science

Multimedia Web Ontology Language

Multimedia Web Ontology 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 Multimedia Web Ontology Language rather than just read about it. In short: Machine interpretation of documents and services in Semantic Web environment is primarily enabled by (a) the capability to mark documents, document segments and services with semantic tags and (b) the ability to establish contextual relations between the tags with a domain model, which is formally represented as ontology. Human beings use natural languages to communicate an abstract view of the world.

Key takeaways

Multimedia Web Ontology 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 Multimedia Web Ontology Language to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Multimedia Web Ontology Language from memory before moving on to harder problems.

Reference excerpt

Machine interpretation of documents and services in Semantic Web environment is primarily enabled by (a) the capability to mark documents, document segments and services with semantic tags and (b) the ability to establish contextual relations between the tags with a domain model, which is formally represented as ontology. Human beings use natural languages to communicate an abstract view of the world. Natural language constructs are symbolic representations of human experience and are close to the conceptual model that Semantic Web technologies deal with. Thus, natural language constructs have been naturally used to represent the ontology elements. This makes it convenient to apply Semantic Web technologies in the domain of textual information. In contrast, multimedia documents are perceptual recording of human experience. An attempt to use a conceptual model to interpret the perceptual records gets severely impaired by the semantic gap that exists between the perceptual media features and the conceptual world. Notably, the concepts have their roots in perceptual experience of human beings and the apparent disconnect between the conceptual and the perceptual world is rather artificial. The key to semantic processing of multimedia data lies in harmonizing the seemingly isolated conceptual and the perceptual worlds. Representation of the Domain knowledge needs to be extended to enable perceptual modeling, over and above conceptual modeling that is supported. The perceptual model of a domain primarily comprises observable media properties of the concepts. Such perceptual models are useful for semantic interpretation of media documents, just as the conceptual models help in the semantic interpretation of textual documents. Multimedia Ontology language (M-OWL) is an ontology representation language that enables such perceptual modeling. It assumes a causal model of the world, where observable media features are caused by underlying concepts. In MOWL, it is possible to associate different types of media features in different media format and at different levels of abstraction with the concepts in a closed domain. The associations are probabilistic in nature to account for inherent uncertainties in observation of media patterns. The spatial and temporal relations between the media properties characterizing a concept (or, event) can also be expressed using MOWL. Often the concepts in a domain inherit the media properties of some related concepts, such as a historic monument inheriting the color and texture properties of its building material. It is possible to reason with the media properties of the concepts in a domain to derive an Observation Model for a concept. Finally, MOWL supports an abductive reasoning framework using Bayesian networks, that is robust against imperfect observations of media data.

History W3C forum has undertaken the initiative of standardizing the ontology representation for web-based applications. The Web Ontology Language (OWL), standardized in 2004 after maturing through XML(S), RDF(S) and DAML+OIL is a result of that effort. Ontology in OWL (and some of its predecessor languages) has been successfully used in establishing semantics of text in specific application contexts. The concepts and properties in these traditional ontology languages are expressed as text, making an ontology readily usable for semantic analysis of textual documents. Semantic processing of media data calls for perceptual modeling of domain concepts with their media properties. M-OWL has been proposed as an ontology language that enables such perceptual modeling. While M-OWL is a syntactic extension of OWL, it uses a completely different semantics based on probabilistic causal model of the world.

Key features Syntactically, MOWL is an extension of OWL. These extensions enable

Definition of media properties following MPEG-7 media description model. Probabilistic association of media properties with the domain concepts. Formal semantics to the media properties to enable reasoning. Formal semantics for spatio-temporal relations across media objects and events. MOWL is accompanied with reasoning tools that support

Construction of model of observation for a concept in multimedia documents with expected media properties. Probabilistic (Bayesian) reasoning for concept recognition with the model of observation.

See also Large Scale Concept Ontology for Multimedia Ontology for Media Resources

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Multimedia Web Ontology Language

Start with the simplest possible case. Write down what Multimedia Web Ontology 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 Multimedia Web Ontology 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 Multimedia Web Ontology 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 Multimedia Web Ontology Language

In research: Multimedia Web Ontology 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 Multimedia Web Ontology 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: Multimedia Web Ontology Language is common in secondary-school and first-year university syllabi. It links to neighbouring topics Multimedia, Ontology languages, Semantic Web, so understanding it makes those chapters shorter.
In everyday life: Look for Multimedia Web Ontology 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.

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Multimedia Web Ontology Language in 20 minutes

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

Frequently asked questions

What is Multimedia Web Ontology Language in simple terms?

Why does Multimedia Web Ontology 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 Multimedia Web Ontology 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 Multimedia Web Ontology Language.