Search (139 results, page 2 of 7)

0.0025370158 = product of:
  0.0050740317 = sum of:
    0.0050740317 = product of:
      0.010148063 = sum of:
        0.010148063 = weight(_text_:a in 2022) [ClassicSimilarity], result of:
          0.010148063 = score(doc=2022,freq=18.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.19109234 = fieldWeight in 2022, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2022)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Logic and precision are fundamental to ontologies underlying the semantic web and, by extension, to linked data. This special section focuses on the interaction of semantics, ontologies and linked data. The discussion presents the Simple Knowledge Organization Scheme (SKOS) as a less formal strategy for expressing concept hierarchies and associations and questions the value of deep domain ontologies in favor of simpler vocabularies that are more open to reuse, albeit risking illogical outcomes. RDF ontologies harbor another unexpected drawback. While structurally sound, they leave validation gaps permitting illogical uses, a problem being addressed by a W3C Working Group. Data models based on RDF graphs and properties may replace traditional library catalog models geared to predefined entities, with relationships between RDF classes providing the semantic connections. The BIBFRAME Initiative takes a different and streamlined approach to linking data, building rich networks of information resources rather than relying on a strict underlying structure and vocabulary. Taken together, the articles illustrate the trend toward a pragmatic approach to a Semantic Web, sacrificing some specificity for greater flexibility and partial interoperability.

Footnote

Introduction to a special section "Linked data and the charm of weak semantics".

Type

a

0.0024857575 = product of:
  0.004971515 = sum of:
    0.004971515 = product of:
      0.00994303 = sum of:
        0.00994303 = weight(_text_:a in 4798) [ClassicSimilarity], result of:
          0.00994303 = score(doc=4798,freq=12.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18723148 = fieldWeight in 4798, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=4798)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Seven years on from OWL becoming a W3C recommendation, and two years on from the more recent OWL 2 W3C recommendation, OWL has still experienced only patchy uptake on the Web. Although certain OWL features (like owl:sameAs) are very popular, other features of OWL are largely neglected by publishers in the Linked Data world. This may suggest that despite the promise of easy implementations and the proposal of tractable profiles suggested in OWL's second version, there is still no "right" standard fragment for the Linked Data community. In this paper, we (1) analyse uptake of OWL on the Web of Data, (2) gain insights into the OWL fragment that is actually used/usable on the Web, where we arrive at the conclusion that this fragment is likely to be a simplified profile based on OWL RL, (3) propose and discuss such a new fragment, which we call OWL LD (for Linked Data).

Type

a

0.0024857575 = product of:
  0.004971515 = sum of:
    0.004971515 = product of:
      0.00994303 = sum of:
        0.00994303 = weight(_text_:a in 3928) [ClassicSimilarity], result of:
          0.00994303 = score(doc=3928,freq=12.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18723148 = fieldWeight in 3928, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=3928)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this tutorial we address the problem of ontology querying, that is, the problem of answering queries against a theory constituted by facts (the data) and inference rules (the ontology). A varied landscape of ontology languages exists in the scientific literature, with several degrees of complexity of query processing. We argue that Datalog±, a family of languages derived from Datalog, is a powerful tool for ontology querying. To illustrate the impact of this comeback of Datalog, we present the basic paradigms behind the main Datalog± as well as some recent extensions. We also present some efficient query processing techniques for some cases.

Type

a

0.0024392908 = product of:
  0.0048785815 = sum of:
    0.0048785815 = product of:
      0.009757163 = sum of:
        0.009757163 = weight(_text_:a in 436) [ClassicSimilarity], result of:
          0.009757163 = score(doc=436,freq=26.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18373153 = fieldWeight in 436, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=436)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Peter and Alice are at home, it is a calm winter night, snow is falling, and it is too cold to go outside. "Why don't we just order a pizza and watch a movie?" says Alice wrapped in her favorite blanket. "Why not?"-Peter replies-"Which movie do you wanna watch?" "Well, what about some comedy, romance-like one? Com'on Pete, look on Facebook, there is that nice application Kara suggested me some days ago!" answers Alice. "Oh yes, MORE, here we go, tell me a movie you like a lot," says Peter excited. "Uhm, I wanna see something like the Bridget Jones's Diary or Four Weddings and a Funeral, humour, romance, good actors..." replies his beloved, rubbing her hands. Peter is a bit concerned, he is more into fantasy genre, but he wants to please Alice, so he looks on MORE for movies similar to the Bridget Jones's Diary and Four Weddings and a Funeral: "Here we are my dear, MORE suggests the sequel or, if you prefer, Love Actually," I would prefer the second." "Great! Let's rent it!" nods Peter in agreement. The scenario just presented highlights an interesting and useful feature of a modern Web application. There are tasks where the users look for items similar to the ones they already know. Hence, we need systems that recommend items based on user preferences. In other words, systems should allow an easy and friendly exploration of the information/data related to a particular domain of interest. Such characteristics are well known in the literature and in common applications such as recommender systems. Nevertheless, new challenges in this field arise whenthe information used by these systems exploits the huge amount of interlinked data coming from the Semantic Web. In this chapter, we present MORE, a system for 'movie recommendation' in the Web of Data.

0.0023919214 = product of:
  0.0047838427 = sum of:
    0.0047838427 = product of:
      0.009567685 = sum of:
        0.009567685 = weight(_text_:a in 4293) [ClassicSimilarity], result of:
          0.009567685 = score(doc=4293,freq=16.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18016359 = fieldWeight in 4293, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4293)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In recent years, the term "semantics" has been widely used in various fields of research and particularly in areas related to information technology. One of the motivators of such an appropriation is the vision of the Semantic Web, a set of developments underway, which might allow one to obtain better results when querying on the web. However, it is worth asking what kind of semantics we can find in the Semantic Web, considering that studying the subject is a complex and controversial endeavor. Working within this context, we present an account of semantics, relying on the main linguist approaches, in order to then analyze what semantics is within the scope of information technology. We critically evaluate a spectrum, which proposes the ordination of instruments (models, languages, taxonomic structures, to mention but a few) according to a semantic scale. In addition to proposing a new extended spectrum, we suggest alternative interpretations with the aim of clarifying the use of the term "semantics" in different contexts. Finally, we offer our conclusions regarding the semantic in the Semantic Web and mention future directions and complementary works.

Type

a

0.0023919214 = product of:
  0.0047838427 = sum of:
    0.0047838427 = product of:
      0.009567685 = sum of:
        0.009567685 = weight(_text_:a in 112) [ClassicSimilarity], result of:
          0.009567685 = score(doc=112,freq=16.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18016359 = fieldWeight in 112, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=112)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Over the past 3 years, the semantic web activity has gained momentum with the widespread publishing of structured data as RDF. The Linked Data paradigm has therefore evolved from a practical research idea into a very promising candidate for addressing one of the biggest challenges in the area of the Semantic Web vision: the exploitation of the Web as a platform for data and information integration. To translate this initial success into a world-scale reality, a number of research challenges need to be addressed: the performance gap between relational and RDF data management has to be closed, coherence and quality of data published on theWeb have to be improved, provenance and trust on the Linked Data Web must be established and generally the entrance barrier for data publishers and users has to be lowered. In this vision statement we discuss these challenges and argue, that research approaches tackling these challenges should be integrated into a mutual refinement cycle. We also present two crucial use-cases for the widespread adoption of linked data.

Type

a

0.0023919214 = product of:
  0.0047838427 = sum of:
    0.0047838427 = product of:
      0.009567685 = sum of:
        0.009567685 = weight(_text_:a in 814) [ClassicSimilarity], result of:
          0.009567685 = score(doc=814,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18016359 = fieldWeight in 814, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.078125 = fieldNorm(doc=814)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Location

A

Type

a

0.0023919214 = product of:
  0.0047838427 = sum of:
    0.0047838427 = product of:
      0.009567685 = sum of:
        0.009567685 = weight(_text_:a in 2292) [ClassicSimilarity], result of:
          0.009567685 = score(doc=2292,freq=16.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18016359 = fieldWeight in 2292, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2292)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Providing efficient access to information is a crucial library mission. Subject classification is one of the major pillars that guarantees the accessibility of records in libraries. In this paper we discuss the need to associate person IDs and URIs with subjects when a named person happens to be the subject of the document. This is often the case with biographies, schools of thought in philosophy, politics, art, and literary criticism. Using Semantic Web compliant data in subject name headings enhances the ability to collocate topics about a person. Also, in retrieval, books about a person would be easily linked to works by that same person. In the context of the Semantic Web, it is expected that, as the available information grows, one would be more effective in the task of information retrieval. Information about a person or, as in the case of this paper, about a researcher exist in various databases, which can be discipline specific or publishers' databases, and in such cases they have an assigned identifier. They also exist in institutional directory databases. We argue that these various databases can be leveraged to support improved discoverability and retrieval of research output for individual authors and institutions, as well as works about those authors.

Source

Classification and authority control: expanding resource discovery: proceedings of the International UDC Seminar 2015, 29-30 October 2015, Lisbon, Portugal. Eds.: Slavic, A. u. M.I. Cordeiro

Type

a

0.0023919214 = product of:
  0.0047838427 = sum of:
    0.0047838427 = product of:
      0.009567685 = sum of:
        0.009567685 = weight(_text_:a in 5300) [ClassicSimilarity], result of:
          0.009567685 = score(doc=5300,freq=16.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18016359 = fieldWeight in 5300, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5300)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The web has been, in the last decades, the place where information retrieval achieved its maximum importance, given its ubiquity and the sheer volume of information. However, its exponential growth made the retrieval task increasingly hard, relying in its effectiveness on idiosyncratic and somewhat biased ranking algorithms. To deal with this problem, a "new" web, called the Semantic Web (SW), was proposed, bringing along concepts like "Web of Data" and "Linked Data," although the definitions and connections among these concepts are often unclear. Based on a qualitative approach built over a literature review, a definition of SW is presented, discussing the related concepts sometimes used as synonyms. It concludes that the SW is a comprehensive and ambitious construct that includes the great purpose of making the web a global database. It also follows the specifications developed and/or associated with its operationalization and the necessary procedures for the connection of data in an open format on the web. The goals of this comprehensive SW are the union of two outcomes still tenuously connected: the virtually unlimited possibility of connections between data-the web domain-with the potentiality of the automated inference of "intelligent" systems-the semantic component.

Type

a

0.0023678814 = product of:
  0.0047357627 = sum of:
    0.0047357627 = product of:
      0.009471525 = sum of:
        0.009471525 = weight(_text_:a in 62) [ClassicSimilarity], result of:
          0.009471525 = score(doc=62,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17835285 = fieldWeight in 62, 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=62)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The paper discusses the challenges of categorising documents and "items of the world" to promote knowledge sharing in large communities of interest. We present the DOCMA method (Document and Item-based Model for Action) dedicated to end-users who have minimal or no knowledge of information science. Community members can elicit structure and indexed business items stemming from their query including projects, actors, products, places of interest, and geo-situated objects. This hybrid method was applied in a collaborative Web portal in the field of sustainability for the past two years.

Type

a

0.0023678814 = product of:
  0.0047357627 = sum of:
    0.0047357627 = product of:
      0.009471525 = sum of:
        0.009471525 = weight(_text_:a in 4172) [ClassicSimilarity], result of:
          0.009471525 = score(doc=4172,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17835285 = fieldWeight in 4172, 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=4172)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Purpose - The purpose of this paper is to find a relationship between traditional faceted classification schemes and semantic web document annotators, particularly in the linked data environment. Design/methodology/approach - A consideration of the conceptual ideas behind faceted classification and linked data architecture is made. Analysis of selected web documents is performed using Calais' Semantic Proxy to support the considerations. Findings - Technical language aside, the principles of both approaches are very similar. Modern classification techniques have the potential to automatically generate metadata to drive more precise information recall by including a semantic layer. Originality/value - Linked data have not been explicitly considered in this context before in the published literature.

Type

a

0.0023678814 = product of:
  0.0047357627 = sum of:
    0.0047357627 = product of:
      0.009471525 = sum of:
        0.009471525 = weight(_text_:a in 1928) [ClassicSimilarity], result of:
          0.009471525 = score(doc=1928,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17835285 = fieldWeight in 1928, 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=1928)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Each of the FR family of models has been represented in Resource Description Framework (RDF), the basis of the Semantic Web. This has involved analysis of the entity-relationship diagrams and text of the models to identify and create the RDF classes, properties, definitions and scope notes required. The work has shown that it is possible to seamlessly connect the models within a semantic framework, specifically in the treatment of names, identifiers, and subjects, and link the RDF elements to those in related namespaces.

Content

Contribution to a special issue "The FRBR family of conceptual models: toward a linked future"

Type

a

0.0023678814 = product of:
  0.0047357627 = sum of:
    0.0047357627 = product of:
      0.009471525 = sum of:
        0.009471525 = weight(_text_:a in 1999) [ClassicSimilarity], result of:
          0.009471525 = score(doc=1999,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17835285 = fieldWeight in 1999, 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=1999)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The article is an updated and expanded version of a paper presented to International Federation of Library Associations and Institutions in 2013. It describes recent work involving the representation of International Standard for Bibliographic Description (ISBD) and UNIMARC (UNIversal MARC) in Resource Description Framework (RDF), the basis of the Semantic Web and linked data. The UNIMARC Bibliographic format is used to illustrate issues arising from the development of a bibliographic element set and its semantic alignment with ISBD. The article discusses the use of such alignments in the automated processing of linked data for interoperability, using examples from ISBD, UNIMARC, and Resource Description and Access.

Footnote

Contribution in a special issue "ISBD: The Bibliographic Content Standard "

Type

a

0.0023678814 = product of:
  0.0047357627 = sum of:
    0.0047357627 = product of:
      0.009471525 = sum of:
        0.009471525 = weight(_text_:a in 5798) [ClassicSimilarity], result of:
          0.009471525 = score(doc=5798,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17835285 = fieldWeight in 5798, 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=5798)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This paper presents the idea and our work of extracting and reassembling a genealogical network automatically from a collection of biographies. The network can be used as a tool for network analysis of historical persons. The data has been published as Linked Data and as an interactive online service as part of the in-use data service and semantic portal BiographySampo - Finnish Biographies on the Semantic Web.

Type

a

0.0022926913 = product of:
  0.0045853825 = sum of:
    0.0045853825 = product of:
      0.009170765 = sum of:
        0.009170765 = weight(_text_:a in 4803) [ClassicSimilarity], result of:
          0.009170765 = score(doc=4803,freq=30.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17268941 = fieldWeight in 4803, product of:
              5.477226 = tf(freq=30.0), with freq of:
                30.0 = termFreq=30.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.02734375 = fieldNorm(doc=4803)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Thanks to the recent Linked Data initiative, the foundations of the Semantic Web have been built. Shared, open and linked RDF datasets give us the possibility to exploit both the strong theoretical results and the robust technologies and tools developed since the seminal paper in the Semantic Web appeared in 2001. In a simplistic way, we may think at the Semantic Web as a ultra large distributed database we can query to get information coming from different sources. In fact, every dataset exposes a SPARQL endpoint to make the data accessible through exact queries. If we know the URI of the famous actress Nicole Kidman in DBpedia we may retrieve all the movies she acted with a simple SPARQL query. Eventually we may aggregate this information with users ratings and genres from IMDB. Even though these are very exciting results and applications, there is much more behind the curtains. Datasets come with the description of their schema structured in an ontological way. Resources refer to classes which are in turn organized in well structured and rich ontologies. Exploiting also this further feature we go beyond the notion of a distributed database and we can refer to the Semantic Web as a distributed knowledge base. If in our knowledge base we have that Paris is located in France (ontological level) and that Moulin Rouge! is set in Paris (data level) we may query the Semantic Web (interpreted as a set of interconnected datasets and related ontologies) to return all the movies starred by Nicole Kidman set in France and Moulin Rouge! will be in the final result set. The ontological level makes possible to infer new relations among data.
The Linked Data initiative and the state of the art in semantic technologies led off all brand new search and mash-up applications. The basic idea is to have smarter lookup services for a huge, distributed and social knowledge base. All these applications catch and (re)propose, under a semantic data perspective, the view of the classical Web as a distributed collection of documents to retrieve. The interlinked nature of the Web, and consequently of the Semantic Web, is exploited (just) to collect and aggregate data coming from different sources. Of course, this is a big step forward in search and Web technologies, but if we limit our investi- gation to retrieval tasks, we miss another important feature of the current Web: browsing and in particular exploratory browsing (a.k.a. exploratory search). Thanks to its hyperlinked nature, the Web defined a new way of browsing documents and knowledge: selection by lookup, navigation and trial-and-error tactics were, and still are, exploited by users to search for relevant information satisfying some initial requirements. The basic assumptions behind a lookup search, typical of Information Retrieval (IR) systems, are no more valid in an exploratory browsing context. An IR system, such as a search engine, assumes that: the user has a clear picture of what she is looking for ; she knows the terminology of the specific knowledge space. On the other side, as argued in, the main challenges in exploratory search can be summarized as: support querying and rapid query refinement; other facets and metadata-based result filtering; leverage search context; support learning and understanding; other visualization to support insight/decision making; facilitate collaboration. In Section 3 we will show two applications for exploratory search in the Semantic Web addressing some of the above challenges.

0.002269176 = product of:
  0.004538352 = sum of:
    0.004538352 = product of:
      0.009076704 = sum of:
        0.009076704 = weight(_text_:a in 4067) [ClassicSimilarity], result of:
          0.009076704 = score(doc=4067,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1709182 = fieldWeight in 4067, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=4067)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Controlled terminologies such as classification schemes, name authorities, and thesauri have long been the domain of the library and information science community. Although historically there have been initiatives towards library style classification of web resources, there remain significant problems with searching and quality judgement of online content. Terminology services can play a key role in opening up access to these valuable resources. By exposing controlled terminologies via a web service, organisations maintain data integrity and version control, whilst motivating external users to design innovative ways to present and utilise their data. We introduce terminology web services and review work in the area. We describe the approaches taken in establishing application programming interfaces (API) and discuss the comparative benefits of a dedicated terminology web service versus general purpose programming languages. We discuss experiences at Glamorgan in creating terminology web services and associated client interface components, in particular for the archaeology domain in the STAR (Semantic Technologies for Archaeological Resources) Project.

Content

Teil von: Papers from Classification at a Crossroads: Multiple Directions to Usability: International UDC Seminar 2009-Part 2

Type

a

0.002269176 = product of:
  0.004538352 = sum of:
    0.004538352 = product of:
      0.009076704 = sum of:
        0.009076704 = weight(_text_:a in 1904) [ClassicSimilarity], result of:
          0.009076704 = score(doc=1904,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1709182 = fieldWeight in 1904, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1904)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Libraries and museums developed rules for the description of their collections prior to formalizing the underlying conceptualization reflected in such rules. That formalizing process took place in the 1990s and resulted in two independent conceptual models: FRBR for bibliographic information (published in 1998), and CIDOC CRM for museum information (developed from 1996 on, and issued as ISO standard 21127 in 2006). An international working group was formed in 2003 with the purpose of harmonizing these two models. The resulting model, FRBROO, was published in 2009. It is an extension to CIDOC CRM, using the formalism in which the former is written. It adds to FRBR the dynamic aspects of CIDOC CRM, and a number of refinements (e.g. in the definitions of Work and Manifestation). Some modifications were made in CIDOC CRM as well. FRBROO was developed with Semantic Web technologies in mind, and lends itself well to the Linked Data environment; but will it be used in that context?

Content

Contribution to a special issue "The FRBR family of conceptual models: toward a linked future"

Type

a

0.0022438213 = product of:
  0.0044876426 = sum of:
    0.0044876426 = product of:
      0.008975285 = sum of:
        0.008975285 = weight(_text_:a in 4725) [ClassicSimilarity], result of:
          0.008975285 = score(doc=4725,freq=22.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.16900843 = fieldWeight in 4725, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=4725)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes - the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study.

0.0022374375 = product of:
  0.004474875 = sum of:
    0.004474875 = product of:
      0.00894975 = sum of:
        0.00894975 = weight(_text_:a in 4749) [ClassicSimilarity], result of:
          0.00894975 = score(doc=4749,freq=14.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1685276 = fieldWeight in 4749, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4749)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Overlapping structures in XML are not symptoms of a misunderstanding of the intrinsic characteristics of a text document nor evidence of extreme scholarly requirements far beyond those needed by the most common XML-based applications. On the contrary, overlaps have started to appear in a large number of incredibly popular applications hidden under the guise of syntactical tricks to the basic hierarchy of the XML data format. Unfortunately, syntactical tricks have the drawback that the affected structures require complicated workarounds to support even the simplest query or usage. In this article, we present Extremely Annotational Resource Description Framework (RDF) Markup (EARMARK), an approach to overlapping markup that simplifies and streamlines the management of multiple hierarchies on the same content, and provides an approach to sophisticated queries and usages over such structures without the need of ad-hoc applications, simply by using Semantic Web tools and languages. We compare how relevant tasks (e.g., the identification of the contribution of an author in a word processor document) are of some substantial complexity when using the original data format and become more or less trivial when using EARMARK. We finally evaluate positively the memory and disk requirements of EARMARK documents in comparison to Open Office and Microsoft Word XML-based formats.

Type

a

0.0022374375 = product of:
  0.004474875 = sum of:
    0.004474875 = product of:
      0.00894975 = sum of:
        0.00894975 = weight(_text_:a in 98) [ClassicSimilarity], result of:
          0.00894975 = score(doc=98,freq=14.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1685276 = fieldWeight in 98, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=98)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The unstructured design of Web resources favors human comprehension, but makes difficult the automatic exploitation of the contents of these resources by machines. So, the Semantic Web aims at making the cooperation between human and machine possible, by giving any information a well defined meaning. The first weavings of the Semantic Web are already prepared. Machines become able to treat and understand the data that were accustomed to only visualization, by using ontologies constitute an essential element of the Semantic Web, as they serve as a form of knowledge representation, sharing, and reuse. However, the Web content is subject to imperfection, and crisp ontologies become less suitable to represent concepts with imprecise definitions. To overcome this problem, fuzzy ontologies constitute a promising research orientation. Indeed, the definition of fuzzy ontologies components constitutes an issue that needs to be well treated. It is necessary to have an appropriate methodology of building an operationalization of fuzzy ontological models. This chapter defines a fuzzy ontological model based on fuzzy description logic. This model uses a new approach for the formal description of fuzzy ontologies. This new methodology shows how all the basic components defined for fuzzy ontologies can be constructed.

Type

a