Relevance measures for the creation of groups in an annotation system

The MADCOW annotation system supports a notion of group, facilitating focused annotations with respect to a domain. In previous work, we adopted ontologies to represent knowledge about domains, thus allowing more refined annotations to a group, and discussed how the use of ontologies facilitates the formulation of semantically significant queries for retrieving annotations on specific topics. We now expand on previous results and study two new types of measures to identify matches between users׳ interests and groups: Degree Centrality, developed for social networks to assess the quality of concepts in an ontology, and URL concordance, indicating the similarity of interests among users who annotate the same pages.     

基于本体集成的语义标注模型设计

语义Web的全面实现需借助于语义标注,标注网页信息会涉及到多个本体.据此,通过研究桥本体,提出一个在本体集成的基础上建立起来的多本体语义标注模型.该模型利用桥本体集成顶层本体和多个领域本体,同时借助基于本体的信息抽取技术对网页进行语义标注,并将标注信息存入标注库,使标注信息与网页分离,提高语义检索的效率.通过举例说明了本模型的合理性.     

An experimental evaluation of ontology-based user profiles

In recent years, a number of research works have been carried out to improve the information retrieval process by exploiting external knowledge, e.g. by employing ontologies. Even though ontologies seem to be a promising technique to improve the retrieval process, hardly any study has been performed to evaluate the use of ontologies over a longer time period to model user interests. In this work we introduce an ontology based video recommender system that exploits implicit relevance feedback to capture users’ evolving information needs. The system exploits a generic ontology to organise users’ interests. We evaluate the recommendations by performing a user-centred multiple time-series study where participants were asked to include the system into their daily news gathering routine. The results of this study suggest that the system can be successfully employed to improve personal information seeking tasks in news domain.     

Consensus ontologies. Reconciling the semantics of Web pages andagents

As you build a Web site, it is worthwhile asking, "Should I put my information where it belongs or where people are most likely to look for it?" Our recent research into improving searching through ontologies is providing some interesting results to answer this question. The techniques developed by our research bring organization to the information received and reconcile the semantics of each document. Our goal is to help users retrieve dynamically generated information that is tailored to their individual needs and preferences. We believe that it is easier for individuals or small groups to develop their own ontologies, regardless of whether global ones are available, and that these can be automatically and ex-post-facto related. We are working to determine the efficacy of local annotation for Web sources, as well as performing reconciliation that is qualified by measures of semantic distance. If successful, this research will enable software agents to resolve the semantic misconceptions that inhibit successful interoperation with other agents and that limit the effectiveness of searching distributed information sources     

Assessing semantic annotation activities with formal concept analysis

This paper describes an approach to assessing semantic annotation activities based on formal concept analysis (FCA). In this approach, annotators use taxonomical ontologies created by domain experts to annotate digital resources. Then, using FCA, domain experts are provided with concept lattices that graphically display how their ontologies were used during the semantic annotation process. In consequence, they can advise annotators on how to better use the ontologies, as well as how to refine these ontologies to better suit the needs of the semantic annotators. To illustrate the approach, we describe its implementation in @note, a Rich Internet Application (RIA) for the collaborative annotation of digitized literary texts, we exemplify its use with a case study, and we provide some evaluation results using the method.     

Brahma: Browsing and Retrieval Architecture for Hierarchical Multimedia Annotation

Traditional browsing of large multimedia documents (e.g., video, audio) is primarily sequential. In the absence of an index structure browsing and searching for relevant information in a long video, audio or other multimedia document becomes difficult. Manual annotation can be used to mark various segments of such documents. Different segments can be combined to create new annotated segments, thus creating hierarchical annotation structures. Given the lack of structure in media data, it is natural for different users to have different views on the same media data. Therefore, different users can create different annotation structures. Users may also share some or all of each other's annotation structures. The annotation structure can be browsed or used to playback as a composed video consisting of different segments. Finally, the annotation structures can be manipulated dynamically by different users to alter views on a document. BRAHMA is a multimedia environment for browsing and retrieval of multimedia documents based on such hierarchical annotation structures.     

Where to publish and find ontologies? A survey of ontology libraries

One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to “talk” to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies. Thus ontology libraries can serve as a link in enabling diverse users and applications to discover, evaluate, use, and publish ontologies. In this paper, we provide a survey of the growing—and surprisingly diverse—landscape of ontology libraries. We highlight how the varying scope and intended use of the libraries affects their features, content, and potential exploitation in applications. From reviewing 11 ontology libraries, we identify a core set of questions that ontology practitioners and users should consider in choosing an ontology library for finding ontologies or publishing their own. We also discuss the research challenges that emerge from this survey, for the developers of ontology libraries to address.     

Semi-automatic construction of domain ontology for agent reasoning

One of the key elements of the Semantic Web technologies is domain ontologies and those ontologies are important constructs for multi-agent system. The Semantic Web relies on domain ontologies that structure underlying data enabling comprehensive and transportable machine understanding. It takes so much time and efforts to construct domain ontologies because these ontologies can be manually made by domain experts and knowledge engineers. To solve these problems, there have been many researches to semi-automatically construct ontologies. Most of the researches focused on relation extraction part but manually selected terms for ontologies. These researches have some problems. In this paper, we propose a hybrid method to extract relations from domain documents which combines a named relation approach and an unnamed relation approach. Our named relation approach is based on the Hearst’s pattern and the Snowball system. We merge a generalized pattern scheme into their methods. In our unnamed relation approach, we extract unnamed relations using association rules and clustering method. Moreover, we recommend candidate relation names of unnamed relations. We evaluate our proposed method by using Ziff document set offered by TREC.     

Relating ontologies with a fuzzy information model

More people than ever before have access to information with the World Wide Web; information volume and number of users both continue to expand. Traditional search methods based on keywords are not effective, resulting in large lists of documents, many of which unrelated to users’ needs. One way to improve information retrieval is to associate meaning to users’ queries by using ontologies, knowledge bases that encode a set of concepts about one domain and their relationships. Encoding a knowledge base using one single ontology is usual, but a document collection can deal with different domains, each organized into an ontology. This work presents a novel way to represent and organize knowledge, from distinct domains, using multiple ontologies that can be related. The model allows the ontologies, as well as the relationships between concepts from distinct ontologies, to be represented independently. Additionally, fuzzy set theory techniques are employed to deal with knowledge subjectivity and uncertainty. This approach to organize knowledge and an associated query expansion method are integrated into a fuzzy model for information retrieval based on multi-related ontologies. The performance of a search engine using this model is compared with another fuzzy-based approach for information retrieval, and with the Apache Lucene search engine. Experimental results show that this model improves precision and recall measures.     

SAMBO—A system for aligning and merging biomedical ontologies

Due to the recent explosion of the amount of on-line accessible biomedical data and tools, finding and retrieving the relevant information is not an easy task. The vision of a Semantic Web for life sciences alleviates these difficulties. A key technology for the Semantic Web is ontologies. In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned or merged. In this paper we propose a framework for aligning and merging ontologies. Further, we developed a system for aligning and merging biomedical ontologies (SAMBO) based on this framework. The framework is also a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations. In this paper we evaluated different strategies and their combinations in terms of quality and processing time and compared SAMBO with two other systems.     

The Digital Reading Desk: A lightweight approach to digital note-taking

Attentive reading is a complex and cognitively demanding task that uses note-taking and annotation to support the reader’s interpretation of the document. When reading on paper, extensive use of highlighting and other activities are conducted to support attentive reading, but this rich behaviour is not used with digital documents. Many users therefore print digital documents and then interact with them in physical form.This paper presents the “Digital Reading Desk,” an enhanced digital reading environment that provides support for attentive reading, providing a large working space for notes that mimics the use of desk space in a conventional physical environment. The Reading Desk uses a single tool to support both annotation and bookmarking, simplifying both the user’s learning of the system and their use of tools. Evaluation of the Reading Desk indicates preference for it, as well as considerable behavioural differences between our approach and traditional digital reading tools.     

Semantics based customization of UBL document schemas

Universal Business Language (UBL) is an OASIS initiative to develop common business document schemas to provide document interoperability in the eBusiness domain. Since the data requirements change according to a context, UBL schemas need to be customized and UBL defines a guideline to be followed for customization of schemas. XSD derivation based customization as proposed by UBL provides syntactic interoperability, that is, an XML parser that can interpret standard UBL documents can also interpret customized UBL documents. We argue that for UBL to become mainstream, syntactic interoperability alone is not enough. It needs to be supported by semantic interoperability, that is, it must be possible for users and even automated processes to discover and reuse customizations provided by other users. In this paper, we describe how to improve the UBL customization mechanism by providing semantic representations for context domains and describe how these semantics can be utilized by automated processes for component discovery and schema customization. For this purpose, we derive ontologies from taxonomies like the North American Industry Classification System (NAICS), the Universal Standard Products and Services Classification (UNSPSC) and relate corresponding concepts from different ontologies through ontology alignment. Then, we process these aligned ontologies using a reasoner to compute inferred ontologies representing context domains. We show that when custom UBL components are annotated using classes from these ontologies, automated discovery and customization becomes possible.     

Context-based information analysis for the Web environment

Finding the relevant set of information that satisfies an information request of a Web user in the availability of today’s vast amount of digital data is becoming a challenging problem. Currently, available Information Retrieval (IR) Systems are designed to return long lists of results, only a few of which are relevant for a specific user. In this paper, an IR method called Context-Based Information Analysis (CONIA) that investigates the context information of the user and user’s information request to provide relevant results for the given domain users is introduced. In this paper, relevance is measured by the semantics of the information provided in the documents. The information extracted from lexical and domain ontologies is integrated by the user’s interest information to expand the terms entered in the request. The obtained set of terms is categorized by a novel approach, and the relations between the categories are obtained from the ontologies. This categorization is used to improve the quality of the document selection by going beyond checking the availability of the words in the document by analyzing the semantic composition of the mapped terms.     

基于维基的协作式本体注记平台

本体技术使得信息系统具有处理知识的能力,增强信息检索与共享,但是目前本体工具与本体语言对普通的用户具有较高的门槛,加上本体建立方式依赖于本体构造者所注重的知识领域的议题,这反而阻碍了本体的演化。本文提出了一种以本体知识库为核心,基于维基的协作式本体注记平台,利用维基方便,简单及共享的特性推动网络化的本体建设。     

Web Authoring for Accessibility (WAfA)

Conventional thought from the Semantic Web community equates the use of ontologies with the representation of the meaning of content. Here, we skew this viewpoint by describing our ontology, Web Authoring for Accessibility (WAfA), which investigates the way ontologies can describe the semantic structure of documents. By understanding the way heterogeneous XHTML (Extensible Hypertext Mark-up Language) documents are structured we can better transform documents, currently inaccessible to visually impaired users. WAfA performs two tasks: (1) it allows us to flexibly model an XHTML document within the context of navigation and orientation through the Web resource; (2) it enables non-expert users to quickly annotate a Web document by providing a ‘lingua franca’ between author and Web Accessibility Domain Experts. Here we describe our ontology, its use, novelty, and importance.     

The Ontolingua Server: a tool for collaborative ontology construction

Reusable ontologies are becoming increasingly important for tasks such as information integration, knowledge-level interoperation and knowledge-base development. We have developed a set of tools and services to support the process of achieving consensus on commonly shared ontologies by geographically distributed groups. These tools make use of the World Wide Web to enable wide access and provide users with the ability to publish, browse, create and edit ontologies stored on anontology server. Users can quickly assemble a new ontology from a library of modules. We discuss how our system was constructed, how it exploits existing protocols and browsing tools, and our experience supporting hundreds of users. We describe applications using our tools to achieve consensus on ontologies and to integrate information.The Ontolingua Server may be accessed through the URLhttp://ontolingua.stanford.edu