PGSW-OS: a novel approach for resource management in a semantic web operating system based on a P2P grid architecture

A web operating system is an operating system that users can access from any hardware at any location. A peer-to-peer (P2P) grid uses P2P communication for resource management and communication between nodes in a grid and manages resources locally in each cluster, and this provides a proper architecture for a web operating system. Use of semantic technology in web operating systems is an emerging field that improves the management and discovery of resources and services. In this paper, we propose PGSW-OS (P2P grid semantic Web OS), a model based on a P2P grid architecture and semantic technology to improve resource management in a web operating system through resource discovery with the aid of semantic features. Our approach integrates distributed hash tables (DHTs) and semantic overlay networks to enable semantic-based resource management by advertising resources in the DHT based upon their annotations to enable semantic-based resource matchmaking. Our model includes ontologies and virtual organizations. Our technique decreases the computational complexity of searching in a web operating system environment. We perform a simulation study using the Gridsim simulator, and our experiments show that our model provides enhanced utilization of resources, better search expressiveness, scalability, and precision.     

Improving large-scale search engines with semantic annotations

Traditional search engines have become the most useful tools to search the World Wide Web. Even though they are good for certain search tasks, they may be less effective for others, such as satisfying ambiguous or synonym queries. In this paper, we propose an algorithm that, with the help of Wikipedia and collaborative semantic annotations, improves the quality of web search engines in the ranking of returned results. Our work is supported by (1) the logs generated after query searching, (2) semantic annotations of queries and (3) semantic annotations of web pages. The algorithm makes use of this information to elaborate an appropriate ranking. To validate our approach we have implemented a system that can apply the algorithm to a particular search engine. Evaluation results show that the number of relevant web resources obtained after executing a query with the algorithm is higher than the one obtained without it.     

A semantic enriched data model for sensor network interoperability

One of the main open issues in the development of applications for sensor network management is the definition of interoperability mechanisms among the several monitoring systems and heterogeneous data. Interesting researches related to integration techniques have taken place, they are primary based on the adoption of sharing data mechanisms. In the last years, the Service-Oriented Architecture (SOA) approach has become predominant in many sensor network projects as it enables the cooperation and interoperability of different sensor platforms at an higher level of abstraction. In this paper we propose a novel architecture for the interoperability of sensor networks, which is based on web services technologies and on a common data model enriched with semantic concepts and annotations. The proposed architecture allows the development of complex decision support system applications by integration of heterogeneous data, accessible through services, according to standard data format and standard protocols.     

A case for automated large-scale semantic annotation

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large-scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.     

基于语义Web的LBS平台研究

移动位置服务近年来发展迅速,然而当前的LBS平台对动态空间信息的存取、交换、集成方式都不尽相同,异构平台之间的内容共享与交互也存在一些缺陷.重点讨论了LBS应用如何利于语义Web和本体技术从不同领域中获取动态空间信息,设计了基于语义Web的LBS平台架构,并在此基础上实现了一个语义LBS平台原型.     

Reflect: A practical approach to web semantics

To date, adding semantic capabilities to web content usually requires considerable server-side re-engineering, thus only a tiny fraction of all web content currently has semantic annotations. Recently, we announced Reflect (http://reflect.ws), a free service that takes a more practical approach: Reflect uses augmented browsing to allow end-users to add systematic semantic annotations to any web-page in real-time, typically within seconds. In this paper we describe the tagging process in detail and show how further entity types can be added to Reflect; we also describe how publishers and content providers can access Reflect programmatically using SOAP, REST (HTTP post), and JavaScript. Usage of Reflect has grown rapidly within the life sciences, and while currently only genes, protein and small molecule names are tagged, we plan to soon expand the scope to include a much broader range of terms (e.g., Wikipedia entries). The popularity of Reflect demonstrates the use and feasibility of letting end-users decide how and when to add semantic annotations. Ultimately, ‘semantics is in the eye of the end-user’, hence we believe end-user approaches such as Reflect will become increasingly important in semantic web technologies.     

Semantic Annotating of Text Documents: Basic Concepts and Taxonomic Approach

One of the tools for the semantic enrichment of the content of information resources is semantic annotating, which makes it possible to comment on and evaluate annotated resources and their fragments and to carry out a semantic search on their basis. The use of the taxonomic approach simultaneously allows classifying the subjects of annotating and generating new scientometric indicators. This paper considers the essence of semantic annotating, defines the basic concepts, discusses the general model of semantic annotation and taxonomic approach to presenting the semantics of annotations, and gives the examples of taxonomies based on various properties of annotations. The implementation of semantic annotating in the Socionet scientific information system is considered as an example.     

机器学习与网络信息处理

机器学习在网络信息处理中占有重要地位。GHunt是一个采用多项机器学习技术的网络信息智能获取与处理系统。首先,这一系统支持分布式的网络信息并行搜索与内容过滤;其次,采用机器学习技术,包括文本分类、聚类,文本概念抽取,从概念层次理解文本信息;再次,基于概念语义空间有效地统一文本信息管理;最后提供高效的基于概念语义的文本信息检索,以及个性化的专题组织与信息推送服务。文中着重阐述了系统中所用到的机器学习技术。     

基于结构相似性的语义网信息检索

语义网是提高网络信息检索质量的重要途径。利用现在流行的Web信息检索系统,扩展其处理语义Web文档中的苎义评注的能力,采用更有效的文档分类算法,将语义Web文档组织成蔟,提高检索质量。介绍的算法与标准的基于图匹配算法相比,计算代价降低,便于后续的存储、抽取和处理等工作。     

Knowledge base grid: A generic grid architecture for semantic web

The emergence of semantic web will result in an enormous amount of knowledge base resources on the web. In this paper, a generic Knowledge Base Grid Architecture (KB-Grid) for building large-scale knowledge systems on the semantic web is presented. KB-Grid suggests a paradigm that emphasizes how to organize, discover, utilize, and manage web knowledge base resources. Four principal components are under development: a semantic browser for retrieving and browsing semantically enriched information, a knowledge server acting as the web container for knowledge, an ontology server for managing web ontologies, and a knowledge base directory server acting as the registry and catalog of KBs. Also a referential model of knowledge service and the mechanisms required for semantic communication within KB-Grid are defined. To verify the design rationale underlying the KB-Grid, an implementation of Traditional Chinese Medicine (TCM) is described.     

基于语义模型的信息检索机制研究

提出了一种基于本体语义模型的信息检索方法。该方法充分利用领域本体提供的概念之间的语义相关性，从语义模型扩展、概念相似度、相关度计算，并以用户反馈等角度探讨了基于语义模型的自动推理方法在信息检索中的应用，文章介绍了系统实现框架。该系统将应用在学习资源的智能检索中。     

资源平台在高校实验课的应用研究

资源平台包括网络硬件平台、软件资源平台以及资源数据库。为保持系统的结构完整性和使用的方便性,将整个模型规划成了两个子系统,即平台资源网站生成系统和平台资源网站管理系统,平台资源网站生成系统为教师桌面使用的客户端软件,平台资源网站管理系统为B/S结构的服务器系统,将教师教学指导意见和学生自主学习结合到两个系统之中,形成互补又可以独立应用的软件支撑体系。     

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.     

Improving image tags by exploiting web search results

Automatic image tagging automatically assigns image with semantic keywords called tags, which significantly facilitates image search and organization. Most of present image tagging approaches are constrained by the training model learned from the training dataset, and moreover they have no exploitation on other type of web resource (e.g., web text documents). In this paper, we proposed a search based image tagging algorithm (CTSTag), in which the result tags are derived from web search result. Specifically, it assigns the query image with a more comprehensive tag set derived from both web images and web text documents. First, a content-based image search technology is used to retrieve a set of visually similar images which are ranked by the semantic consistency values. Then, a set of relevant tags are derived from these top ranked images as the initial tag set. Second, a text-based search is used to retrieve other relevant web resources by using the initial tag set as the query. After the denoising process, the initial tag set is expanded with other tags mined from the text-based search result. Then, an probability flow measure method is proposed to estimate the probabilities of the expanded tags. Finally, all the tags are refined using the Random Walk with Restart (RWR) method and the top ones are assigned to the query images. Experiments on NUS-WIDE dataset show not only the performance of the proposed algorithm but also the advantage of image retrieval and organization based on the result tags.     

语义网络及其Web信息检索机制研究

研究了语义网络的基本技术，重点探讨了语义网络中的信息检索机制，并利用其中的部分技术设计了一个图书检索系统。     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

Relevance Feedback and Learning in Content-Based Image Search

A major bottleneck in content-based image retrieval (CBIR) systems or search engines is the large gap between low-level image features used to index images and high-level semantic contents of images. One solution to this bottleneck is to apply relevance feedback to refine the query or similarity measures in image search process. In this paper, we first address the key issues involved in relevance feedback of CBIR systems and present a brief overview of a set of commonly used relevance feedback algorithms. Almost all of the previously proposed methods fall well into such framework. We present a framework of relevance feedback and semantic learning in CBIR. In this framework, low-level features and keyword annotations are integrated in image retrieval and in feedback processes to improve the retrieval performance. We have also extended framework to a content-based web image search engine in which hosting web pages are used to collect relevant annotations for images and users' feedback logs are used to refine annotations. A prototype system has developed to evaluate our proposed schemes, and our experimental results indicated that our approach outperforms traditional CBIR system and relevance feedback approaches.     

基于微博的学生学习资源推荐系统的构建

随着Internet技术的不断发展,信息孤岛、信息过载和资源迷航问题严重影响用户高效率地发现、利用互联网资源。以为学生用户推荐优秀的学习资源为出发点,利用微博技术构建一个为学生用户推荐学习资源的资源推荐平台。该平台以空间向量模型为表示模型,通过Heritrix、SCWS分词器、基于内容且带有协同过滤的混合推荐算法等多种技术来收集、获取、筛选、推介学习资源数据。     

Enhancing electronic medical record retrieval through semantic query expansion

There are currently many active movements towards computerizing patient healthcare information. As Electronic Medical Record (EMR) systems are being increasingly adopted in healthcare facilities, however, there is a big challenge in effectively utilizing this massive information source. It is very time-consuming for healthcare providers to dig into the voluminous medical records of a patient to find the few that are indeed relevant to the patient’s current problem. Due to the complex semantic relationships among medical concepts and use of many synonyms, antonyms, and hypernym/hyponym, simple word-based information retrieval does not produce satisfactory results. In this paper, we propose an EMR retrieval system that leverages semantic query expansion to retrieve medical records that are relevant to the patient’s current symptom/problem. The proposed framework integrates various technologies, including information retrieval, domain ontologies, automatic semantic relationship learning, as well as a body of domain knowledge elicited from healthcare experts. Knowledge of semantic relationships among medical concepts, such as symptoms, exams and tests, diagnoses, and treatments, as well as knowledge of synonyms and hypernym/hyponyms, is used to expand and enhance initial queries posed by a user. We have implemented a preliminary prototype and conducted a pilot testing using sample nursing notes drawn from the EMR system of a community health center.     

Adapting web pages using graph partitioning algorithms for user-centric multi-device web browsing

In the era of ubiquitous computing, applications are emerging to benefit from using devices of different users and different capabilities together. This paper focuses on user-centric web browsing using multiple devices, where content of a web page is partitioned, adapted and allocated to devices in the vicinity. We contribute two novel web page partitioning algorithms. They differ from existing approaches by allowing for both, automatic and semi-automatic partitioning. On the one hand, this provides good automatic, web page independent results by utilizing sophisticated structural pre- and postprocessing of the web page. On the other hand, these results can be improved by considering additional semantic information provided through user-generated web page annotations. We further present a performance evaluation of our algorithms. Moreover, we contribute the results of a user study. These clearly show that (1) our algorithms provide good automatic results and (2) the application of user-centric, annotation-based semantic information leads to a significantly higher user satisfaction.