Search (419 results, page 2 of 21)

0.01756874 = product of:
  0.03513748 = sum of:
    0.03513748 = sum of:
      0.009593598 = weight(_text_:a in 635) [ClassicSimilarity], result of:
        0.009593598 = score(doc=635,freq=24.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.22065444 = fieldWeight in 635, product of:
            4.8989797 = tf(freq=24.0), with freq of:
              24.0 = termFreq=24.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=635)
      0.02554388 = weight(_text_:22 in 635) [ClassicSimilarity], result of:
        0.02554388 = score(doc=635,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 635, 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=635)
  0.5 = coord(1/2)

Abstract

Despite the dissemination of modeling languages and tools for representation and construction of ontologies, their underlying methodologies can still be improved. As a consequence, ontology tools can be enhanced accordingly, in order to support users through the ontology construction process. This paper proposes suggestions for ontology tools' improvement based on a case study within the domain of bioinformatics, applying a reuse method ology. Quantitative and qualitative analyses were carried out on a subset of 28 terms of Gene Ontology on a semi-automatic alignment with other biomedical ontologies. As a result, a report is presented containing suggestions for enhancing ontology reuse tools, which is a product derived from difficulties that we had in reusing a set of OBO ontologies. For the reuse process, a set of steps closely related to those of Pinto and Martin's methodology was used. In each step, it was observed that the experiment would have been significantly improved if ontology manipulation tools had provided certain features. Accordingly, problematic aspects in ontology tools are presented and suggestions are made aiming at getting better results in ontology reuse.

Date

22. 2.2013 12:03:53

Type

a

0.017150797 = product of:
  0.034301594 = sum of:
    0.034301594 = sum of:
      0.008757716 = weight(_text_:a in 3403) [ClassicSimilarity], result of:
        0.008757716 = score(doc=3403,freq=20.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.20142901 = fieldWeight in 3403, product of:
            4.472136 = tf(freq=20.0), with freq of:
              20.0 = termFreq=20.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=3403)
      0.02554388 = weight(_text_:22 in 3403) [ClassicSimilarity], result of:
        0.02554388 = score(doc=3403,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 3403, 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=3403)
  0.5 = coord(1/2)

Abstract

What is knowledge representation and reasoning (KR&R)? Alas, a thorough account would require a book, or at least a dedicated, full-length paper, but here we shall have to make do with something simpler. Since most readers are likely to have an intuitive grasp of the essence of KR&R, our simple account should suffice. The interesting thing is that this simple account itself makes reference to some of the foundational distinctions in the field of philosophy. These distinctions also play a central role in artificial intelligence (AI) and computer science. To begin with, the first distinction in KR&R is that we identify knowledge with knowledge that such-and-such holds (possibly to a degree), rather than knowing how. If you ask an expert tennis player how he manages to serve a ball at 130 miles per hour on his first serve, and then serve a safer, topspin serve on his second should the first be out, you may well receive a confession that, if truth be told, this athlete can't really tell you. He just does it; he does something he has been doing since his youth. Yet, there is no denying that he knows how to serve. In contrast, the knowledge in KR&R must be expressible in declarative statements. For example, our tennis player knows that if his first serve lands outside the service box, it's not in play. He thus knows a proposition, conditional in form.

Date

9. 2.2017 19:22:14

Type

a

0.016987987 = product of:
  0.033975974 = sum of:
    0.033975974 = sum of:
      0.0033233196 = weight(_text_:a in 4011) [ClassicSimilarity], result of:
        0.0033233196 = score(doc=4011,freq=2.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.07643694 = fieldWeight in 4011, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=4011)
      0.030652655 = weight(_text_:22 in 4011) [ClassicSimilarity], result of:
        0.030652655 = score(doc=4011,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.23214069 = fieldWeight in 4011, 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=4011)
  0.5 = coord(1/2)

Source

Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly

Type

a

0.016987987 = product of:
  0.033975974 = sum of:
    0.033975974 = sum of:
      0.0033233196 = weight(_text_:a in 4095) [ClassicSimilarity], result of:
        0.0033233196 = score(doc=4095,freq=2.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.07643694 = fieldWeight in 4095, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=4095)
      0.030652655 = weight(_text_:22 in 4095) [ClassicSimilarity], result of:
        0.030652655 = score(doc=4095,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.23214069 = fieldWeight in 4095, 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=4095)
  0.5 = coord(1/2)

Source

Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly

Type

a

0.016987987 = product of:
  0.033975974 = sum of:
    0.033975974 = sum of:
      0.0033233196 = weight(_text_:a in 3387) [ClassicSimilarity], result of:
        0.0033233196 = score(doc=3387,freq=2.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.07643694 = fieldWeight in 3387, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=3387)
      0.030652655 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
        0.030652655 = score(doc=3387,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.23214069 = fieldWeight in 3387, 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=3387)
  0.5 = coord(1/2)

Date

1. 8.2010 12:35:22

Type

a

0.016987987 = product of:
  0.033975974 = sum of:
    0.033975974 = sum of:
      0.0033233196 = weight(_text_:a in 4820) [ClassicSimilarity], result of:
        0.0033233196 = score(doc=4820,freq=2.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.07643694 = fieldWeight in 4820, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=4820)
      0.030652655 = weight(_text_:22 in 4820) [ClassicSimilarity], result of:
        0.030652655 = score(doc=4820,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.23214069 = fieldWeight in 4820, 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=4820)
  0.5 = coord(1/2)

Date

3.12.2016 18:39:22

Type

a

0.01692609 = product of:
  0.03385218 = sum of:
    0.03385218 = sum of:
      0.008308299 = weight(_text_:a in 2831) [ClassicSimilarity], result of:
        0.008308299 = score(doc=2831,freq=18.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.19109234 = fieldWeight in 2831, 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=2831)
      0.02554388 = weight(_text_:22 in 2831) [ClassicSimilarity], result of:
        0.02554388 = score(doc=2831,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 2831, 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=2831)
  0.5 = coord(1/2)

Abstract

The purpose of this work is to develop an ontology-based framework for developing an information retrieval system to cater to specific queries of users. For creating such an ontology, information was obtained from a wide range of information sources involved with brain tumour study and research. The information thus obtained was compiled and analysed to provide a standard, reliable and relevant information base to aid our proposed system. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as identification of the terminology, analysis, synthesis, standardization and ordering. A vast majority of the ontologies being developed nowadays lack flexibility. This becomes a formidable constraint when it comes to interoperability. We found that a facet-based method provides a distinct guideline for the development of a robust and flexible model concerning the domain of brain tumours. Our attempt has been to bridge library and information science and computer science, which itself involved an experimental approach. It was discovered that a faceted approach is really enduring, as it helps in the achievement of properties like navigation, exploration and faceted browsing. Computer-based brain tumour ontology supports the work of researchers towards gathering information on brain tumour research and allows users across the world to intelligently access new scientific information quickly and efficiently.

Date

12. 3.2016 13:21:22

Type

a

0.016435556 = product of:
  0.032871112 = sum of:
    0.032871112 = sum of:
      0.0073272306 = weight(_text_:a in 2589) [ClassicSimilarity], result of:
        0.0073272306 = score(doc=2589,freq=14.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.1685276 = fieldWeight in 2589, 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=2589)
      0.02554388 = weight(_text_:22 in 2589) [ClassicSimilarity], result of:
        0.02554388 = score(doc=2589,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 2589, 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=2589)
  0.5 = coord(1/2)

Abstract

Purpose Ontologies are used to formally describe the concepts within a domain in a machine-understandable way. Matching of heterogeneous ontologies is often essential for many applications like semantic annotation, query answering or ontology integration. Some ontologies may include a large number of entities which make the ontology matching process very complex in terms of the search space and execution time requirements. The purpose of this paper is to present a technique for finding degree of similarity between ontologies that trims down the search space by eliminating the ontology concepts that have less likelihood of being matched. Design/methodology/approach Algorithms are written for finding key concepts, concept matching and relationship matching. WordNet is used for solving synonym problems during the matching process. The technique is evaluated using the reference alignments between ontologies from ontology alignment evaluation initiative benchmark in terms of degree of similarity, Pearson's correlation coefficient and IR measures precision, recall and F-measure. Findings Positive correlation between the degree of similarity and degree of similarity (reference alignment) and computed values of precision, recall and F-measure showed that if only key concepts of ontologies are compared, a time and search space efficient ontology matching system can be developed. Originality/value On the basis of the present novel approach for ontology matching, it is concluded that using key concepts for ontology matching gives comparable results in reduced time and space.

Date

20. 1.2015 18:30:22

Type

a

0.016435556 = product of:
  0.032871112 = sum of:
    0.032871112 = sum of:
      0.0073272306 = weight(_text_:a in 2645) [ClassicSimilarity], result of:
        0.0073272306 = score(doc=2645,freq=14.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.1685276 = fieldWeight in 2645, 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=2645)
      0.02554388 = weight(_text_:22 in 2645) [ClassicSimilarity], result of:
        0.02554388 = score(doc=2645,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = 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)

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

Type

a

0.016163789 = product of:
  0.032327577 = sum of:
    0.032327577 = sum of:
      0.0067836978 = weight(_text_:a in 3466) [ClassicSimilarity], result of:
        0.0067836978 = score(doc=3466,freq=12.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.15602624 = fieldWeight in 3466, product of:
            3.4641016 = tf(freq=12.0), with freq of:
              12.0 = termFreq=12.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=3466)
      0.02554388 = weight(_text_:22 in 3466) [ClassicSimilarity], result of:
        0.02554388 = score(doc=3466,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 3466, 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=3466)
  0.5 = coord(1/2)

Abstract

Specimen identification keys are still the most commonly created tools used by systematic biologists to access biodiversity information. Creating identification keys requires analyzing and synthesizing large amounts of information from specimens and their descriptions and is a very labor-intensive and time-consuming activity. Automating the generation of identification keys from text descriptions becomes a highly attractive text mining application in the biodiversity domain. Fine-grained semantic annotation of morphological descriptions of organisms is a necessary first step in generating keys from text. Machine-readable ontologies are needed in this process because most biological characters are only implied (i.e., not stated) in descriptions. The immediate question to ask is How well do existing ontologies support semantic annotation and automated key generation? With the intention to either select an existing ontology or develop a unified ontology based on existing ones, this paper evaluates the coverage, semantic consistency, and inter-ontology agreement of a biodiversity character ontology and three plant glossaries that may be turned into ontologies. The coverage and semantic consistency of the ontology/glossaries are checked against the authoritative domain literature, namely, Flora of North America and Flora of China. The evaluation results suggest that more work is needed to improve the coverage and interoperability of the ontology/glossaries. More concepts need to be added to the ontology/glossaries and careful work is needed to improve the semantic consistency. The method used in this paper to evaluate the ontology/glossaries can be used to propose new candidate concepts from the domain literature and suggest appropriate definitions.

Date

1. 6.2010 9:55:22

Type

a

0.01586826 = product of:
  0.03173652 = sum of:
    0.03173652 = sum of:
      0.0061926404 = weight(_text_:a in 4607) [ClassicSimilarity], result of:
        0.0061926404 = score(doc=4607,freq=10.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.14243183 = fieldWeight in 4607, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4607)
      0.02554388 = weight(_text_:22 in 4607) [ClassicSimilarity], result of:
        0.02554388 = score(doc=4607,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 4607, 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=4607)
  0.5 = coord(1/2)

Abstract

Smart applications behave intelligently because they understand at least partially the context where they operate. To do this, they need not only a formal domain model but also formal descriptions of the data they process and their own operational behaviour. Interoperability of smart applications is based on formalised definitions of all their data and processes. This paper studies the semantic interoperability of data in the case of eLearning and describes an experiment and its assessment. New content is imported into a knowledge-based learning environment without real updates of the original domain model, which is encoded as a knowledge base of conceptual graphs. A component called mediator enables the import by assigning dummy metadata annotations for the imported items. However, some functionality of the original system is lost, when processing the imported content, due to the lack of proper metadata annotation which cannot be associated fully automatically. So the paper presents an interoperability scenario when appropriate content items are viewed from the perspective of the original world and can be (partially) reused there.

Source

Conceptual structures: knowledge architectures for smart applications: 15th International Conference on Conceptual Structures, ICCS 2007, Sheffield, UK, July 22 - 27, 2007 ; proceedings. Eds.: U. Priss u.a

Type

a

0.015541373 = product of:
  0.031082746 = sum of:
    0.031082746 = sum of:
      0.0055388655 = weight(_text_:a in 2829) [ClassicSimilarity], result of:
        0.0055388655 = score(doc=2829,freq=8.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.12739488 = fieldWeight in 2829, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2829)
      0.02554388 = weight(_text_:22 in 2829) [ClassicSimilarity], result of:
        0.02554388 = score(doc=2829,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 2829, 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=2829)
  0.5 = coord(1/2)

Abstract

Knowledge organization faces the challenge of managing the amount of knowledge available on the Web. Published literature in biomedical sciences is a huge source of knowledge, which can only efficiently be managed through automatic methods. The conventional channel for reporting scientific results is Web electronic publishing. Despite its advances, scientific articles are still published in print formats such as portable document format (PDF). Semantic Web and Linked Data technologies provides new opportunities for communicating, sharing, and integrating scientific knowledge that can overcome the limitations of the current print format. Here is proposed a semantic model of scholarly electronic articles in biomedical sciences that can overcome the limitations of traditional flat records formats. Scientific knowledge consists of claims made throughout article texts, especially when semantic elements such as questions, hypotheses and conclusions are stated. These elements, although having different roles, express relationships between phenomena. Once such knowledge units are extracted and represented with technologies such as RDF (Resource Description Framework) and linked data, they may be integrated in reasoning chains. Thereby, the results of scientific research can be published and shared in structured formats, enabling crawling by software agents, semantic retrieval, knowledge reuse, validation of scientific results, and identification of traces of scientific discoveries.

Date

12. 3.2016 13:17:22

Type

a

0.015541373 = product of:
  0.031082746 = sum of:
    0.031082746 = sum of:
      0.0055388655 = weight(_text_:a in 4553) [ClassicSimilarity], result of:
        0.0055388655 = score(doc=4553,freq=8.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.12739488 = fieldWeight in 4553, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4553)
      0.02554388 = weight(_text_:22 in 4553) [ClassicSimilarity], result of:
        0.02554388 = score(doc=4553,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 4553, 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=4553)
  0.5 = coord(1/2)

Abstract

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.

Date

16.11.2018 14:22:01

Type

a

0.015541373 = product of:
  0.031082746 = sum of:
    0.031082746 = sum of:
      0.0055388655 = weight(_text_:a in 106) [ClassicSimilarity], result of:
        0.0055388655 = score(doc=106,freq=8.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.12739488 = fieldWeight in 106, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=106)
      0.02554388 = weight(_text_:22 in 106) [ClassicSimilarity], result of:
        0.02554388 = score(doc=106,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 106, 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=106)
  0.5 = coord(1/2)

Abstract

Purpose The purpose of this paper is to identify the concepts, component parts and relationships between vocabularies, linked data and knowledge graphs (KGs) from the perspectives of data and knowledge transitions. Design/methodology/approach This paper uses conceptual analysis methods. This study focuses on distinguishing concepts and analyzing composition and intercorrelations to explore data and knowledge transitions. Findings Vocabularies are the cornerstone for accurately building understanding of the meaning of data. Vocabularies provide for a data-sharing model and play an important role in supporting the semantic expression of linked data and defining the schema layer; they are also used for entity recognition, alignment and linkage for KGs. KGs, which consist of a schema layer and a data layer, are presented as cubes that organically combine vocabularies, linked data and big data. Originality/value This paper first describes the composition of vocabularies, linked data and KGs. More importantly, this paper innovatively analyzes and summarizes the interrelatedness of these factors, which comes from frequent interactions between data and knowledge. The three factors empower each other and can ultimately empower the Semantic Web.

Date

22. 1.2021 14:24:32

Type

a

0.0151703395 = product of:
  0.030340679 = sum of:
    0.030340679 = sum of:
      0.004796799 = weight(_text_:a in 633) [ClassicSimilarity], result of:
        0.004796799 = score(doc=633,freq=6.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.11032722 = fieldWeight in 633, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=633)
      0.02554388 = weight(_text_:22 in 633) [ClassicSimilarity], result of:
        0.02554388 = score(doc=633,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = 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)

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

Type

a

0.01436246 = product of:
  0.02872492 = sum of:
    0.02872492 = sum of:
      0.008289815 = weight(_text_:a in 1634) [ClassicSimilarity], result of:
        0.008289815 = score(doc=1634,freq=28.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.19066721 = fieldWeight in 1634, product of:
            5.2915025 = tf(freq=28.0), with freq of:
              28.0 = termFreq=28.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.03125 = fieldNorm(doc=1634)
      0.020435104 = weight(_text_:22 in 1634) [ClassicSimilarity], result of:
        0.020435104 = score(doc=1634,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.15476047 = fieldWeight in 1634, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=1634)
  0.5 = coord(1/2)

Abstract

Purpose - Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations. Design/methodology/approach - Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies' semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies. Findings - To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies. Research limitations/implications - This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research. Practical implications - This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results. Originality/value - To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.

Date

20. 1.2015 18:30:22

Type

a

0.014156656 = product of:
  0.028313313 = sum of:
    0.028313313 = sum of:
      0.0027694327 = weight(_text_:a in 4422) [ClassicSimilarity], result of:
        0.0027694327 = score(doc=4422,freq=2.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.06369744 = fieldWeight in 4422, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4422)
      0.02554388 = weight(_text_:22 in 4422) [ClassicSimilarity], result of:
        0.02554388 = score(doc=4422,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.19345059 = fieldWeight in 4422, 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=4422)
  0.5 = coord(1/2)

Source

Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly

Type

a

0.013350808 = product of:
  0.026701616 = sum of:
    0.026701616 = sum of:
      0.0062665115 = weight(_text_:a in 1436) [ClassicSimilarity], result of:
        0.0062665115 = score(doc=1436,freq=16.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.14413087 = fieldWeight in 1436, product of:
            4.0 = tf(freq=16.0), with freq of:
              16.0 = termFreq=16.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.03125 = fieldNorm(doc=1436)
      0.020435104 = weight(_text_:22 in 1436) [ClassicSimilarity], result of:
        0.020435104 = score(doc=1436,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.15476047 = fieldWeight in 1436, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=1436)
  0.5 = coord(1/2)

Abstract

The development of complex projects in the aerospace industry is based on the collaboration of geographically distributed teams and companies. In this context, the need of sharing different types of data and information is a key factor to assure the successful execution of the projects. In the case of European projects, the ECSS standards provide a normative framework that specifies, among other requirements, the different document types, information items and artifacts that need to be generated. The specification of the characteristics of these information items are usually incorporated as annex to the different ECSS standards, and they provide the intended purpose, scope, and structure of the documents and information items. In these standards, documents or deliverables should not be considered as independent items, but as the results of packaging different information artifacts for their delivery between the involved parties. Successful information integration and knowledge exchange cannot be based exclusively on the conceptual definition of information types. It also requires the definition of methods and techniques for serializing and exchanging these documents and artifacts. This area is not covered by ECSS standards, and the definition of these data schemas would improve the opportunity for improving collaboration processes among companies. This paper describes the development of an OWL-based ontology to manage the different artifacts and information items requested in the European Space Agency (ESA) ECSS standards for SW development. The ECSS set of standards is the main reference in aerospace projects in Europe, and in addition to engineering and managerial requirements they provide a set of DRD (Document Requirements Documents) with the structure of the different documents and records necessary to manage projects and describe intermediate information products and final deliverables. Information integration is a must-have in aerospace projects, where different players need to collaborate and share data during the life cycle of the products about requirements, design elements, problems, etc. The proposed ontology provides the basis for building advanced information systems where the information coming from different companies and institutions can be integrated into a coherent set of related data. It also provides a conceptual framework to enable the development of interfaces and gateways between the different tools and information systems used by the different players in aerospace projects.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

Type

a

0.013350808 = product of:
  0.026701616 = sum of:
    0.026701616 = sum of:
      0.0062665115 = weight(_text_:a in 179) [ClassicSimilarity], result of:
        0.0062665115 = score(doc=179,freq=16.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.14413087 = fieldWeight in 179, product of:
            4.0 = tf(freq=16.0), with freq of:
              16.0 = termFreq=16.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.03125 = fieldNorm(doc=179)
      0.020435104 = weight(_text_:22 in 179) [ClassicSimilarity], result of:
        0.020435104 = score(doc=179,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.15476047 = fieldWeight in 179, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=179)
  0.5 = coord(1/2)

Abstract

Purpose The open science movement calls for transparent and retraceable research processes. While infrastructures to support these practices in qualitative research are lacking, the design needs to consider different approaches and workflows. The paper bases on the definition of ontologies as shared conceptualizations of knowledge (Borst, 1999). The authors argue that participatory design is a good way to create these shared conceptualizations by giving domain experts and future users a voice in the design process via interviews, workshops and observations. Design/methodology/approach This paper presents a novel approach for creating ontologies in the field of open science using participatory design. As a case study the creation of an ontology for qualitative coding schemas is presented. Coding schemas are an important result of qualitative research, and reuse can yield great potential for open science making qualitative research more transparent, enhance sharing of coding schemas and teaching of qualitative methods. The participatory design process consisted of three parts: a requirement analysis using interviews and an observation, a design phase accompanied by interviews and an evaluation phase based on user tests as well as interviews. Findings The research showed several positive outcomes due to participatory design: higher commitment of users, mutual learning, high quality feedback and better quality of the ontology. However, there are two obstacles in this approach: First, contradictive answers by the interviewees, which needs to be balanced; second, this approach takes more time due to interview planning and analysis. Practical implications The implication of the paper is in the long run to decentralize the design of open science infrastructures and to involve parties affected on several levels. Originality/value In ontology design, several methods exist by using user-centered design or participatory design doing workshops. In this paper, the authors outline the potentials for participatory design using mainly interviews in creating an ontology for open science. The authors focus on close contact to researchers in order to build the ontology upon the expert's knowledge.

Date

20. 1.2015 18:30:22

Type

a

0.010619237 = product of:
  0.021238474 = sum of:
    0.021238474 = sum of:
      0.003357759 = weight(_text_:a in 1633) [ClassicSimilarity], result of:
        0.003357759 = score(doc=1633,freq=6.0), product of:
          0.043477926 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.037706986 = queryNorm
          0.07722905 = fieldWeight in 1633, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.02734375 = fieldNorm(doc=1633)
      0.017880715 = weight(_text_:22 in 1633) [ClassicSimilarity], result of:
        0.017880715 = score(doc=1633,freq=2.0), product of:
          0.13204344 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.037706986 = queryNorm
          0.1354154 = fieldWeight in 1633, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.02734375 = fieldNorm(doc=1633)
  0.5 = coord(1/2)

Abstract

Purpose - The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach - In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings - The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42. Practical implications - When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved. Originality/value - In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

Date

20. 1.2015 18:30:22

Type

a