An evaluation of semantic fisheye views for opportunistic search in an annotated image collection

Visual interfaces are potentially powerful tools for users to explore a representation of a collection and opportunistically discover information that will guide them toward relevant documents. Semantic fisheye views (SFEVs) are focus + context visualization techniques that manage visual complexity by selectively emphasizing and increasing the detail of information related to the users focus and deemphasizing or filtering less important information.In this paper we describe a prototype for visualizing an annotated image collection and an experiment to compare the effectiveness of two distinctly different SFEVs for a complex opportunistic search task. The first SFEV calculates relevance based on keyword-content similarity and the second based on conceptual relationships between images derived using WordNet. The results of the experiment suggest that semantic-guided search is significantly more effective than similarity-guided search for discovering and using domain knowledge in a collection.     

基于Lucene的语义检索系统的设计和实现

研究了语义检索,以Lucene为主要索引引擎,组合其它索引引擎,结合同义词查询扩展的概念,设计了一个语义检索系统,详细地描述了系统的实现过程.分析了语义检索的特点,从同义词扩展的角度,丰富和扩展了查询语义,查询扩展模块被很好的整合到以Lucene为基础的检索系统中,该系统在实验中表现了良好的效果.     

基于语义分析的垂直搜索网络蜘蛛

通用搜索引擎数据量庞大,但查询结果不够准确.分类目录正好相反.为了综合两者优势,对垂直搜索引擎进行了研究和分析.着重研究了垂直搜索引擎的核心模块--智能网络搜索蜘蛛.提出了搜索分析的新概念--规则.研究了蜘蛛中定义支持同义词的语义词典的方法,给出了按照规则分析和检索的实现方法和流程.程序需要定义多种规则,让蜘蛛依照规则进行网页爬行和信息采集.最后给出一个项目实例,证明了上述方法的可行性.     

基于ARM7的MPEG-4视频解码器的优化

分析了ARM7处理器的结构特点,针对解码器的优化特点和芯片的硬件结构,采用了算法级、语言级、ARM级联合优化的方法,对标准MPEG-4解码过程进行了优化。通过本文所总结的ARM7TDMI上视频解码的优化方法,可以使MPEG4视频解码节约大量的数据处理时间,能较好地满足低分辨率、低帧率场合实时解码的要求。     

DanVideo: an MPEG-7 authoring and retrieval system for dance videos

A well-annotated dance media is an essential part of a nation’s identity, transcending cultural and language barriers. Many dance video archives suffer from problems concerning authoring and access, because of the complex spatio-temporal relationships that exist between the dancers in terms of movements of their body parts and the emotions expressed by them in a dance. This paper presents a system named DanVideo for semi-automatic authoring and access to dance archives. DanVideo provides methods of annotation and authoring and retrieval tools for choreographers, dancers, and students. We demonstrate how dance media can be semantically annotated and how this information can be used for the retrieval of the dance video semantics. In particular, DanVideo offers an MPEG-7 based semi-automatic authoring tool that takes dance video annotations generated by dance experts and produces MPEG-7 metadata. DanVideo also has a search engine that takes users’ queries and retrieves dance semantics from metadata arranged using tree-embedding technique and based on spatial, temporal and spatio-temporal features of dancers. The search engine also leverages a domain-specific ontology to process knowledge-based queries. We have assessed the dance-video queries and semantic annotations in terms of precision, recall, and fidelity.     

A game approach to optimization of bandwidth allocation using MPEG-7 and MPEG-21

Resource allocation aims at optimizing the usage of shared resources and maximizing the end-user experience by determining the optimum sharing of resources and choosing whom to serve, how and when. Approaches addressing this challenge are driven by the requirements of the environment and the characteristics of users. This paper addresses the challenge of optimising resource allocation through the combined application of game theory and normative tools such MPEG-21 and MPEG-7. The resulting game play leads to a forced Nash equilibrium where a change of strategy will not make any player better off.     

Using semantic components to search for domain-specific documents: An evaluation from the system perspective and the user perspective

We seek to leverage an expert user's knowledge about how information is organized in a domain and how information is presented in typical documents within a particular domain-specific collection, to effectively and efficiently meet the expert's targeted information needs. We have developed the semantic components model to describe important semantic content within documents. The semantic components model for a given collection (based on a general understanding of the type of information needs expected) consists of a set of document classes, where each class has an associated set of semantic components. Each semantic component instance consists of segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. This paper describes how the semantic components model can be used to improve an information retrieval system. We present experimental evidence from a large interactive searching study that compared the use of semantic components in a system with full text and keyword indexing, where we extended the query language to allow users to search using semantic components, to a base system that did not have semantic components. We evaluate the systems from a system perspective, where semantic components were shown to improve document ranking for precision-oriented searches, and from a user perspective. We also evaluate the systems from a session-based perspective, evaluating not only the results of individual queries but also the results of multiple queries during a single interactive query session.     

Relevance feedback for category search in music retrieval based on semantic concept learning

Traditional content-based music retrieval systems retrieve a specific music object which is similar to what a user has requested. However, the need exists for the development of category search for the retrieval of a specific category of music objects which share a common semantic concept. The concept of category search in content-based music retrieval is subjective and dynamic. Therefore, this paper investigates a relevance feedback mechanism for category search of polyphonic symbolic music based on semantic concept learning. For the consideration of both global and local properties of music objects, a segment-based music object modeling approach is presented. Furthermore, in order to discover the user semantic concept in terms of discriminative features of discriminative segments, a concept learning mechanism based on data mining techniques is proposed to find the discriminative characteristics between relevant and irrelevant objects. Moreover, three strategies, the Most-Positive, the Most-Informative, and the Hybrid, to return music objects concerning user relevance judgments are investigated. Finally, comparative experiments are conducted to evaluate the effectiveness of the proposed relevance feedback mechanism. Experimental results show that, for a database of 215 polyphonic music objects, 60% average precision can be achieved through the use of the proposed relevance feedback mechanism.

QBEES: query-by-example entity search in semantic knowledge graphs based on maximal aspects,diversity-awareness and relaxation

Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based on structural features, so-called aspects and maximal aspects of the entities, that naturally model potential interest profiles of a user submitting an ambiguous query. Our approach based on maximal aspects provides natural diversity awareness and includes query-dependent and query-independent entity ranking components. We present evaluation results with a number of existing entity list completion benchmarks, comparing to several state-of-the-art baselines.     

Evaluation of semantic similarity metrics applied to the automatic retrieval of medical documents: An UMLS approach

One promise of current information retrieval systems is the capability to identify risk groups for certain diseases and pathologies based on the automatic analysis of vast amounts of Electronic Medical Records repositories. However, the complexity and the degree of specialization of the language used by the experts in this context, make this task both challenging and complex. In this work, we introduce a novel experimental study to evaluate the performance of the two semantic similarity metrics (Path and Intrinsic IC-Path, both widely accepted in the literature) in a real-life information retrieval situation. In order to achieve this goal and due to the lack of methodologies for this context in the literature, we propose a straightforward information retrieval system for the biomedical field based on the UMLS Metathesaurus and on semantic similarity metrics. In contrast with previous studies which focus on testbeds with limited and controlled sets of concepts, we use a large amount of information (101,712 medical documents extracted from TREC Medical Records Track 2011). Our results show that in real-life cases, both metrics display similar performance, Path (F-Measure = 0.430) e Intrinsic IC-Path (F-Measure = 0.427). Thereby we suggest that the use of Intrinsic IC-Path is not justified in real scenarios.     

一个MPEG-7搜索引擎的设计、实现和评估

MPEG-7处理链包括特征提取,描述本身和搜索引擎。目前MPEG-7方面的研究多集中于标准化部分和特征抽取,很少有人研究MPEG-7的搜索引擎。论文描述了一个基于MPEG-7的E-Learning领域视频语义摘要框架“SearchIt”的MPEG-7搜索引擎,它是一个智能的、使用原生XML数据库和XQL的分布式元数据搜索引擎。     

SemreX: Efficient search in a semantic overlay for literature retrieval

The World Wide Web is growing at such a pace that even the biggest centralized search engines are able to index only a small part of the available documents on the Internet. The decentralized structure, together with the features of self-organization and fault-tolerance, makes peer-to-peer networking an effective information-sharing model; however, content searching still remains a serious challenge of large scale peer-to-peer networks. In this paper we present SemreX, a semantic overlay for desktop literature/ document retrieval in peer-to-peer networks. We present a semantic overlay algorithm by which semantically similar peers are locally clustered together, and long-range connections are rewired for a short-cut in peer-to-peer networks. Based on the semantic overlay, a heuristic query routing algorithm is proposed for efficient content searching. We conduct a comprehensive simulation to evaluate the search performance of our algorithms. Results show that search in our SemreX semantic overlay greatly improves search efficiency.     

Modeling and evaluation of trust with an extension in semantic web

The aim of this paper is two folds. Firstly, some of the well known methods of trust modeling and trust evaluation that relates mainly to the semantic web structure are reviewed and analyzed. A categorization for calculation of trust and an analytical view of possible models of trust rating through a chain of acquaintances are presented. Based on experimental results the well known methods are compared and contrasted. Secondly a new method for evaluating trust is also proposed. This new model has the advantages of simplicity in calculation and enhanced accuracy. The method is associated with two algorithms, an algorithm for propagation and another for aggregation. The propagation algorithm utilizes statistical techniques and the aggregation algorithm is based on a weighting mechanism. The technique is named Max-weight method and is also implemented and the results are compared based on a designed accuracy metric. The proposed method may be employed as a subsystem for trust management in semantic web and trust evaluation in human interaction in a social networks as well as machines (artificial agents). Experimental results illustrate the efficiency and effectiveness of the proposed method.     

基于MPEG-7和MM混合模型的图像自动标注算法

为了弥补图像低层视觉特征和高层语义之间的"语义鸿沟",改善图像自动标注的性能,提出了基于多媒体描述接口(MPEG-7)和MM(Mixture Model)混合模型的图像标注算法。该算法采用MPEG-7标准推荐的颜色和纹理描述子提取图像的低层视觉特征,通过MM混合模型建立低层特征到高层语义空间的映射,实现了基于图像整体低层特征的多标签图像自动标注。通过在corel图像数据集上的一系列实验测试验证了该方法的可行性和有效性。