science

Semantic technology

Semantic technology 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 Semantic technology rather than just read about it. In short: The ultimate goal of semantic technology is to help machines understand data. Well-known technologies that enable the encoding of semantics in data include the Resource Description Framework (RDF) and the Web Ontology Language (OWL).

Key takeaways

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

Reference excerpt

The ultimate goal of semantic technology is to help machines understand data. Well-known technologies that enable the encoding of semantics in data include the Resource Description Framework (RDF) and the Web Ontology Language (OWL). These technologies formally represent the meaning involved in information. For example, ontology can describe concepts, relationships between things, and categories of things. Embedding semantics in data offers significant advantages, such as enabling reasoning over data and dealing with heterogeneous data sources.

Overview In software, semantic technology encodes meanings separately from data and content files, and separately from application code. This enables machines as well as people to understand, share and reason with them at execution time. With semantic technologies, adding, changing and implementing new relationships or interconnecting programs in a different way can be just as simple as changing the external model that these programs share. With traditional information technology, on the other hand, meanings and relationships must be predefined and "hard wired" into data formats and the application program code at design time. This means that when something changes, previously unexchanged information needs to be exchanged, or two programs need to interoperate in a new way, the humans must get involved. Off-line, the parties must define and communicate between them the knowledge needed to make the change, and then recode the data structures and program logic to accommodate it, and then apply these changes to the database and the application. Then, and only then, can they implement the changes. Semantic technologies are "meaning-centered". They involve but are not limited to the following areas of application:

encoding/decoding of semantic representation, knowledge graphs of entities and their interrelationships, auto-recognition of topics and concepts, information and meaning extraction, semantic data integration, and taxonomies/classification. Given a question, semantic technologies can directly search topics, concepts, associations that span a vast number of sources. Semantic technologies provide an abstraction layer above existing IT technologies that enables bridging and interconnection of data, content, and processes. Second, from the portal perspective, semantic technologies can be thought of as a new level of depth that provides far more intelligent, capable, relevant, and responsive interaction than with information technologies alone. Semantic technologies would often leverage natural language processing and machine learning in order to extract topics, concepts, and associations between concepts in text.

See also Knowledge graph Metadata Ontology – also known as a knowledge graph in a generalized term Resource Description Framework Schema.org – a set of schemas for structured data markup on web pages Semantic heterogeneity Semantic integration Semantic matching Semantic networks Semantic interoperability Semantic Web Web Ontology Language

References

Further reading J.T. Pollock, R. Hodgson. Adaptive Information: Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration. John Wiley & Sons, October 2004 R. Guha, R. McCool, and E. Miller. Semantic search. In WWW2003 — Proc. of the 12th international conference on World Wide Web, pp 700–709. ACM Press, 2003. I. Polikoff and D. Allemang. Semantic technology. TopQuadrant Technology Briefing v1.1, September 2003. T. Berners-Lee, J. Hendler, and O. Lassila. The Semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, May 2001. A.P. Sheth, C. Ramakrishnan. Semantic (Web) Technology In Action: Ontology Driven Information Systems For Search, Integration and Analysis. IEEE Data Engineering Bulletin, 2003. Steffen Staab, Rudi Studer (Ed.), Handbook on Ontologies, Springer, Mills Davis. The Business Value of Semantic Technologies. Presentation and Report. Semantic Technologies for E-Government, September 2004. Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, Foundations of Semantic Web Technologies, Chapman&Hall/CRC, 2009, ISBN 978-1-4200-9050-5 Milošević, Nikola, and Wolfgang Thielemann. "Comparison of biomedical relationship extraction methods and models for knowledge graph creation." Journal of Semantic Web (JoWS) (2022). Elsevier, doi:10.1016/j.websem.2022.100756

Illustrations

Worked examples

Example 1 — a first encounter with Semantic technology

Start with the simplest possible case. Write down what Semantic technology 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 Semantic technology 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 Semantic technology 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 Semantic technology

In research: Semantic technology 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 Semantic technology 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: Semantic technology is common in secondary-school and first-year university syllabi. It links to neighbouring topics Information retrieval techniques, Semantics, so understanding it makes those chapters shorter.
In everyday life: Look for Semantic technology 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 Semantic technology in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Semantic technology 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 Semantic technology out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Semantic technology in simple terms?

Why does Semantic technology 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 Semantic technology?

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 Semantic technology.