您如何为本体中不同用户所断言的陈述分配客观确定性？
例如，考虑用户 A 断言“鲍勃的帽子是蓝色的”，而用户 B 断言“鲍勃的帽子是红色的”。您如何确定：
- 用户 A 和用户 B 指的是名为 Bob 的不同人，并且可能正确也可能不正确。
- 两个用户指的是同一个人，但用户 A 是正确的，用户 B 是错误的（反之亦然）。
- 两个用户都指的是同一个人，但用户 A 是对的而用户 B 是在撒谎（反之亦然）。
- 两个用户都指的是同一个人，这两种用法要么是错误的，要么是撒谎的。
我看到的主要困难是本体没有任何方法来获取第一手数据（例如它不能问鲍勃他的帽子是什么颜色）。
我意识到可能没有完全客观的方法来解决这个问题。是否有任何可以采用的启发法？这个问题有正式的名称吗？
How would you assign objective certainties to statements asserted by different users in an ontology?
For example, consider User A asserts "Bob's hat is blue" while User B asserts "Bob's hat is red". How would you determine if:
- User A and User B are referring to different people named Bob, and may or may not be correct.
- Both users are referring to the same person, but User A is right and User B is mistaken (or vice versa).
- Both users are referring to the same person, but User A is right and User B is lying (or vice versa).
- Both users are referring to the same person, and both uses are either mistaken or lying.
The main difficulty I see is the ontology doesn't have any way to obtain first-hand data (e.g. it can't ask Bob what color his hat is).
I realize there's probably no entirely objective way to resolve this. Are there any heuristics that could be employed? Does this problem have a formal name?
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我不是这个领域的专家，但我对本体论和语义网的不确定性进行了一些研究。当然，解决这个问题的方法与语义网无关，但我的知识仅限于此。
我认为与您的问题相关的两个问题是身份危机和URI 危机。上述陈述的正式表述可以在 RDF（资源描述框架）中发布。
如果我将语句“鲍勃的帽子是蓝色/红色”转换为三元组，这将是：
事实 1：
事实 2：
这里的问题是 X、Y、H1 和 H2 是资源，它们可能相同也可能不同。因此，在您的示例中，未知 X 和 Y 是同一个人还是不同的人，如果没有进一步的信息，您就无法知道。 （帽子也是如此。）
然而，问题更复杂，因为用户 A 和 B 只是陈述了这些事情，所以它们不是“真实”的事实。 RDF为此提供了具体化的方法，但我不会写完全放在这里，就太长了。您基本上要做的就是向上述每个语句添加“UserA statesThat (...)”。
如果你具备了这一切，你就可以开始推理了。在大学里我们曾经使用 RACER 来做这种事情，但那是旧版本而且我对当前的不熟悉。
当然，您也可以在没有 RDF 的情况下完成这些工作，例如在 LISP 中。
希望有帮助。
I'm not an expert in this field, but I've worked a bit with uncertainties in ontologies and the Semantic Web. There are, of course, approaches to this problem that have nothing to do with the semantic web, but my knowledge ends there.
Two problems that I feel are connected with your question are the Identity Crisis and the URI crisis. Formal representations of the statements above can be issued in RDF (Resource Description Framework).
If I convert the statementss "Bob's hat is blue/red" into triples, this would be:
Fact 1:
Fact 2:
The problem here is that X, Y, H1 and H2 are resources, which may or may not be the same. So in your example it is unknown if X and Y are the same person or distinct and you can't know without further information. (Same holds for the hats.)
However, the problem is more complex, because User A and B just stated those things, so they are no "real" facts. RDF offer the method of Reification for this, but I won't write this down here completely, it would be too long. What you basically would do is add an "UserA statesThat (...)" to every above mentioned statement.
If you have all this, you can start reasoning. At the university we once used RACER for this kind of stuff, but that was an old version and I'm not familiar with the current one.
Of Course, you can do that stuff without RDF as well, e.g., in LISP.
Hope it helped.
我听说这种事情被称为信息融合，反映了数据融合的想法。我对此了解不多，但似乎有关于该主题的会议。
我还要在这里添加另一个困难，即区分客观信息和主观信息。如果用户 A 说“鲍勃是个好人”而用户 B 说“鲍勃不是个好人”，那么他们在断言看似相反的陈述时可能都是正确的。
I have heard this kind of thing being referred to as information fusion, mirroring the idea of data fusion. I don't know much about it but it seems there are conferences on the subject.
I'd also add another difficulty here, that of distinguishing between objective and subjective information. If user A says 'Bob is a nice guy' and user B says 'Bob is not a nice guy' then they can both be right while asserting seemingly opposing statements.
第 1 步：做出一些假设。否则，你就没有任何依据。一个可能的假设是，“如果鲍勃的帽子是红色的，则 90% 的用户 A 会说他的帽子是红色的。”
第 2 步：应用相关数学。要将条件概率与其逆概率联系起来（即根据我提出的假设，知道 A 所说的话，询问鲍勃的帽子是红色的概率），请使用 贝叶斯定理。
Step 1: Make some assumptions. Otherwise, you have nothing to base anything on. A possible assumption would be, "If bob's hat is red, there is a 90% that User A will say his hat is red."
Step 2: Apply relevant math. To relate a conditional probability to its inverse (i.e. to ask the probability that bob's hat is red knowing what A said based on the assumption I proposed), use Bayes Theorem.