Search (7 results, page 1 of 1)

0.026575929 = product of:
  0.053151857 = sum of:
    0.053151857 = sum of:
      0.009471525 = weight(_text_:a in 759) [ClassicSimilarity], result of:
        0.009471525 = score(doc=759,freq=8.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.17835285 = fieldWeight in 759, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0546875 = fieldNorm(doc=759)
      0.043680333 = weight(_text_:22 in 759) [ClassicSimilarity], result of:
        0.043680333 = score(doc=759,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = queryNorm
          0.2708308 = fieldWeight in 759, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=759)
  0.5 = coord(1/2)

Abstract

XML will have a profound impact on the way data is exchanged on the Internet. An important feature of this language is the separation of content from presentation, which makes it easier to select and/or reformat the data. However, due to the likelihood of numerous industry and domain specific DTDs, those who wish to integrate information will still be faced with the problem of semantic interoperability. In this paper we discuss why this problem is not solved by XML, and then discuss why the Resource Description Framework is only a partial solution. We then present the SHOE language, which we feel has many of the features necessary to enable a semantic web, and describe an existing set of tools that make it easy to use the language.

Date

11. 5.2013 19:22:18

Type

a

0.0026742492 = product of:
  0.0053484985 = sum of:
    0.0053484985 = product of:
      0.010696997 = sum of:
        0.010696997 = weight(_text_:a in 5309) [ClassicSimilarity], result of:
          0.010696997 = score(doc=5309,freq=20.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.20142901 = fieldWeight in 5309, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5309)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Currently, several datasets released in a Linked Open Data format are available at a national and international level, but the lack of shared strategies concerning the definition of concepts related to the statistical publishing community makes difficult a comparison among given facts starting from different data sources. In order to guarantee a shared representation framework for what concerns the dissemination of statistical concepts about graduates, we developed SW4AL, an ontology-based system for graduate's surveys domain. The developed system transforms low-level data into an enriched information model and is based on the AlmaLaurea surveys covering more than 90% of Italian graduates. SW4AL: i) semantically describes the different peculiarities of the graduates; ii) promotes the structured definition of the AlmaLaurea data and the following publication in the Linked Open Data context; iii) provides their reuse in the open data scope; iv) enables logical reasoning about knowledge representation. SW4AL establishes a common semantic for addressing the concept of graduate's surveys domain by proposing the creation of a SPARQL endpoint and a Web based interface for the query and the visualization of the structured data.

Type

a

0.0025803389 = product of:
  0.0051606777 = sum of:
    0.0051606777 = product of:
      0.010321355 = sum of:
        0.010321355 = weight(_text_:a in 5903) [ClassicSimilarity], result of:
          0.010321355 = score(doc=5903,freq=38.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.19435552 = fieldWeight in 5903, product of:
              6.164414 = tf(freq=38.0), with freq of:
                38.0 = termFreq=38.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.02734375 = fieldNorm(doc=5903)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

RDF - the Resource Description Framework - is a foundation for processing metadata; it provides interoperability between applications that exchange machine-understandable information on the Web. RDF emphasizes facilities to enable automated processing of Web resources. RDF metadata can be used in a variety of application areas; for example: in resource discovery to provide better search engine capabilities; in cataloging for describing the content and content relationships available at a particular Web site, page, or digital library; by intelligent software agents to facilitate knowledge sharing and exchange; in content rating; in describing collections of pages that represent a single logical "document"; for describing intellectual property rights of Web pages, and in many others. RDF with digital signatures will be key to building the "Web of Trust" for electronic commerce, collaboration, and other applications. Metadata is "data about data" or specifically in the context of RDF "data describing web resources." The distinction between "data" and "metadata" is not an absolute one; it is a distinction created primarily by a particular application. Many times the same resource will be interpreted in both ways simultaneously. RDF encourages this view by using XML as the encoding syntax for the metadata. The resources being described by RDF are, in general, anything that can be named via a URI. The broad goal of RDF is to define a mechanism for describing resources that makes no assumptions about a particular application domain, nor defines the semantics of any application domain. The definition of the mechanism should be domain neutral, yet the mechanism should be suitable for describing information about any domain. This document introduces a model for representing RDF metadata and one syntax for expressing and transporting this metadata in a manner that maximizes the interoperability of independently developed web servers and clients. The syntax described in this document is best considered as a "serialization syntax" for the underlying RDF representation model. The serialization syntax is XML, XML being the W3C's work-in-progress to define a richer Web syntax for a variety of applications. RDF and XML are complementary; there will be alternate ways to represent the same RDF data model, some more suitable for direct human authoring. Future work may lead to including such alternatives in this document.

Content

RDF Data Model At the core of RDF is a model for representing named properties and their values. These properties serve both to represent attributes of resources (and in this sense correspond to usual attribute-value-pairs) and to represent relationships between resources. The RDF data model is a syntax-independent way of representing RDF statements. RDF statements that are syntactically very different could mean the same thing. This concept of equivalence in meaning is very important when performing queries, aggregation and a number of other tasks at which RDF is aimed. The equivalence is defined in a clean machine understandable way. Two pieces of RDF are equivalent if and only if their corresponding data model representations are the same. Table of contents 1. Introduction 2. RDF Data Model 3. RDF Grammar 4. Signed RDF 5. Examples 6. Appendix A: Brief Explanation of XML Namespaces

0.0025115174 = product of:
  0.0050230348 = sum of:
    0.0050230348 = product of:
      0.0100460695 = sum of:
        0.0100460695 = weight(_text_:a in 553) [ClassicSimilarity], result of:
          0.0100460695 = score(doc=553,freq=36.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18917176 = fieldWeight in 553, product of:
              6.0 = tf(freq=36.0), with freq of:
                36.0 = termFreq=36.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.02734375 = fieldNorm(doc=553)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Currently, a number of efforts are being carried out to integrate collections from different institutions and containing heterogeneous material. Examples of such projects are The European Library [1] and the Memory of the Netherlands [2]. A crucial point for the success of these is the availability to provide a unified access on top of the different collections, e.g. using one single vocabulary for querying or browsing the objects they contain. This is made difficult by the fact that the objects from different collections are often described using different vocabularies - thesauri, classification schemes - and are therefore not interoperable at the semantic level. To solve this problem, one can turn to semantic links - mappings - between the elements of the different vocabularies. If one knows that a concept C from a vocabulary V is semantically equivalent to a concept to a concept D from vocabulary W, then an appropriate search engine can return all the objects that were indexed against D for a query for objects described using C. We thus have an access to other collections, using a single one vocabulary. This is however an ideal situation, and hard alignment work is required to reach it. Several projects in the past have tried to implement such a solution, like MACS [3] and Renardus [4]. They have demonstrated very interesting results, but also highlighted the difficulty of aligning manually all the different vocabularies involved in practical cases, which sometimes contain hundreds of thousands of concepts. To alleviate this problem, a number of tools have been proposed in order to provide with candidate mappings between two input vocabularies, making alignment a (semi-) automatic task. Recently, the Semantic Web community has produced a lot of these alignment tools'. Several techniques are found, depending on the material they exploit: labels of concepts, structure of vocabularies, collection objects and external knowledge sources. Throughout our presentation, we will present a concrete heterogeneity case where alignment techniques have been applied to build a (pilot) browser, developed in the context of the STITCH project [5]. This browser enables a unified access to two collections of illuminated manuscripts, using the description vocabulary used in the first collection, Mandragore [6], or the one used by the second, Iconclass [7]. In our talk, we will also make the point for using unified representations the vocabulary semantic and lexical information. Additionally to ease the use of the alignment tools that have these vocabularies as input, turning to a standard representation format helps designing applications that are more generic, like the browser we demonstrate. We give pointers to SKOS [8], an open and web-enabled format currently developed by the Semantic Web community.

0.002325213 = product of:
  0.004650426 = sum of:
    0.004650426 = product of:
      0.009300852 = sum of:
        0.009300852 = weight(_text_:a in 2127) [ClassicSimilarity], result of:
          0.009300852 = score(doc=2127,freq=42.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17513901 = fieldWeight in 2127, product of:
              6.4807405 = tf(freq=42.0), with freq of:
                42.0 = termFreq=42.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0234375 = fieldNorm(doc=2127)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Content

"When dealing with a large-scale and widely-used knowledge organization system like the Dewey Decimal Classification, we often tend to focus solely on the organization aspect, which is closely intertwined with editorial work. This is perfectly understandable, since developing and updating the DDC, keeping up with current scientific developments, spotting new trends in both scholarly communication and popular publishing, and figuring out how to fit those patterns into the structure of the scheme are as intriguing as they are challenging. From the organization perspective, the intended user of the scheme is mainly the classifier. Dewey acts very much as a number-building engine, providing richly documented concepts to help with classification decisions. Since the Middle Ages, quasi-religious battles have been fought over the "valid" arrangement of places according to specific views of the world, as parodied by Jorge Luis Borges and others. Organizing knowledge has always been primarily an ontological activity; it is about putting the world into the classification. However, there is another side to this coin--the discovery side. While the hierarchical organization of the DDC establishes a default set of places and neighborhoods that is also visible in the physical manifestation of library shelves, this is just one set of relationships in the DDC. A KOS (Knowledge Organization System) becomes powerful by expressing those other relationships in a manner that not only collocates items in a physical place but in a knowledge space, and exposes those other relationships in ways beneficial and congenial to the unique perspective of an information seeker.
What are those "other" relationships that Dewey possesses and that seem so important to surface? Firstly, there is the relationship of concepts to resources. Dewey has been used for a long time, and over 200,000 numbers are assigned to information resources each year and added to WorldCat by the Library of Congress and the German National Library alone. Secondly, we have relationships between concepts in the scheme itself. Dewey provides a rich set of non-hierarchical relations, indicating other relevant and related subjects across disciplinary boundaries. Thirdly, perhaps most importantly, there is the relationship between the same concepts across different languages. Dewey has been translated extensively, and current versions are available in French, German, Hebrew, Italian, Spanish, and Vietnamese. Briefer representations of the top-three levels (the DDC Summaries) are available in several languages in the DeweyBrowser. This multilingual nature of the scheme allows searchers to access a broader range of resources or to switch the language of--and thus localize--subject metadata seamlessly. MelvilClass, a Dewey front-end developed by the German National Library for the German translation, could be used as a common interface to the DDC in any language, as it is built upon the standard DDC data format. It is not hard to give an example of the basic terminology of a class pulled together in a multilingual way: <class/794.8> a skos:Concept ; skos:notation "794.8"^^ddc:notation ; skos:prefLabel "Computer games"@en ; skos:prefLabel "Computerspiele"@de ; skos:prefLabel "Jeux sur ordinateur"@fr ; skos:prefLabel "Juegos por computador"@es .
Expressed in such manner, the Dewey number provides a language-independent representation of a Dewey concept, accompanied by language-dependent assertions about the concept. This information, identified by a URI, can be easily consumed by semantic web agents and used in various metadata scenarios. Fourthly, as we have seen, it is important to play well with others, i.e., establishing and maintaining relationships to other KOS and making the scheme available in different formats. As noted in the Dewey blog post "Tags and Dewey," since no single scheme is ever going to be the be-all, end-all solution for knowledge discovery, DDC concepts have been extensively mapped to other vocabularies and taxonomies, sometimes bridging them and acting as a backbone, sometimes using them as additional access vocabulary to be able to do more work "behind the scenes." To enable other applications and schemes to make use of those relationships, the full Dewey database is available in XML format; RDF-based formats and a web service are forthcoming. Pulling those relationships together under a common surface will be the next challenge going forward. In the semantic web community the concept of Linked Data (http://en.wikipedia.org/wiki/Linked_Data) currently receives some attention, with its emphasis on exposing and connecting data using technologies like URIs, HTTP and RDF to improve information discovery on the web. With its focus on relationships and discovery, it seems that Dewey will be well prepared to become part of this big linked data set. Now it is about putting the classification back into the world!"

0.0019376777 = product of:
  0.0038753555 = sum of:
    0.0038753555 = product of:
      0.007750711 = sum of:
        0.007750711 = weight(_text_:a in 4232) [ClassicSimilarity], result of:
          0.007750711 = score(doc=4232,freq=42.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14594918 = fieldWeight in 4232, product of:
              6.4807405 = tf(freq=42.0), with freq of:
                42.0 = termFreq=42.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.01953125 = fieldNorm(doc=4232)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

After the launch of the World Wide Web, it became clear that searching documentson the Web would not be trivial. Well-known engines to search the web, like Google, focus on search in web documents using keywords. The documents are structured and indexed to ensure keywords match documents as accurately as possible. However, searching by keywords does not always suice. It is oen the case that users do not know exactly how to formulate the search query or which keywords guarantee retrieving the most relevant documents. Besides that, it occurs that users rather want to browse information than looking up something specific. It turned out that there is need for systems that enable more interactivity and facilitate the gradual refinement of search queries to explore the Web. Users expect more from the Web because the short keyword-based queries they pose during search, do not suffice for all cases. On top of that, the Web is changing structurally. The Web comprises, apart from a collection of documents, more and more linked data, pieces of information structured so they can be processed by machines. The consequently applied semantics allow users to exactly indicate machines their search intentions. This is made possible by describing data following controlled vocabularies, concept lists composed by experts, published uniquely identifiable on the Web. Even so, it is still not trivial to explore data on the Web. There is a large variety of vocabularies and various data sources use different terms to identify the same concepts.
This PhD-thesis describes how to effectively explore linked data on the Web. The main focus is on scenarios where users want to discover relationships between resources rather than finding out more about something specific. Searching for a specific document or piece of information fits in the theoretical framework of information retrieval and is associated with exploratory search. Exploratory search goes beyond 'looking up something' when users are seeking more detailed understanding, further investigation or navigation of the initial search results. The ideas behind exploratory search and querying linked data merge when it comes to the way knowledge is represented and indexed by machines - how data is structured and stored for optimal searchability. Queries and information should be aligned to facilitate that searches also reveal connections between results. This implies that they take into account the same semantic entities, relevant at that moment. To realize this, we research three techniques that are evaluated one by one in an experimental set-up to assess how well they succeed in their goals. In the end, the techniques are applied to a practical use case that focuses on forming a bridge between the Web and the use of digital libraries in scientific research. Our first technique focuses on the interactive visualization of search results. Linked data resources can be brought in relation with each other at will. This leads to complex and diverse graphs structures. Our technique facilitates navigation and supports a workflow starting from a broad overview on the data and allows narrowing down until the desired level of detail to then broaden again. To validate the flow, two visualizations where implemented and presented to test-users. The users judged the usability of the visualizations, how the visualizations fit in the workflow and to which degree their features seemed useful for the exploration of linked data.
The ideas behind exploratory search and querying linked data merge when it comes to the way knowledge is represented and indexed by machines - how data is structured and stored for optimal searchability. eries and information should be aligned to facilitate that searches also reveal connections between results. This implies that they take into account the same semantic entities, relevant at that moment. To realize this, we research three techniques that are evaluated one by one in an experimental set-up to assess how well they succeed in their goals. In the end, the techniques are applied to a practical use case that focuses on forming a bridge between the Web and the use of digital libraries in scientific research.
Our first technique focuses on the interactive visualization of search results. Linked data resources can be brought in relation with each other at will. This leads to complex and diverse graphs structures. Our technique facilitates navigation and supports a workflow starting from a broad overview on the data and allows narrowing down until the desired level of detail to then broaden again. To validate the flow, two visualizations where implemented and presented to test-users. The users judged the usability of the visualizations, how the visualizations fit in the workflow and to which degree their features seemed useful for the exploration of linked data. There is a difference in the way users interact with resources, visually or textually, and how resources are represented for machines to be processed by algorithms. This difference complicates bridging the users' intents and machine executable queries. It is important to implement this 'translation' mechanism to impact the search as favorable as possible in terms of performance, complexity and accuracy. To do this, we explain a second technique, that supports such a bridging component. Our second technique is developed around three features that support the search process: looking up, relating and ranking resources. The main goal is to ensure that resources in the results are as precise and relevant as possible. During the evaluation of this technique, we did not only look at the precision of the search results but also investigated how the effectiveness of the search evolved while the user executed certain actions sequentially.
When we speak about finding relationships between resources, it is necessary to dive deeper in the structure. The graph structure of linked data where the semantics give meaning to the relationships between resources enable the execution of pathfinding algorithms. The assigned weights and heuristics are base components of such algorithms and ultimately define (the order) which resources are included in a path. These paths explain indirect connections between resources. Our third technique proposes an algorithm that optimizes the choice of resources in terms of serendipity. Some optimizations guard the consistence of candidate-paths where the coherence of consecutive connections is maximized to avoid trivial and too arbitrary paths. The implementation uses the A* algorithm, the de-facto reference when it comes to heuristically optimized minimal cost paths. The effectiveness of paths was measured based on common automatic metrics and surveys where the users could indicate their preference for paths, generated each time in a different way. Finally, all our techniques are applied to a use case about publications in digital libraries where they are aligned with information about scientific conferences and researchers. The application to this use case is a practical example because the different aspects of exploratory search come together. In fact, the techniques also evolved from the experiences when implementing the use case. Practical details about the semantic model are explained and the implementation of the search system is clarified module by module. The evaluation positions the result, a prototype of a tool to explore scientific publications, researchers and conferences next to some important alternatives.

0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 3152) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=3152,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 3152, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3152)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Content

Table of Contents Part I: Accepted Papers Christoph Tempich and Raphael Volz: Towards a benchmark for Semantic Web reasoners - an analysis of the DAML ontology library M. Carmen Suarez-Figueroa and Asuncion Gomez-Perez: Results of Taxonomic Evaluation of RDF(S) and DAML+OIL ontologies using RDF(S) and DAML+OIL Validation Tools and Ontology Platforms import services Volker Haarslev and Ralf Möller: Racer: A Core Inference Engine for the Semantic Web Mikhail Kazakov and Habib Abdulrab: DL-workbench: a metamodeling approach to ontology manipulation Thorsten Liebig and Olaf Noppens: OntoTrack: Fast Browsing and Easy Editing of Large Ontologie Frederic Fürst, Michel Leclere, and Francky Trichet: TooCoM : a Tool to Operationalize an Ontology with the Conceptual Graph Model Naoki Sugiura, Masaki Kurematsu, Naoki Fukuta, Noriaki Izumi, and Takahira Yamaguchi: A domain ontology engineering tool with general ontologies and text corpus Howard Goldberg, Alfredo Morales, David MacMillan, and Matthew Quinlan: An Ontology-Driven Application to Improve the Prescription of Educational Resources to Parents of Premature Infants Part II: Experiment Contributions Domain natural language description for the experiment Raphael Troncy, Antoine Isaac, and Veronique Malaise: Using XSLT for Interoperability: DOE and The Travelling Domain Experiment Christian Fillies: SemTalk EON2003 Semantic Web Export / Import Interface Test Óscar Corcho, Asunción Gómez-Pérez, Danilo José Guerrero-Rodríguez, David Pérez-Rey, Alberto Ruiz-Cristina, Teresa Sastre-Toral, M. Carmen Suárez-Figueroa: Evaluation experiment of ontology tools' interoperability with the WebODE ontology engineering workbench Holger Knublauch: Case Study: Using Protege to Convert the Travel Ontology to UML and OWL Franz Calvo and John Gennari: Interoperability of Protege 2.0 beta and OilEd 3.5 in the Domain Knowledge of Osteoporosis