000			01680nam a22002057a 4500
003			ZW-GwMSU
005			20221128111707.0
008			221128b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100			_aWenwen, Li _eauthor
245			_aSemantic similarity measurement based on knowledge mining: an artificial neural net approach _ccreated by Wenwen enwen Li , Robert Raskin and Michael F. Goodchild
264			_bTaylor & Francis _c2012
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aetween spatial objects. It combines a description logic based knowledge base (an ontology) and a multi-layer neural network to simulate the human process of similarity perception. In the knowledge base, spatial concepts are organized hierarchically and are modelled by a set of features that best represent the spatial, temporal and descriptive attributes of the concepts, such as origin, shape and function. Water body ontology is used as a case study. The neural network was designed and human subjects' rankings on similarity of concept pairs were collected for data training, knowledge mining and result validation. The experiment shows that the proposed method achieves good performance in terms of both correlation and mean standard error analysis in measuring the similarity between neural network prediction and human subject ranking. The application of similarity measurement with respect to improving relevancy ranking of a semantic search engine is introduced at the end.
856			_uhttps://doi.org/10.1080/13658816.2011.635595
942			_2lcc _cJA
999			_c160624 _d160624