science

Ontology-based data integration

Ontology-based data integration 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 Ontology-based data integration rather than just read about it. In short: Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV).

Key takeaways

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

Reference excerpt

Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). The effectiveness of ontology‑based data integration is closely tied to the consistency and expressivity of the ontology used in the integration process.

Background Data from multiple sources are characterized by multiple types of heterogeneity. The following hierarchy is often used:

Syntactic heterogeneity: is a result of differences in representation format of data Schematic or structural heterogeneity: the native model or structure to store data differ in data sources leading to structural heterogeneity. Schematic heterogeneity that particularly appears in structured databases is also an aspect of structural heterogeneity. Semantic heterogeneity: differences in interpretation of the 'meaning' of data are source of semantic heterogeneity System heterogeneity: use of different operating system, hardware platforms lead to system heterogeneity Ontologies, as formal models of representation with explicitly defined concepts and named relationships linking them, are used to address the issue of semantic heterogeneity in data sources. In domains like bioinformatics and biomedicine, the rapid development, adoption and public availability of ontologies [1] Archived 2007-06-16 at the Wayback Machine has made it possible for the data integration community to leverage them for semantic integration of data and information.

The role of ontologies Ontologies enable the unambiguous identification of entities in heterogeneous information systems and assertion of applicable named relationships that connect these entities together. Specifically, ontologies play the following roles:

Content Explication The ontology enables accurate interpretation of data from multiple sources through the explicit definition of terms and relationships in the ontology. Query Model In some systems like SIMS, the query is formulated using the ontology as a global query schema. Verification The ontology verifies the mappings used to integrate data from multiple sources. These mappings may either be user specified or generated by a system.

Approaches using ontologies for data integration There are three main architectures that are implemented in ontology‑based data integration applications, namely,

Single ontology approach A single ontology is used as a global reference model in the system. This is the simplest approach as it can be simulated by other approaches. SIMS is a prominent example of this approach. The Structured Knowledge Source Integration component of Research Cyc is another prominent example of this approach. (Title = Harnessing Cyc to Answer Clinical Researchers' Ad Hoc Queries). The Gellish Taxonomic Dictionary-Ontology follows this approach as well. Multiple ontologies Multiple ontologies, each modeling an individual data source, are used in combination for integration. Though, this approach is more flexible than the single ontology approach, it requires creation of mappings between the multiple ontologies. Ontology mapping is a challenging issue and is focus of large number of research efforts in computer science [2]. The OBSERVER system is an example of this approach. Hybrid approaches The hybrid approach involves the use of multiple ontologies that subscribe to a common, top-level vocabulary. The top-level vocabulary defines the basic terms of the domain. Thus, the hybrid approach makes it easier to use multiple ontologies for integration in presence of the common vocabulary.

See also Data mapping Enterprise application integration Enterprise information integration Ontology mapping Schema matching

Further reading Chicco, D; Masseroli, M (2016). "Ontology-based prediction and prioritization of gene functional annotations". IEEE/ACM Transactions on Computational Biology and Bioinformatics. 13 (2): 248–260. doi:10.1109/TCBB.2015.2459694. PMID 27045825. S2CID 2795344.

References

External links OBSERVER home page Cyc Semantic Knowledge Source Integration (SKSI) Archived 2014-05-17 at the Wayback Machine

Worked examples

Example 1 — a first encounter with Ontology-based data integration

Start with the simplest possible case. Write down what Ontology-based data integration 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 Ontology-based data integration 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 Ontology-based data integration 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 Ontology-based data integration

In research: Ontology-based data integration 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 Ontology-based data integration 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: Ontology-based data integration is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data integration, Data management, Ontology (information science), so understanding it makes those chapters shorter.
In everyday life: Look for Ontology-based data integration 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 Ontology-based data integration in 20 minutes

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

Frequently asked questions

What is Ontology-based data integration in simple terms?

Why does Ontology-based data integration 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 Ontology-based data integration?

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 Ontology-based data integration.