“语义网”中的本体论
我一直在努力跟上计算机科学领域的最新主题，并且不断阅读有关“语义网”的内容。
根据我的理解，语义网络意味着以下内容：
1）网络上的信息被赋予明确的含义
2）网络服务可以自动处理和集成网络上可用的信息。
很简单，我确信这些要点中没有涵盖一些具体细节，但我现在不关注它们。
我也知道“本体论”的概念作为实现语义网的方法。
这就是我未能将其概念化为实用的东西。
是否有一个真实的世界，或者有一个实际的例子来说明这一点？目前有使用此功能的示例吗？
I have been attempting to keep up to date with up to date topics in computer science, and I keep reading about the "semantic web".
From my understanding, the semantic web means the following:
1)Information on the web is given explicit meaning
2)It would be possible for web services to automatically process and integrate information available on the web.
Simple enough, Im sure there are specifics that are not covered in these points, but I am not focusing on them right now.
I also am aware of the concept of "Ontology" as a method of implementing the semantic web.
This is what Im failing to conceptualize as practical.
Is there a real world, or a practical example of what this would be like? Is there any examples of this currently being used?
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一个非常实用的“语义”示例是谷歌支持的丰富片段。请参阅以下网站：http://www.google.com/ support/webmasters/bin/answer.py?answer=99170。通过嵌入特定标记，您可以向搜索引擎描述您的企业的“营业时间”等内容。我相信 Bing 和 Yahoo 也支持这个相同的标准。
在这种情况下，本体由模式定义。
One very practical "semantic" example is the Rich Snippets supported by google. See the website at: http://www.google.com/support/webmasters/bin/answer.py?answer=99170 . By embedding specific markup you can describe things like "hours of operation" for your business to the search engine. I believe that Bing and Yahoo also support this same standard.
In this case the ontology is defined by a schema.
“语义网”实际上是一种理想化和概念化；它是一种所有数据和信息都以机器可以理解和解释的方式表示的状态，以便可以根据人的意图精确地检索信息。
语义网的想法是由 Tim Berners-Lee 在他的众多开创性文章中提出（并且可能是创造的）：语义网，《科学美国人》，2001 年 5 月，与 James Hendler 和 Ora Lassila 合作。 Tim 在 W3C 中创立了语义 Web 活动，致力于追求这一理想化。 它的主页提供了一个简单的介绍以及几个重要的参考资料，你想研究一下吗主题更深入。
Miller 和 Swick 还在“W3C 语义 Web 活动概述”中撰写了一篇介绍性文章2003 年。我发现这篇文章是理解“语义网”含义的一个很好的切入点。
The "Semantic Web" is really a idealisation and conceptualisation; it is a state where all data and information are represented in a way that is understandable and interpretable by machines, so that information can be retrieved precisely according to to one's intent.
The idea of a Semantic Web was proposed (and probably coined) by Tim Berners-Lee in one of his numerous seminal articles: The Semantic Web, Scientific American, May 2001, along with James Hendler and Ora Lassila. Tim founded the Semantic Web Activity in W3C, which endeavours to pursue this idealisation. Its homepage provides a brief introduction as well as several important references, shall you want to research into this topic in more depth.
Miller and Swick also wrote an introductory article on "An Overview of W3C Semantic Web Activity" in 2003. I found this article a good entry point to understanding what "Semantic Web" means.
这是一个数据网络。全部机器可读。这是一个示例，但不是真正的实现。 http://www.youtube.com/watch?v=RNJl9EEcsoE
It's a web of data. All machine readable. Here's an example, but not a real implementation. http://www.youtube.com/watch?v=RNJl9EEcsoE
语义网是一种以一个点通向另一个相关点的方式连接的网络。在语义网中，每个图像或数据都将具有含义，并且不会基于关键字。
http://www.business-science-articles.com /science/articles/computer/601-semantic-web
Semantic web is a web that is connected in such a way that one point leads to another relevant point.In semantic web every image or data will have a meaning and will not be based on key words.
http://www.business-science-articles.com/science/articles/computer/601-semantic-web
非常简短的概述可以在此处找到。
现实世界的例子就在那里，而且有很多。
虽然语义网最初是为网络而设计的，但它比网络更有用。例如，它可用于发现和构建知识库（表示为本体）。在我的工作环境中，我们正在考虑使用它来处理领域分析不断变化的方面，并且由于我们对领域的看法发生了变化（或目前尚不清楚），因此更容易表达知识和本体的某些依赖关系。
另一种选择可以是使用标准软件工程技术、关系数据库、图表等，但同样，在我们的背景下（科学和共享知识），使用语义网络概念是有意义的。
Very brief overview can be found here.
Real world examples are out there, and there are plenty of them.
While Semantic Web was originally meant for the web, it is more useful than that. For example, it can be used to discover and build knowledge bases (expressed as ontologies). In my work context we are looking into using it for dealing with changing aspects of domain analysis, and since our perception about the domain changes (or is not yet clear at the moment), it is easier to express knowledge and some dependencies with ontologies.
The alternative could be using standard software engineering techniques, relational databases, diagrams, etc., but again, in our context (scientific and shared knowledge) it makes sense to use semantic web concepts.
思考语义网的最简单方法是看看如何将其应用于现实世界的场景。
我们来看例句：
1.约翰·史密斯是麻省理工学院的教授。
2. Smith教授兼任计算机科学系主任。
我们人类可以很容易地尝试将上述两句话联系起来。对于机器来说，这是不可能的。为什么？因为他们不知道约翰是汽车、人还是街道名称。以及史密斯是姓氏还是职称。
但是，如果我们在网络中的某处有以下附加信息：
一个。约翰是名字
b.史密斯是姓氏
c.网络上的唯一地址是 http://www.example.com//person/uid/9087809812< /a>
现在，如果我们有每个实体的附加信息（通常称为元数据），就像上面一样，并且它们是否可以取消引用（即如果我们可以查看使用相应的唯一地址），机器可以自行关联实体。这是语义网络的愿景——拥有关于每个可能实体的元信息，并按照某些受控词汇和逻辑（称为“本体”）创建元信息。
The easiest way to think Semantic web is to see how it can be applied to real world scenarios.
Let's take example sentences:
1. John Smith is a professor at MIT.
2. Prof. Smith is also dean of CS.
We, humans, can easily try to relate the above two sentences. For machines, it's not possible. why? because they don't know whether John is a car,person or a street name. And whether Smith is a last name or the job title.
But what if we have following additional information somewhere in the web:
a. John is first name
b. Smith is last name
c. unique address on web is http://www.example.com//person/uid/9087809812
Now, if we have additional information (often called metadata) for each entity like above and if they can be dereferenced (i.e. if we can look up using corresponding unique address), machines can relate entities on their own. This is the vision of semantic web- to have meta information about every possible entities and create meta information following certain controlled vocabularies and logics referred to as 'ontology'.
查看本体的一种方法（请注意，这是一种非常简单的观点）是只关注本体本身中发现的主谓三元组，然后参考一些已经发布了本体（其中许多来自自然科学。）
YMMV，但至少对我来说，直到我回到学校并在 Prolog 的规则和事实。
使用简单的例子，假设您想要将信息表达为主谓宾三元组中的关系（由主体“Y”引起的疾病“X”，与某些法律“B”相关的法律先例“A”等等。）仅使用关键字是不可能实现这一点的。因此，您建立关系来表达信息，然后这些关系以某种机器可读的格式实现。
那么，至少按照这个想法，这些关系是可以枚举的。它们可以被处理，人们可以提出这样的问题：X 与 Y 通过关系 Z 相关吗？与X有什么关系？通过关系 Z 关联什么？您可以通过这种方式推断出新知识，特别是如果使用一个完善的本体论（如我之前提供的链接中所示）……
或者理论是这样的。这是一个很好的概念，人们已经很好地利用了这一概念，我认为我们正在朝着正确的方向前进。时间会证明这个想法是否普遍适用于实际。
One way to look at ontologies (very simplistic view mind you) is to just focus on the subject-predicate-triples found in ontologies themselves and then refer to some of the already published ontologies out there (many of them from the natural sciences.)
YMMV, but at least for me, the idea of the semantic web and ontologies didn't click on me until I went back to my school years and found an analogy in Prolog's rules and facts.
Using trivial examples, say you want to express information as relations in subject-predicate-object triples (disease "X" caused-by agent "Y", legal precedent "A" related-to some law "B" and so on.) This is not possible by just using keywords. So you build relations to express information, and then those relations are implemented in some machine-readable format.
Then, at least so the idea goes, these relations can be enumerable. They can be processed and people can ask questions such as: is X related to Y by relation Z? What is related to X? What is related via relation Z? You can infer new knowledge this way, specially if one uses a well-established ontology (as in the link I previously provided)...
... or so does the theory goes. It is a good concept, one that people are already putting to good use, and I think we are moving in the right direction. Time will tell if the idea is universally applicable in a practical manner.