Open annotations on multimedia Web resources

Many Web portals allow users to associate additional information with existing multimedia resources such as images, audio, and video. However, these portals are usually closed systems and user-generated annotations are almost always kept locked up and remain inaccessible to the Web of Data. We believe that an important step to take is the integration of multimedia annotations and the Linked Data principles. We present the current state of the Open Annotation Model, explain our design rationale, and describe how the model can represent user annotations on multimedia Web resources. Applying this model in Web portals and devices, which support user annotations, should allow clients to easily publish and consume, thus exchange annotations on multimedia Web resources via common Web standards.     

Piggy Bank: Experience the Semantic Web inside your web browser

The Semantic Web Initiative envisions a Web wherein information is offered free of presentation, allowing more effective exchange and mixing across web sites and across web pages. But without substantial Semantic Web content, few tools will be written to consume it; without many such tools, there is little appeal to publish Semantic Web content.To break this chicken-and-egg problem, thus enabling more flexible information access, we have created a web browser extension called Piggy Bank that lets users make use of Semantic Web content within Web content as users browse the Web. Wherever Semantic Web content is not available, Piggy Bank can invoke screenscrapers to re-structure information within web pages into Semantic Web format. Through the use of Semantic Web technologies, Piggy Bank provides direct, immediate benefits to users in their use of the existing Web. Thus, the existence of even just a few Semantic Web-enabled sites or a few scrapers already benefits users. Piggy Bank thereby offers an easy, incremental upgrade path to users without requiring a wholesale adoption of the Semantic Web's vision.To further improve this Semantic Web experience, we have created Semantic Bank, a web server application that lets Piggy Bank users share the Semantic Web information they have collected, enabling collaborative efforts to build sophisticated Semantic Web information repositories through simple, everyday's use of Piggy Bank.     

Trends in built environment semantic Web applications: Where are we today?

The built environment sector impacts significantly on communities. At the same time, it is the sector with the highest cost and environmental saving potentials provided effective strategies are implemented. The emerging Semantic Web promises new opportunities for efficient management of information and knowledge about various domains. While other domains, particularly bioinformatics have fully embraced the Semantic Web, knowledge about how the same has been applied to the built environment is sketchy. This study investigates the development and trend of Semantic Web applications in the built environment. Understanding the different applications of the Semantic Web is essential for evaluation, improvement and opening of new research. A review of over 120 refereed articles on built environment Semantic Web applications has been conducted. A classification of the different Semantic Web applications in relation to their year of application is presented to highlight the trend. Two major findings have emerged. Firstly, despite limited research about easy-to-use applications, progress is being made from often too-common ontological concepts to more innovative concepts such as Linked Data. Secondly, a shift from traditional construction applications to Semantic Web sustainable construction applications is gradually emerging. To conclude, research challenges, potential future development and research directions have been discussed.     

语义物联网中一种多领域信息互操作方法

针对当前语义物联网尚未形成一套比较完善、可共享的语义系统,无法支持不同领域资源描述和语义互操作性问题,提出了一个语义物联网中基于多个领域本体及链接开放数据的语义互操作方法。该方法可以半自动化标注新部署的传感器,为传感器数据添加语义,使机器理解数据含义,并对其进行推理;采用本体实体表管理同一领域本体,来规范统一本体;基于链接开放数据获取命名实体相关的语义,对信息进行补充。最后通过具体实例阐述其工作过程,并与其它经典的方法进行比较。结果表明,其可以较好的实现多领域信息的互操作。     

Revyu: Linking reviews and ratings into the Web of Data

Revyu is a live, publicly accessible reviewing and rating Web site, designed to be usable by humans whilst transparently generating machine-readable RDF metadata for the Semantic Web, based on user input. The site uses Semantic Web specifications such as RDF and SPARQL, and the latest Linked Data best practices to create a major node in a potentially Web-wide ecosystem of reviews and related data. Throughout the implementation of Revyu design decisions have been made that aim to minimize the burden on users, by maximizing the reuse of external data sources, and allowing less structured human input (in the form of Web 2.0-style tagging) from which stronger semantics can later be derived. Links to external sources such as DBpedia are exploited to create human-oriented mashups at the HTML level, whilst links are also made in RDF to ensure Revyu plays a first class role in the blossoming Web of Data. In this paper we document design decisions made during the implementation of Revyu, discuss the techniques used for linking Revyu data with external sources, and outline how data from the site is being used to infer the trustworthiness of reviewers as sources of information and recommendations.     

Challenges for the multilingual Web of Data

The Web has witnessed an enormous growth in the amount of semantic information published in recent years. This growth has been stimulated to a large extent by the emergence of Linked Data. Although this brings us a big step closer to the vision of a Semantic Web, it also raises new issues such as the need for dealing with information expressed in different natural languages. Indeed, although the Web of Data can contain any kind of information in any language, it still lacks explicit mechanisms to automatically reconcile such information when it is expressed in different languages. This leads to situations in which data expressed in a certain language is not easily accessible to speakers of other languages.The Web of Data shows the potential for being extended to a truly multilingual web as vocabularies and data can be published in a language-independent fashion, while associated language-dependent (linguistic) information supporting the access across languages can be stored separately. In this sense, the multilingual Web of Data can be realized in our view as a layer of services and resources on top of the existing Linked Data infrastructure adding (i) linguistic information for data and vocabularies in different languages, (ii) mappings between data with labels in different languages, and (iii) services to dynamically access and traverse Linked Data across different languages.In this article, we present this vision of a multilingual Web of Data. We discuss challenges that need to be addressed to make this vision come true and discuss the role that techniques such as ontology localization, ontology mapping, and cross-lingual ontology-based information access and presentation will play in achieving this. Further, we propose an initial architecture and describe a roadmap that can provide a basis for the implementation of this vision.     

Core techniques of question answering systems over knowledge bases: a survey

The Semantic Web contains an enormous amount of information in the form of knowledge bases (KB). To make this information available, many question answering (QA) systems over KBs were created in the last years. Building a QA system over KBs is difficult because there are many different challenges to be solved. In order to address these challenges, QA systems generally combine techniques from natural language processing, information retrieval, machine learning and Semantic Web. The aim of this survey is to give an overview of the techniques used in current QA systems over KBs. We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data. Techniques that solve the same task are first grouped together and then described. The advantages and disadvantages are discussed for each technique. This allows a direct comparison of similar techniques. Additionally, we point to techniques that are used over WebQuestions and SimpleQuestions, which are two other popular benchmarks for QA systems.     

语义物联网中一种基于QoS的动态服务组合方法

针对语义物联网中实体设备所提供的原子服务的资源有限性及其所处环境的动态变化性等问题,提出了一种语义物联网中基于QoS的动态服务组合方法。该方法依据添加情境的QoS本体,在对语义物联网中的服务进行动态地语义标注后,对待选择服务集进行动态地选择与组合,并将组合后的新服务通过服务ID映射到对应的实体设备上,最后通过实体设备将结果反馈给服务请求者。通过对具体实例的对比分析表明,该方法可以针对不同的服务对象以及不同的服务环境动态地为服务请求者提供更加准确的服务。     

Exploring Social Annotations with the Application to Web Page Recommendation

Collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources such as Web pages, photos, or videos. These annotations are a method for organizing and labeling information. They have the potential to help users navigate the Web and locate the needed resources. However, since annotations are posted by users under no central control, there exist problems such as spam and synonymous annotations. To efficiently use annotation information to facilitate knowledge discovery from the Web, it is advantageous if we organize social annotations from semantic perspective and embed them into algorithms for knowledge discovery. This inspires the Web page recommendation with annotations, in which users and Web pages are clustered so that semantically similar items can be related. In this paper we propose four graphic models which cluster users, Web pages and annotations and recommend Web pages for given users by assigning items to the right cluster first. The algorithms are then compared to the classical collaborative filtering recommendation method on a real-world data set. Our result indicates that the graphic models provide better recommendation performance and are robust to fit for the real applications.     

Laying the foundations for a World Wide Argument Web

This paper lays theoretical and software foundations for a World Wide Argument Web (WWAW): a large-scale Web of inter-connected arguments posted by individuals to express their opinions in a structured manner. First, we extend the recently proposed Argument Interchange Format (AIF) to express arguments with a structure based on Walton's theory of argumentation schemes. Then, we describe an implementation of this ontology using the RDF Schema Semantic Web-based ontology language, and demonstrate how our ontology enables the representation of networks of arguments on the Semantic Web. Finally, we present a pilot Semantic Web-based system, ArgDF, through which users can create arguments using different argumentation schemes and can query arguments using a Semantic Web query language. Manipulation of existing arguments is also handled in ArgDF: users can attack or support parts of existing arguments, or use existing parts of an argument in the creation of new arguments. ArgDF also enables users to create new argumentation schemes. As such, ArgDF is an open platform not only for representing arguments, but also for building interlinked and dynamic argument networks on the Semantic Web. This initial public-domain tool is intended to seed a variety of future applications for authoring, linking, navigating, searching, and evaluating arguments on the Web.     

Linking open data and the crowd for real-time passenger information

The availability of real-time passenger information (RTPI) is a key factor in making public transport both accessible and attractive to users. Unfortunately, rural areas often lack the infrastructure necessary to provide such information, and the cost of deploying and maintaining the required technologies outside of urban areas is seen as prohibitive. In this paper we present the GetThere system developed to overcome such issues and to provide public transport users in rural areas with RTPI. An ontological framework for representing mobility information is described, along with the Linked Data approach used to integrate heterogeneous data from multiple sources including government, transport operators, and the public. To mitigate possible issues with the veracity of this data, a quality assessment framework was developed that utilises data provenance. We also discuss our experiences working with Semantic Web technologies in this domain, and present results from both a user trial and a performance evaluation of the system.     

Enhancing Learning Objects with an Ontology-Based Memory

The reusability in learning objects has always been a hot issue. However, we believe that current approaches to e-Learning failed to find a satisfying answer to this concern. This paper presents an approach that enables capitalization of existing learning resources by first creating "content metadatardquo through text mining and natural language processing and second by creating dynamically learning knowledge objects, i.e., active, adaptable, reusable, and independent learning objects. The proposed model also suggests integrating explicitly instructional theories in an on-the-fly composition process of learning objects. Semantic Web technologies are used to satisfy such an objective by creating an ontology-based organizational memory able to act as a knowledge base for multiple training environments.     

Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation

We investigate the possibility of using Semantic Web data to improve hypertext Web search. In particular, we use relevance feedback to create a ‘virtuous cycle’ between data gathered from the Semantic Web of Linked Data and web-pages gathered from the hypertext Web. Previous approaches have generally considered the searching over the Semantic Web and hypertext Web to be entirely disparate, indexing, and searching over different domains. While relevance feedback has traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings over a single data-set. Our novel approach is to use relevance feedback from hypertext Web results to improve Semantic Web search, and results from the Semantic Web to improve the retrieval of hypertext Web data. In both cases, an evaluation is performed based on certain kinds of informational queries (abstract concepts, people, and places) selected from a real-life query log and checked by human judges. We evaluate our work over a wide range of algorithms and options, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Web Search engine FALCON-S and Yahoo! Web search. We further show that the use of Semantic Web inference seems to hurt performance, while the pseudo-relevance feedback increases performance in both cases, although not as much as actual relevance feedback. Lastly, our evaluation is the first rigorous ‘Cranfield’ evaluation of Semantic Web search.     

SESLDS: An Extension Scheme for Linked Data Sources Based on Semantically Enhanced Annotation and Reasoning

In the era of Big Data, users prefer to get knowledge rather than pages from Web. Linked Data, a rather new form of knowledge representation and publishing described by RDF, can provide a more precise and comprehensible semantic structure to satisfy the aforementioned requirement. Besides, as the standard query language for RDF data, SPARQL has become the foundation protocol of Linked Data querying. The core idea of RDF Schema (RDFS) is to extend upon RDF vocabulary and allow attachment of semantics to user defined classes and properties. However, RDFS cannot fully utilize the potential of RDF since it cannot express the implicit semantics between linked entities in Linked Data sources. To fill this gap, in this paper, we design a new semantic annotating and reasoning approach that can extend more implicit semantics from different properties. We firstly establish a well‐defined semantically enhanced annotation strategy for Linked Data sources. In particular, we present some new semantic properties for predicates in RDF triples and design a Semantic Matrix for Predicates (SM_P). We then propose a novel general Semantically Extended Scheme for Linked Data Sources (SES_LDS) to realize the semantic extension over the target Linked Data source through semantically enhanced reasoning. Lastly, based on the experimental analyses, we verify that our proposal has advantages over the initial Linked Data source and can return more valid results.     

Extracting significant Website Key Objects: A Semantic Web mining approach

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.