this paper, we propose a method to incorporate word embedding (WE) semantic similarities into existing probabilistic IR models for Arabic in order to deal with term mismatch. Experiments are performed on the standard Arabic TREC collection using three neural word embedding models. The results show that extending the existing IR models improves significantly baseline bag-of-words models. Although the proposed extensions significantly outperform their baseline bag-of-words, the difference between the evaluated neural word embedding models is not statistically significant. Moreover, the overall comparison results show that our extensions significantly improve the Arabic WordNet based semantic indexing approach and three recent WE-based IR language models.     

Region-based image retrieval using integrated color, shape, and location index

A technique to retrieve images by region matching using a combined feature index based on color, shape, and location is presented within the framework of MPEG-7. Dominant regions within each image are indexed using integrated color, shape, and location features. Various combinations of regions are also indexed. The resulting indices and related metadata are stored in a Hash structure, where similar images tend to form clusters. The retrieval process is non-cascading and images can be retrieved based on color, shape or location and also based on a combined color–shape–location index. Results obtained show that retrieval effectiveness increases in non-cascaded region-based querying by combined index.     

An empirical analysis of information retrieval based concept location techniques in software comprehension

Concept location, the problem of associating human oriented concepts with their counterpart solution domain concepts, is a fundamental problem that lies at the heart of software comprehension. Recent research has attempted to alleviate the impact of the concept location problem through the application of methods drawn from the information retrieval (IR) community. Here we present a new approach based on a complimentary IR method which also has a sound basis in cognitive theory. We compare our approach to related work through an experiment and present our conclusions. This research adapts and expands upon existing language modelling frameworks in IR for use in concept location, in software systems. In doing so it is novel in that it leverages implicit information available in system documentation. Surprisingly, empirical evaluation of this approach showed little performance benefit overall and several possible explanations are forwarded for this finding.

Integrating information retrieval, execution and link analysis algorithms to improve feature location in software

Data fusion is the process of integrating multiple sources of information such that their combination yields better results than if the data sources are used individually. This paper applies the idea of data fusion to feature location, the process of identifying the source code that implements specific functionality in software. A data fusion model for feature location is presented which defines new feature location techniques based on combining information from textual, dynamic, and web mining or link analyses algorithms applied to software. A novel contribution of the proposed model is the use of advanced web mining algorithms to analyze execution information during feature location. The results of an extensive evaluation on three Java systems indicate that the new feature location techniques based on web mining improve the effectiveness of existing approaches by as much as 87%.     

利用领域本体概念关系的混合信息检索方法*

利用领域本体概念关系实现语义检索,当没有可用的本体知识时,按传统关键词匹配完成检索.这种基于领域本体的混合信息检索模型融合关键词检索和语义检索的优势,弥补各自的不足,以改善检索性能.将其应用于计算机网络领域,将基于领域本体的混合信息检索模型与传统的关键词检索模型进行对比实验,结果表明混合模型的平均查准率得到了提高,验证...     

基于距离分布信息熵的商标图像检索

提出了一种基于距离分布信息熵的图像检索方法,该方法首先对图像的目标区域进行区域划分,然后提取区域的信息熵作为特征来描述图像形状,最后使用欧式距离度量熵矢量之间的相似性。实验结果表明,距离分布信息熵能有效地刻画出二值图象的形状特征,并且具有良好的平移、旋转及尺度不变性,检索结果符合人眼的视觉感受。