RDFa 如何使用？
我想知道 RDFa 是如何使用的。更具体地说，如果数据语义网由 RDF 中的三元组组成，那么 RDFa 内容的消费者如何使用该数据？必须用GRDDL之类的工具转换吗？或者还有其他方法从 RDFa 网站获取语义数据吗？
谢谢， 布鲁斯
I was wondering how RDFa is being used. More specifically, if the Semantic web of data is made up of triplees in RDF, how does a consumer of RDFa content use that data? Does it have to be converted with a tool like GRDDL? Or are there other methods of getting the semantic data from RDFa websites?
Thanks,
Bruce
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一种方法是使用 RDFa 解析器从 RDFa 文档中提取三元组，然后在应用程序中处理它们。我想这就是谷歌及其合作伙伴在搜索引擎中所做的事情。
或者，您可以告诉 SPARQL 存储（在内存中或持久存储中）使用其 API 或 SPARQL 1.1 的 LOAD 命令加载文档，然后使用 SPARQL 查询来访问数据。这还允许您同时查询多个文档。
我可能建议不要尝试直接处理三元组，除非您的应用程序或数据非常简单。
One way is to use an RDFa parser to extract the triples from the RDFa document, and then process them in your app. this is what Google and co do in their search engines, I imagine.
Alternatively you can tell a SPARQL store (either in memory or persistent) to load the document, either with it's API, or SPARQL 1.1's LOAD command, and then use SPARQL queries to access the data. This will also allow you to query across many documents at the same time.
I would probably advise against trying to process triples directly unless your app or data is very simple.
有很多方法可以访问 RDF 数据。例如，像 dbpedia 这样的网站有一个 sparql 端点，因此您可以直接使用某种数据库语言查询三元组。这是相当方便的。
有时您还可以直接获取 rdf 并使用 Jena (Java) 等框架对其进行解析。
There are a lot of ways to access RDF data. Websites like dbpedia for example have a sparql endpoint so you directly query the triples in a sort-of database language. This is quite convenient.
You can also sometimes grab the rdf directly and parse it with frameworks like Jena (Java).
RDF只不过是一个通过命名边将节点（通常是资源）连接到其他节点（其他资源或文字）的图，但有所不同。边缘可以通过逻辑推断，这意味着即使您从未逐字提供答案，您也可以获得问题的答案。推理引擎足够聪明，可以从它对系统的了解以及它所描述的逻辑的了解中得出这些答案。示例：如果您指定 A 是 B 的母亲，则推理引擎可以推断 B 有一个父 A。您从未指定连接 B -- hasParent -> 的边。 A，但您指定了
A -- motherOf -> B并且您还指定了hasParent和motherOf是相反的。获得数据后，您可以向数据库/推理引擎提出问题。该操作基本上是子图同构搜索。您的查询是一个图，您的数据库包含一个巨大的图，其中所有资源都通过命名边连接。数据库的任务是找到巨大图表中与您的查询匹配（同构）的部分，同时考虑每个边和节点的风格，而不仅仅是它们的互连性。
它是用来做什么的？很多事情。您可以使用此数据模型做非常强大的事情。一个容易理解的问题是思考社交网络：人（一种资源，图的一个节点）通过关系（friendOf）连接并被描述（isAged，livesIn）。通过查询，可以找到所有拥有至少 3 个女性朋友且居住在巴黎的 23 岁人群。
RDF is nothing but a graph connecting nodes (normally, resources) to other nodes (other resources, or literals) via named edges, but with a twist. Edges can be inferred via logic, meaning that you can get answers to questions even if you never provided the answer verbatim. The inference engine is smart enough to derive these answers from what it knows about the system, and what it knows about the logic of what it describes. Example: if you specify that A is a the mother of B, the inference engine can infer that B has a parent A. You never specified an edge connecting
B -- hasParent -> A, but you specified thatA -- motherOf -> Band you also specified thathasParentandmotherOfare inverses.Once you have your data, you can ask questions to the database/inference engine. This operation is basically a subgraph isomoprhism search. Your query is a graph, and your database contains a huge graph with all the resources connected by named edges. The task of the database is to find those parts of the huge graph that match (are isomorph) to your query, considering also the flavour of each edge and node, not only their interconnectivity.
What is it used for ? a lot of things. You can do very powerful stuff with this data model. One that is trivial to understand is to think to social networks: people (a resource, a node of the graph) connected by relations (friendOf) and being described (isAged, livesIn). With a query, you can find all 23 years old people having at least three female friends and living in Paris.