Search (189 results, page 1 of 10)

  • × theme_ss:"Wissensrepräsentation"
  1. Davies, J.; Weeks, R.; Krohn, U.: QuizRDF: search technology for the Semantic Web (2004) 0.06
    0.06499224 = product of:
      0.09748836 = sum of:
        0.078233026 = weight(_text_:resources in 4316) [ClassicSimilarity], result of:
          0.078233026 = score(doc=4316,freq=6.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.4191312 = fieldWeight in 4316, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.046875 = fieldNorm(doc=4316)
        0.01925533 = product of:
          0.03851066 = sum of:
            0.03851066 = weight(_text_:management in 4316) [ClassicSimilarity], result of:
              0.03851066 = score(doc=4316,freq=2.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.22344214 = fieldWeight in 4316, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4316)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RDF annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the Semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.
  2. Ziemba, L.: Information retrieval with concept discovery in digital collections for agriculture and natural resources (2011) 0.06
    0.06127527 = product of:
      0.0919129 = sum of:
        0.0737588 = weight(_text_:resources in 4728) [ClassicSimilarity], result of:
          0.0737588 = score(doc=4728,freq=12.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.39516068 = fieldWeight in 4728, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.03125 = fieldNorm(doc=4728)
        0.018154101 = product of:
          0.036308203 = sum of:
            0.036308203 = weight(_text_:management in 4728) [ClassicSimilarity], result of:
              0.036308203 = score(doc=4728,freq=4.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.21066327 = fieldWeight in 4728, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.03125 = fieldNorm(doc=4728)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    The amount and complexity of information available in a digital form is already huge and new information is being produced every day. Retrieving information relevant to address a particular need becomes a significant issue. This work utilizes knowledge organization systems (KOS), such as thesauri and ontologies and applies information extraction (IE) and computational linguistics (CL) techniques to organize, manage and retrieve information stored in digital collections in the agricultural domain. Two real world applications of the approach have been developed and are available and actively used by the public. An ontology is used to manage the Water Conservation Digital Library holding a dynamic collection of various types of digital resources in the domain of urban water conservation in Florida, USA. The ontology based back-end powers a fully operational web interface, available at http://library.conservefloridawater.org. The system has demonstrated numerous benefits of the ontology application, including accurate retrieval of resources, information sharing and reuse, and has proved to effectively facilitate information management. The major difficulty encountered with the approach is that large and dynamic number of concepts makes it difficult to keep the ontology consistent and to accurately catalog resources manually. To address the aforementioned issues, a combination of IE and CL techniques, such as Vector Space Model and probabilistic parsing, with the use of Agricultural Thesaurus were adapted to automatically extract concepts important for each of the texts in the Best Management Practices (BMP) Publication Library--a collection of documents in the domain of agricultural BMPs in Florida available at http://lyra.ifas.ufl.edu/LIB. A new approach of domain-specific concept discovery with the use of Internet search engine was developed. Initial evaluation of the results indicates significant improvement in precision of information extraction. The approach presented in this work focuses on problems unique to agriculture and natural resources domain, such as domain specific concepts and vocabularies, but should be applicable to any collection of texts in digital format. It may be of potential interest for anyone who needs to effectively manage a collection of digital resources.
  3. Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014) 0.06
    0.060883917 = product of:
      0.09132587 = sum of:
        0.075279765 = weight(_text_:resources in 2670) [ClassicSimilarity], result of:
          0.075279765 = score(doc=2670,freq=8.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.40330917 = fieldWeight in 2670, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2670)
        0.016046109 = product of:
          0.032092217 = sum of:
            0.032092217 = weight(_text_:management in 2670) [ClassicSimilarity], result of:
              0.032092217 = score(doc=2670,freq=2.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.18620178 = fieldWeight in 2670, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2670)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.
    Source
    Information processing and management. 50(2014) no.1, S.132-155
  4. Pepper, S.: ¬The TAO of topic maps : finding the way in the age of infoglut (2002) 0.06
    0.056310337 = product of:
      0.0844655 = sum of:
        0.052695833 = weight(_text_:resources in 4724) [ClassicSimilarity], result of:
          0.052695833 = score(doc=4724,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.28231642 = fieldWeight in 4724, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4724)
        0.031769674 = product of:
          0.06353935 = sum of:
            0.06353935 = weight(_text_:management in 4724) [ClassicSimilarity], result of:
              0.06353935 = score(doc=4724,freq=4.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.36866072 = fieldWeight in 4724, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4724)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Topic maps are a new ISO standard for describing knowledge structures and associating them with information resources. As such they constitute an enabling technology for knowledge management. Dubbed "the GPS of the information universe", topic maps are also destined to provide powerful new ways of navigating large and interconnected corpora. While it is possible to represent immensely complex structures using topic maps, the basic concepts of the model - Topics, Associations, and Occurrences (TAO) - are easily grasped. This paper provides a non-technical introduction to these and other concepts (the IFS and BUTS of topic maps), relating them to things that are familiar to all of us from the realms of publishing and information management, and attempting to convey some idea of the uses to which topic maps will be put in the future.
  5. Davies, J.; Weeks, R.: QuizRDF: search technology for the Semantic Web (2004) 0.05
    0.0541602 = product of:
      0.0812403 = sum of:
        0.06519419 = weight(_text_:resources in 4320) [ClassicSimilarity], result of:
          0.06519419 = score(doc=4320,freq=6.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.349276 = fieldWeight in 4320, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4320)
        0.016046109 = product of:
          0.032092217 = sum of:
            0.032092217 = weight(_text_:management in 4320) [ClassicSimilarity], result of:
              0.032092217 = score(doc=4320,freq=2.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.18620178 = fieldWeight in 4320, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4320)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RD annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the Semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.
  6. Davies, J.; Weeks, R.; Krohn, U.: QuizRDF: search technology for the Semantic Web (2004) 0.05
    0.052251942 = product of:
      0.07837791 = sum of:
        0.06022381 = weight(_text_:resources in 4406) [ClassicSimilarity], result of:
          0.06022381 = score(doc=4406,freq=8.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.32264733 = fieldWeight in 4406, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.03125 = fieldNorm(doc=4406)
        0.018154101 = product of:
          0.036308203 = sum of:
            0.036308203 = weight(_text_:management in 4406) [ClassicSimilarity], result of:
              0.036308203 = score(doc=4406,freq=4.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.21066327 = fieldWeight in 4406, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.03125 = fieldNorm(doc=4406)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Important information is often scattered across Web and/or intranet resources. Traditional search engines return ranked retrieval lists that offer little or no information on the semantic relationships among documents. Knowledge workers spend a substantial amount of their time browsing and reading to find out how documents are related to one another and where each falls into the overall structure of the problem domain. Yet only when knowledge workers begin to locate the similarities and differences among pieces of information do they move into an essential part of their work: building relationships to create new knowledge. Information retrieval traditionally focuses on the relationship between a given query (or user profile) and the information store. On the other hand, exploitation of interrelationships between selected pieces of information (which can be facilitated by the use of ontologies) can put otherwise isolated information into a meaningful context. The implicit structures so revealed help users use and manage information more efficiently. Knowledge management tools are needed that integrate the resources dispersed across Web resources into a coherent corpus of interrelated information. Previous research in information integration has largely focused on integrating heterogeneous databases and knowledge bases, which represent information in a highly structured way, often by means of formal languages. In contrast, the Web consists to a large extent of unstructured or semi-structured natural language texts. As we have seen, ontologies offer an alternative way to cope with heterogeneous representations of Web resources. The domain model implicit in an ontology can be taken as a unifying structure for giving information a common representation and semantics. Once such a unifying structure exists, it can be exploited to improve browsing and retrieval performance in information access tools. QuizRDF is an example of such a tool.
    Source
    Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a
  7. Kruk, S.R.; Westerki, A.; Kruk, E.: Architecture of semantic digital libraries (2009) 0.05
    0.048266005 = product of:
      0.072399005 = sum of:
        0.045167856 = weight(_text_:resources in 3379) [ClassicSimilarity], result of:
          0.045167856 = score(doc=3379,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.2419855 = fieldWeight in 3379, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.046875 = fieldNorm(doc=3379)
        0.027231153 = product of:
          0.054462306 = sum of:
            0.054462306 = weight(_text_:management in 3379) [ClassicSimilarity], result of:
              0.054462306 = score(doc=3379,freq=4.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.31599492 = fieldWeight in 3379, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3379)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    The main motivation of this chapter was to gather existing requirements and solutions, and to present a generic architectural design of semantic digital libraries. This design is meant to answer a number of requirements, such as interoperability or ability to exchange resources and solutions, and set up the foundations for the best practices in the new domain of semantic digital libraries. We start by presenting the library from different high-level perspectives, i.e., user (see Sect. 2) and metadata (see Sect. 1) perspective; this overview narrows the scope and puts emphasis on certain aspects related to the system perspective, i.e., the architecture of the actual digital library management system. We conclude by presenting the system architecture from three perspectives: top-down layered architecture (see Sect. 3), vertical architecture of core services (see Sect. 4), and stack of enabling infrastructures (see Sect. 5); based upon the observations and evaluation of the contemporary state of the art presented in the previous sections, these last three subsections will describe an in-depth model of the digital library management system.
  8. Breslin, J.G.: Social semantic information spaces (2009) 0.05
    0.04618463 = product of:
      0.069276944 = sum of:
        0.053230833 = weight(_text_:resources in 3377) [ClassicSimilarity], result of:
          0.053230833 = score(doc=3377,freq=4.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.28518265 = fieldWeight in 3377, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3377)
        0.016046109 = product of:
          0.032092217 = sum of:
            0.032092217 = weight(_text_:management in 3377) [ClassicSimilarity], result of:
              0.032092217 = score(doc=3377,freq=2.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.18620178 = fieldWeight in 3377, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3377)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    The structural and syntactic web put in place in the early 90s is still much the same as what we use today: resources (web pages, files, etc.) connected by untyped hyperlinks. By untyped, we mean that there is no easy way for a computer to figure out what a link between two pages means - for example, on the W3C website, there are hundreds of links to the various organisations that are registered members of the association, but there is nothing explicitly saying that the link is to an organisation that is a "member of" the W3C or what type of organisation is represented by the link. On John's work page, he links to many papers he has written, but it does not explicitly say that he is the author of those papers or that he wrote such-and-such when he was working at a particular university. In fact, the Web was envisaged to be much more, as one can see from the image in Fig. 1 which is taken from Tim Berners Lee's original outline for the Web in 1989, entitled "Information Management: A Proposal". In this, all the resources are connected by links describing the type of relationships, e.g. "wrote", "describe", "refers to", etc. This is a precursor to the Semantic Web which we will come back to later.
  9. Davies, J.; Duke, A.; Stonkus, A.: OntoShare: evolving ontologies in a knowledge sharing system (2004) 0.04
    0.04465293 = product of:
      0.06697939 = sum of:
        0.037261583 = weight(_text_:resources in 4409) [ClassicSimilarity], result of:
          0.037261583 = score(doc=4409,freq=4.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.19962786 = fieldWeight in 4409, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.02734375 = fieldNorm(doc=4409)
        0.029717812 = product of:
          0.059435625 = sum of:
            0.059435625 = weight(_text_:management in 4409) [ClassicSimilarity], result of:
              0.059435625 = score(doc=4409,freq=14.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.34485054 = fieldWeight in 4409, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.02734375 = fieldNorm(doc=4409)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    We saw in the introduction how the Semantic Web makes possible a new generation of knowledge management tools. We now turn our attention more specifically to Semantic Web based support for virtual communities of practice. The notion of communities of practice has attracted much attention in the field of knowledge management. Communities of practice are groups within (or sometimes across) organizations who share a common set of information needs or problems. They are typically not a formal organizational unit but an informal network, each sharing in part a common agenda and shared interests or issues. In one example it was found that a lot of knowledge sharing among copier engineers took place through informal exchanges, often around a water cooler. As well as local, geographically based communities, trends towards flexible working and globalisation have led to interest in supporting dispersed communities using Internet technology. The challenge for organizations is to support such communities and make them effective. Provided with an ontology meeting the needs of a particular community of practice, knowledge management tools can arrange knowledge assets into the predefined conceptual classes of the ontology, allowing more natural and intuitive access to knowledge. Knowledge management tools must give users the ability to organize information into a controllable asset. Building an intranet-based store of information is not sufficient for knowledge management; the relationships within the stored information are vital. These relationships cover such diverse issues as relative importance, context, sequence, significance, causality and association. The potential for knowledge management tools is vast; not only can they make better use of the raw information already available, but they can sift, abstract and help to share new information, and present it to users in new and compelling ways.
    In this chapter, we describe the OntoShare system which facilitates and encourages the sharing of information between communities of practice within (or perhaps across) organizations and which encourages people - who may not previously have known of each other's existence in a large organization - to make contact where there are mutual concerns or interests. As users contribute information to the community, a knowledge resource annotated with meta-data is created. Ontologies defined using the resource description framework (RDF) and RDF Schema (RDFS) are used in this process. RDF is a W3C recommendation for the formulation of meta-data for WWW resources. RDF(S) extends this standard with the means to specify domain vocabulary and object structures - that is, concepts and the relationships that hold between them. In the next section, we describe in detail the way in which OntoShare can be used to share and retrieve knowledge and how that knowledge is represented in an RDF-based ontology. We then proceed to discuss in Section 10.3 how the ontologies in OntoShare evolve over time based on user interaction with the system and motivate our approach to user-based creation of RDF-annotated information resources. The way in which OntoShare can help to locate expertise within an organization is then described, followed by a discussion of the sociotechnical issues of deploying such a tool. Finally, a planned evaluation exercise and avenues for further research are outlined.
    Source
    Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a
  10. Gendt, M. van; Isaac, I.; Meij, L. van der; Schlobach, S.: Semantic Web techniques for multiple views on heterogeneous collections : a case study (2006) 0.04
    0.04396772 = product of:
      0.06595158 = sum of:
        0.045167856 = weight(_text_:resources in 2418) [ClassicSimilarity], result of:
          0.045167856 = score(doc=2418,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.2419855 = fieldWeight in 2418, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.046875 = fieldNorm(doc=2418)
        0.020783724 = product of:
          0.04156745 = sum of:
            0.04156745 = weight(_text_:22 in 2418) [ClassicSimilarity], result of:
              0.04156745 = score(doc=2418,freq=2.0), product of:
                0.17906146 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.051133685 = queryNorm
                0.23214069 = fieldWeight in 2418, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2418)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Integrated digital access to multiple collections is a prominent issue for many Cultural Heritage institutions. The metadata describing diverse collections must be interoperable, which requires aligning the controlled vocabularies that are used to annotate objects from these collections. In this paper, we present an experiment where we match the vocabularies of two collections by applying the Knowledge Representation techniques established in recent Semantic Web research. We discuss the steps that are required for such matching, namely formalising the initial resources using Semantic Web languages, and running ontology mapping tools on the resulting representations. In addition, we present a prototype that enables the user to browse the two collections using the obtained alignment while still providing her with the original vocabulary structures.
    Source
    Research and advanced technology for digital libraries : 10th European conference, proceedings / ECDL 2006, Alicante, Spain, September 17 - 22, 2006
  11. Solskinnsbakk, G.; Gulla, J.A.: Contextual search navigation using semantic tag signatures (2011) 0.04
    0.042948794 = product of:
      0.06442319 = sum of:
        0.045167856 = weight(_text_:resources in 1033) [ClassicSimilarity], result of:
          0.045167856 = score(doc=1033,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.2419855 = fieldWeight in 1033, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.046875 = fieldNorm(doc=1033)
        0.01925533 = product of:
          0.03851066 = sum of:
            0.03851066 = weight(_text_:management in 1033) [ClassicSimilarity], result of:
              0.03851066 = score(doc=1033,freq=2.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.22344214 = fieldWeight in 1033, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1033)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Search has been and will continue to be an important tool for users who need to locate information in an ever increasing mount of resources. Not all queries have a well defined information need that can easily be described by a keyword query. Exploratory search is one such type of search where the user is not necessarily proficient in the domain or does not have a clear idea of what he is looking for. In such types of search, navigation is beneficial to guide the user in his quest. In this paper we present an approach to contextual navigation search, based on a hierarchical structure constructed from folksonomy tags. The tags are associated with an extended semantic representation used to guide the navigation. Five semantic navigators are introduced, which are navigation strategies the user can benefit from. We present a prototype which has been implemented to show the applicability of the approach to the problem at hand. The preliminary results are promising and demonstrate the ability to direct the user at interesting navigational suggestions and documents.
    Source
    I-KNOW '11: Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, Article 34
  12. De Maio, C.; Fenza, G.; Loia, V.; Senatore, S.: Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis (2012) 0.04
    0.042948794 = product of:
      0.06442319 = sum of:
        0.045167856 = weight(_text_:resources in 2737) [ClassicSimilarity], result of:
          0.045167856 = score(doc=2737,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.2419855 = fieldWeight in 2737, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.046875 = fieldNorm(doc=2737)
        0.01925533 = product of:
          0.03851066 = sum of:
            0.03851066 = weight(_text_:management in 2737) [ClassicSimilarity], result of:
              0.03851066 = score(doc=2737,freq=2.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.22344214 = fieldWeight in 2737, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2737)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Source
    Information processing and management. 48(2012) no.3, S.399-418
  13. Baião Salgado Silva, G.; Lima, G.Â. Borém de Oliveira: Using topic maps in establishing compatibility of semantically structured hypertext contents (2012) 0.04
    0.036639772 = product of:
      0.054959655 = sum of:
        0.037639882 = weight(_text_:resources in 633) [ClassicSimilarity], result of:
          0.037639882 = score(doc=633,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.20165458 = fieldWeight in 633, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0390625 = fieldNorm(doc=633)
        0.017319772 = product of:
          0.034639545 = sum of:
            0.034639545 = weight(_text_:22 in 633) [ClassicSimilarity], result of:
              0.034639545 = score(doc=633,freq=2.0), product of:
                0.17906146 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.051133685 = queryNorm
                0.19345059 = fieldWeight in 633, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=633)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Considering the characteristics of hypertext systems and problems such as cognitive overload and the disorientation of users, this project studies subject hypertext documents that have undergone conceptual structuring using facets for content representation and improvement of information retrieval during navigation. The main objective was to assess the possibility of the application of topic map technology for automating the compatibilization process of these structures. For this purpose, two dissertations from the UFMG Information Science Post-Graduation Program were adopted as samples. Both dissertations had been duly analyzed and structured on the MHTX (Hypertextual Map) prototype database. The faceted structures of both dissertations, which had been represented in conceptual maps, were then converted into topic maps. It was then possible to use the merge property of the topic maps to promote the semantic interrelationship between the maps and, consequently, between the hypertextual information resources proper. The merge results were then analyzed in the light of theories dealing with the compatibilization of languages developed within the realm of information technology and librarianship from the 1960s on. The main goals accomplished were: (a) the detailed conceptualization of the merge process of the topic maps, considering the possible compatibilization levels and the applicability of this technology in the integration of faceted structures; and (b) the production of a detailed sequence of steps that may be used in the implementation of topic maps based on faceted structures.
    Date
    22. 2.2013 11:39:23
  14. Conde, A.; Larrañaga, M.; Arruarte, A.; Elorriaga, J.A.; Roth, D.: litewi: a combined term extraction and entity linking method for eliciting educational ontologies from textbooks (2016) 0.04
    0.036639772 = product of:
      0.054959655 = sum of:
        0.037639882 = weight(_text_:resources in 2645) [ClassicSimilarity], result of:
          0.037639882 = score(doc=2645,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.20165458 = fieldWeight in 2645, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2645)
        0.017319772 = product of:
          0.034639545 = sum of:
            0.034639545 = weight(_text_:22 in 2645) [ClassicSimilarity], result of:
              0.034639545 = score(doc=2645,freq=2.0), product of:
                0.17906146 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.051133685 = queryNorm
                0.19345059 = fieldWeight in 2645, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2645)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Major efforts have been conducted on ontology learning, that is, semiautomatic processes for the construction of domain ontologies from diverse sources of information. In the past few years, a research trend has focused on the construction of educational ontologies, that is, ontologies to be used for educational purposes. The identification of the terminology is crucial to build ontologies. Term extraction techniques allow the identification of the domain-related terms from electronic resources. This paper presents LiTeWi, a novel method that combines current unsupervised term extraction approaches for creating educational ontologies for technology supported learning systems from electronic textbooks. LiTeWi uses Wikipedia as an additional information source. Wikipedia contains more than 30 million articles covering the terminology of nearly every domain in 288 languages, which makes it an appropriate generic corpus for term extraction. Furthermore, given that its content is available in several languages, it promotes both domain and language independence. LiTeWi is aimed at being used by teachers, who usually develop their didactic material from textbooks. To evaluate its performance, LiTeWi was tuned up using a textbook on object oriented programming and then tested with two textbooks of different domains-astronomy and molecular biology.
    Date
    22. 1.2016 12:38:14
  15. Gray, A.J.G.; Gray, N.; Hall, C.W.; Ounis, I.: Finding the right term : retrieving and exploring semantic concepts in astronomical vocabularies (2010) 0.04
    0.035790663 = product of:
      0.053685993 = sum of:
        0.037639882 = weight(_text_:resources in 4235) [ClassicSimilarity], result of:
          0.037639882 = score(doc=4235,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.20165458 = fieldWeight in 4235, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4235)
        0.016046109 = product of:
          0.032092217 = sum of:
            0.032092217 = weight(_text_:management in 4235) [ClassicSimilarity], result of:
              0.032092217 = score(doc=4235,freq=2.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.18620178 = fieldWeight in 4235, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4235)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Astronomy, like many domains, already has several sets of terminology in general use, referred to as controlled vocabularies. For example, the keywords for tagging journal articles, or the taxonomy of terms used to label image files. These existing vocabularies can be encoded into skos, a W3C proposed recommendation for representing vocabularies on the Semantic Web, so that computer systems can help users to search for and discover resources tagged with vocabulary concepts. However, this requires a search mechanism to go from a user-supplied string to a vocabulary concept. In this paper, we present our experiences in implementing the Vocabulary Explorer, a vocabulary search service based on the Terrier Information Retrieval Platform. We investigate the capabilities of existing document weighting models for identifying the correct vocabulary concept for a query. Due to the highly structured nature of a skos encoded vocabulary, we investigate the effects of term weighting (boosting the score of concepts that match on particular fields of a vocabulary concept), and query expansion. We found that the existing document weighting models provided very high quality results, but these could be improved further with the use of term weighting that makes use of the semantic evidence.
    Source
    Information processing and management. 46(2010) no.4, S.470-478
  16. Bertola, F.; Patti, V.: Ontology-based affective models to organize artworks in the social semantic web (2016) 0.04
    0.035790663 = product of:
      0.053685993 = sum of:
        0.037639882 = weight(_text_:resources in 2669) [ClassicSimilarity], result of:
          0.037639882 = score(doc=2669,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.20165458 = fieldWeight in 2669, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2669)
        0.016046109 = product of:
          0.032092217 = sum of:
            0.032092217 = weight(_text_:management in 2669) [ClassicSimilarity], result of:
              0.032092217 = score(doc=2669,freq=2.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.18620178 = fieldWeight in 2669, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2669)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    In this paper, we focus on applying sentiment analysis to resources from online art collections, by exploiting, as information source, tags intended as textual traces that visitors leave to comment artworks on social platforms. We present a framework where methods and tools from a set of disciplines, ranging from Semantic and Social Web to Natural Language Processing, provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions. The ontology is inspired by the Plutchik's circumplex model, a well-founded psychological model of human emotions. Users can be involved in the creation of the emotional space, through a graphical interactive interface. The development of such semantic space enables new ways of accessing and exploring art collections. The affective categorization model and the emotion detection output are encoded into W3C ontology languages. This gives us the twofold advantage to enable tractable reasoning on detected emotions and related artworks, and to foster the interoperability and integration of tools developed in the Semantic Web and Linked Data community. The proposal has been evaluated against a real-word case study, a dataset of tagged multimedia artworks from the ArsMeteo Italian online collection, and validated through a user study.
    Source
    Information processing and management. 52(2016) no.1, S.139-162
  17. Calegari, S.; Pasi, G.: Personal ontologies : generation of user profiles based on the YAGO ontology (2013) 0.04
    0.035790663 = product of:
      0.053685993 = sum of:
        0.037639882 = weight(_text_:resources in 2719) [ClassicSimilarity], result of:
          0.037639882 = score(doc=2719,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.20165458 = fieldWeight in 2719, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2719)
        0.016046109 = product of:
          0.032092217 = sum of:
            0.032092217 = weight(_text_:management in 2719) [ClassicSimilarity], result of:
              0.032092217 = score(doc=2719,freq=2.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.18620178 = fieldWeight in 2719, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2719)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Personalized search is aimed at tailoring the search outcome to users; to this aim user profiles play an important role: the more faithfully a user profile represents the user interests and preferences, the higher is the probability to improve the search process. In the approaches proposed in the literature, user profiles are formally represented as bags of words, as vectors, or as conceptual taxonomies, generally defined based on external knowledge resources (such as the WordNet and the ODP - Open Directory Project). Ontologies have been more recently considered as a powerful expressive means for knowledge representation. The advantage offered by ontological languages is that they allow a more structured and expressive knowledge representation with respect to the above mentioned approaches. A challenging research activity consists in defining user profiles by a knowledge extraction process from an existing ontology, with the main aim of producing a semantically rich representation of the user interests. In this paper a method to automatically define a personal ontology via a knowledge extraction process from the general purpose ontology YAGO is presented; starting from a set of keywords, which are representatives of the user interests, the process is aimed to define a structured and semantically coherent representation of the user topical interests. In the paper the proposed method is described, as well as some evaluations that show its effectiveness.
    Source
    Information processing and management. 49(2013) no.3, S.640-658
  18. Castellanos Ardila, J.P.: Investigation of an OSLC-domain targeting ISO 26262 : focus on the left side of the software V-model (2016) 0.03
    0.03217734 = product of:
      0.04826601 = sum of:
        0.030111905 = weight(_text_:resources in 5819) [ClassicSimilarity], result of:
          0.030111905 = score(doc=5819,freq=2.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.16132367 = fieldWeight in 5819, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.03125 = fieldNorm(doc=5819)
        0.018154101 = product of:
          0.036308203 = sum of:
            0.036308203 = weight(_text_:management in 5819) [ClassicSimilarity], result of:
              0.036308203 = score(doc=5819,freq=4.0), product of:
                0.17235184 = queryWeight, product of:
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.051133685 = queryNorm
                0.21066327 = fieldWeight in 5819, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.3706124 = idf(docFreq=4130, maxDocs=44218)
                  0.03125 = fieldNorm(doc=5819)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Industries have adopted a standardized set of practices for developing their products. In the automotive domain, the provision of safety-compliant systems is guided by ISO 26262, a standard that specifies a set of requirements and recommendations for developing automotive safety-critical systems. For being in compliance with ISO 26262, the safety lifecycle proposed by the standard must be included in the development process of a vehicle. Besides, a safety case that shows that the system is acceptably safe has to be provided. The provision of a safety case implies the execution of a precise documentation process. This process makes sure that the work products are available and traceable. Further, the documentation management is defined in the standard as a mandatory activity and guidelines are proposed/imposed for its elaboration. It would be appropriate to point out that a well-documented safety lifecycle will provide the necessary inputs for the generation of an ISO 26262-compliant safety case. The OSLC (Open Services for Lifecycle Collaboration) standard and the maturing stack of semantic web technologies represent a promising integration platform for enabling semantic interoperability between the tools involved in the safety lifecycle. Tools for requirements, architecture, development management, among others, are expected to interact and shared data with the help of domains specifications created in OSLC. This thesis proposes the creation of an OSLC tool-chain infrastructure for sharing safety-related information, where fragments of safety information can be generated. The steps carried out during the elaboration of this master thesis consist in the identification, representation, and shaping of the RDF resources needed for the creation of a safety case. The focus of the thesis is limited to a tiny portion of the ISO 26262 left-hand side of the V-model, more exactly part 6 clause 8 of the standard: Software unit design and implementation. Regardless of the use of a restricted portion of the standard during the execution of this thesis, the findings can be extended to other parts, and the conclusions can be generalize. This master thesis is considered one of the first steps towards the provision of an OSLC-based and ISO 26262-compliant methodological approach for representing and shaping the work products resulting from the execution of the safety lifecycle, documentation required in the conformation of an ISO-compliant safety case.
  19. Deokattey, S.; Neelameghan, A.; Kumar, V.: ¬A method for developing a domain ontology : a case study for a multidisciplinary subject (2010) 0.03
    0.03114149 = product of:
      0.09342447 = sum of:
        0.09342447 = sum of:
          0.044929106 = weight(_text_:management in 3694) [ClassicSimilarity], result of:
            0.044929106 = score(doc=3694,freq=2.0), product of:
              0.17235184 = queryWeight, product of:
                3.3706124 = idf(docFreq=4130, maxDocs=44218)
                0.051133685 = queryNorm
              0.2606825 = fieldWeight in 3694, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.3706124 = idf(docFreq=4130, maxDocs=44218)
                0.0546875 = fieldNorm(doc=3694)
          0.04849536 = weight(_text_:22 in 3694) [ClassicSimilarity], result of:
            0.04849536 = score(doc=3694,freq=2.0), product of:
              0.17906146 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051133685 = queryNorm
              0.2708308 = fieldWeight in 3694, 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=3694)
      0.33333334 = coord(1/3)
    
    Abstract
    A method to develop a prototype domain ontology has been described. The domain selected for the study is Accelerator Driven Systems. This is a multidisciplinary and interdisciplinary subject comprising Nuclear Physics, Nuclear and Reactor Engineering, Reactor Fuels and Radioactive Waste Management. Since Accelerator Driven Systems is a vast topic, select areas in it were singled out for the study. Both qualitative and quantitative methods such as Content analysis, Facet analysis and Clustering were used, to develop the web-based model.
    Date
    22. 7.2010 19:41:16
  20. Mustafa El Hadi, W.: Terminologies, ontologies and information access (2006) 0.03
    0.028389778 = product of:
      0.08516933 = sum of:
        0.08516933 = weight(_text_:resources in 1488) [ClassicSimilarity], result of:
          0.08516933 = score(doc=1488,freq=4.0), product of:
            0.18665522 = queryWeight, product of:
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.051133685 = queryNorm
            0.45629224 = fieldWeight in 1488, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.650338 = idf(docFreq=3122, maxDocs=44218)
              0.0625 = fieldNorm(doc=1488)
      0.33333334 = coord(1/3)
    
    Abstract
    Ontologies have become an important issue in research communities across several disciplines. This paper discusses some of the innovative techniques involving automatic terminology resources acquisition are briefly discussed. Suggests that NLP-based ontologies are useful in reducing the cost of ontology engineering. Emphasizes that linguistic ontologies covering both ontological and lexical information can offer solutions since they can be more easily updated by the resources of NLP products.

Authors

Years

Languages

  • e 165
  • d 21
  • pt 2
  • More… Less…

Types

  • a 139
  • el 47
  • m 13
  • x 8
  • n 7
  • s 6
  • r 1
  • More… Less…

Subjects