Search (2 results, page 1 of 1)

  • × author_ss:"Bar-Ilan, J."
  • × theme_ss:"Wissensrepräsentation"
  1. Zhitomirsky-Geffet, M.; Bar-Ilan, J.: Towards maximal unification of semantically diverse ontologies for controversial domains (2014) 0.01
    0.0068576634 = product of:
      0.02057299 = sum of:
        0.008743925 = weight(_text_:in in 1634) [ClassicSimilarity], result of:
          0.008743925 = score(doc=1634,freq=12.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.14725187 = fieldWeight in 1634, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.03125 = fieldNorm(doc=1634)
        0.011829065 = product of:
          0.02365813 = sum of:
            0.02365813 = weight(_text_:22 in 1634) [ClassicSimilarity], result of:
              0.02365813 = score(doc=1634,freq=2.0), product of:
                0.15286934 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.043654136 = 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)
      0.33333334 = coord(2/6)
    
    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
  2. Zhitomirsky-Geffet, M.; Erez, E.S.; Bar-Ilan, J.: Toward multiviewpoint ontology construction by collaboration of non-experts and crowdsourcing : the case of the effect of diet on health (2017) 0.00
    0.0016629322 = product of:
      0.009977593 = sum of:
        0.009977593 = weight(_text_:in in 3439) [ClassicSimilarity], result of:
          0.009977593 = score(doc=3439,freq=10.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.16802745 = fieldWeight in 3439, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3439)
      0.16666667 = coord(1/6)
    
    Abstract
    Domain experts are skilled in buliding a narrow ontology that reflects their subfield of expertise based on their work experience and personal beliefs. We call this type of ontology a single-viewpoint ontology. There can be a variety of such single viewpoint ontologies that represent a wide spectrum of subfields and expert opinions on the domain. However, to have a complete formal vocabulary for the domain they need to be linked and unified into a multiviewpoint model while having the subjective viewpoint statements marked and distinguished from the objectively true statements. In this study, we propose and implement a two-phase methodology for multiviewpoint ontology construction by nonexpert users. The proposed methodology was implemented for the domain of the effect of diet on health. A large-scale crowdsourcing experiment was conducted with about 750 ontological statements to determine whether each of these statements is objectively true, viewpoint, or erroneous. Typically, in crowdsourcing experiments the workers are asked for their personal opinions on the given subject. However, in our case their ability to objectively assess others' opinions was examined as well. Our results show substantially higher accuracy in classification for the objective assessment approach compared to the results based on personal opinions.