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Provenance Markup Language

Provenance Markup Language is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Provenance Markup Language rather than just read about it. In short: The Provenance Markup Language (abbreviated PML; originally called Proof Markup Language) is an interlingua for representing and sharing knowledge about how information published on the Web was asserted from information sources and/or derived from Web information by intelligent agents. The language was initially developed in support of DARPA Agent Markup Language with a goal of explaining how automated theorem prove…

Key takeaways

  • Provenance Markup Language belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Provenance Markup Language to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Provenance Markup Language from memory before moving on to harder problems.

Reference excerpt

The Provenance Markup Language (abbreviated PML; originally called Proof Markup Language) is an interlingua for representing and sharing knowledge about how information published on the Web was asserted from information sources and/or derived from Web information by intelligent agents. The language was initially developed in support of DARPA Agent Markup Language with a goal of explaining how automated theorem provers (ATP) derive conclusions from a set of axioms. Information, inference steps, inference rules, and agents are the three main building blocks of the language. In the context of an inference step, information can play the role of antecedent (also called premise) and conclusion. Information can also play the role of axiom that is basically a conclusion with no antecedents. PML uses the broad philosophical definition of agent as opposed to any other more specific definition of agent. The use of PML in subsequent projects evolved the language in new directions broadening its capability to represent provenance knowledge beyond the realm of ATPs and automated reasoning. The original set of requirements were relaxed to include the following: information originally represented as logical sentences in the Knowledge Interchange Format were allowed to be information written in any language including the English language; and inference rules originally defined as patterns over antecedents and conclusions of inference steps were allowed to be underspecified as long as they were identified and named. These relaxations were essential to explain how knowledge is extracted from text through the use of information extraction components. Enhancements were also required to further understand motivation behind the need of automated theorem provers to derive conclusions: new capabilities were added to annotate how information playing the role of axioms were attributes as assertions from information sources; and the notion of questions and answers were introduced to the language to explain to a third-party agent why an automated theorem prover was used to prove a theorem (i.e., an answer) from a given set of axioms.

Development history The first version of PML (PML1) was developed at Stanford University's Knowledge Systems Laboratory in 2003 and was originally co-authored by Paulo Pinheiro, Deborah McGuinness, and Richard Fikes. The second version of PML (PML2) developed in 2007 modularized PML1 into three modules to reduce maintenance and reuse cost: provenance, justification, and trust relations. A new version of PML (PML3) based on World Wide Web Consortium's PROV is under development.

References

External links inference-web.org-main page Resources and Information. PROV-Overview

Worked examples

Example 1 — a first encounter with Provenance Markup Language

Start with the simplest possible case. Write down what Provenance Markup Language claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Provenance Markup Language before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Provenance Markup Language ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Provenance Markup Language

In research
Provenance Markup Language appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Provenance Markup Language in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Provenance Markup Language is common in secondary-school and first-year university syllabi. It links to neighbouring topics Markup languages, so understanding it makes those chapters shorter.
In everyday life
Look for Provenance Markup Language outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Provenance Markup Language in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Provenance Markup Language means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Provenance Markup Language out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Provenance Markup Language in simple terms?

The Provenance Markup Language (abbreviated PML; originally called Proof Markup Language) is an interlingua for representing and sharing knowledge about how information published on the Web was asserted from information sources and/or derived from Web information by intelligent agents. The language…

Why does Provenance Markup Language matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Provenance Markup Language?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Provenance Markup Language.

Tags

  • Markup languages

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