2007年--计算机外文翻译--社交网络数据的整合与推论（节选）

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1、 2450 单词， 12500 英文字符 ,3940汉字 出处： Mika P, Jain R, Sheth A. Modeling and Aggregating Social Network DataJ. Social Networks & the Semantic Web, 2007.P109-113 5.4 Aggregating and reasoning with social network data Supposing that we have started out with some data sets in traditional formats (rela- tiona

2、l databases, Excel sheets, XML files etc.) our first step is to convert them into an RDF-based syntax, which allows to store the data in an ontology store and ma- nipulate it with ontology-based tools. In this process we need to assign identifiers to resources (an issue that we deal with in Section

3、5.4.1) and re-represent our data in terms of a shared ontology such as FOAF. In case our data sets come from external sources it is often more natural to pre- serve their original schema. For example, in case of converting data from a relational database or Excel sheet it is natural to preserve the

4、schema of the database or spread- sheet as represented by the table definitions or table headings. We can then apply ontology mapping to unify our data on the schema level by mapping classes (types) and properties from different schemas to a shared ontology such as FOAF. In effect, ontology mapping

5、allows us to treat the data set as if it had a single shared schema. Note that research on automated methods for ontology mapping is an active research area within the Semantic Web community. It is not our primary concern as the num- ber of ontologies involved and their size does not make it necessa

6、ry to automate ontology mapping in our typical case. The task of aggregation, however, is not complete yet: we need to find identical resources across the data sets. This is a two step process. First, it requires capturing the domain-specific knowledge of when to consider two instances to be the same. As we will see the FOAF ontology itself also prescribes ways to infer the equal- ity of two instances, for example based on their email address. However, beyond these properties it is likely