TY - GEN
T1 - Are names meaningful? Quantifying social meaning on the semantic web
AU - de Rooij, Steven
AU - Beek, Wouter
AU - Bloem, Peter
AU - van Harmelen, Frank
AU - Schlobach, Stefan
PY - 2016
Y1 - 2016
N2 - According to its model-theoretic semantics, Semantic Web IRIs are individual constants or predicate letters whose names are chosen arbitrarily and carry no formal meaning. At the same time it is a well-known aspect of Semantic Web pragmatics that IRIs are often constructed mnemonically, in order to be meaningful to a human interpreter. The latter has traditionally been termed ‘social meaning’, a concept that has been discussed but not yet quantitatively studied by the Semantic Web community. In this paper we use measures of mutual information content and methods from statistical model learning to quantify the meaning that is (at least) encoded in Semantic Web names. We implement the approach and evaluate it over hundreds of thousands of datasets in order to illustrate its efficacy. Our experiments confirm that many Semantic Web names are indeed meaningful and, more interestingly, we provide a quantitative lower bound on how much meaning is encoded in names on a per-dataset basis. To our knowledge, this is the first paper about the interaction between social and formal meaning, as well as the first paper that uses statistical model learning as a method to quantify meaning in the Semantic Web context. These insights are useful for the design of a new generation of Semantic Web tools that take such social meaning into account.
AB - According to its model-theoretic semantics, Semantic Web IRIs are individual constants or predicate letters whose names are chosen arbitrarily and carry no formal meaning. At the same time it is a well-known aspect of Semantic Web pragmatics that IRIs are often constructed mnemonically, in order to be meaningful to a human interpreter. The latter has traditionally been termed ‘social meaning’, a concept that has been discussed but not yet quantitatively studied by the Semantic Web community. In this paper we use measures of mutual information content and methods from statistical model learning to quantify the meaning that is (at least) encoded in Semantic Web names. We implement the approach and evaluate it over hundreds of thousands of datasets in order to illustrate its efficacy. Our experiments confirm that many Semantic Web names are indeed meaningful and, more interestingly, we provide a quantitative lower bound on how much meaning is encoded in names on a per-dataset basis. To our knowledge, this is the first paper about the interaction between social and formal meaning, as well as the first paper that uses statistical model learning as a method to quantify meaning in the Semantic Web context. These insights are useful for the design of a new generation of Semantic Web tools that take such social meaning into account.
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U2 - 10.1007/978-3-319-46523-4_12
DO - 10.1007/978-3-319-46523-4_12
M3 - Conference contribution
SN - 978-3-319-46522-7
VL - 9981 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 184
EP - 199
BT - The Semantic Web - 15th International Semantic Web Conference, ISWC 2016, Proceedings
PB - Springer/Verlag
T2 - 15th International Semantic Web Conference, ISWC 2016
Y2 - 17 October 2016 through 21 October 2016
ER -