The schema.org vocabularies were created by major search engines to improve the discovery of information on the web. Webmasters and information architects can use the metadata elements defined in schema.org vocabularies to describe their web pages and things on their websites. Please explore this topic and try to find out whether and how schema.org vocabularies have been used in web design, and whether webmasters or information architects need metadata training to implement schema.org vocabularies.
References: The reference list should include a minimum of 7 scholarly resources. Base your reference style on the latest edition of the APA .
Comprehension: Comprehension of materials; ability to articulate understanding with clarity
Completeness: Complete discussion on key points; appropriateness of supporting resources and materials
Critical thinking & analysis: ability to establish relationships and synthesize ideas; ability to reflect on the material with an eye to raising serious, critical questions
Structure & logic: organization of paper; logical flow of ideas in the paper
Format: Grammar, spelling, punctuation; style manual; word limit
Schema.org is a collaborative platform created by the most powerful search engines to deliver a space where communities can come together to create, promote, and maintain schemas. These search engines included Yandex, Google, Yahoo, and Microsoft. Schema can be defined as the representation of a model/outline that provides standardization for plan or theory development. Currently, schema.org provides this representation for emails, websites, the internet, and so on. Schema.org as developed its vocabulary that can be used with diverse coding styles e.g., JSON-LD, RDFa, and Microdata. In web designs, these vocabularies have been used by webmasters to markup emails and their pages. Furthermore, schema.org vocabularies are well known to cover relationships between entities and actions while at the same time serving as extensions for models. The founders developed this platform for webmasters and information architects to share vocabulary and reap maximum benefits for their work. Schema.org provides a way for webmasters to create extensions that support web systems’ diverse functionality to improve search results across various enterprise websites.
Schema.org is termed as the de facto vocabulary for web design as it is used to interact with web entities and actions. The main aim for web design to be functional under schema vocabularies was to see that they were adaptive, capable, autonomous and communicative (Stavrakantonakis et al., 2014). These vocabularies act by standardizing the website metadata such that sites can interact with each other without much complication. The communities on schema.org have worked tirelessly over the years to ensure the vocabularies and ontologies on websites are machine-understandable. Web agents rely on these semantic annotations by the vocabularies to perform as expected and give end-users the promised product (Stavrakantonakis et al., 2014). Schema-or had the vision to accelerate the development of semantic annotations, which has proved to be successful, as seen in the following examples concerning big data and the tourism sector. These real-world applications can be used to show how schema.org vocabularies are used in web design to link entities and actions.
Karle et al., (2017), evaluate the use of schema.org vocabulary in hotel websites by analyzing the available schema and proposing an extension of the library. Since the platform’s founding in 2011, users have continuously added to the available content to give a wide range of available metadata. However, the authors claim that up till version 3.0 of the vocabulary, the hotel industry was disadvantaged as the available data was too shallow. Present in schema.org/hotel were only descriptions for core data such as contacts, descriptions and names. Other crucial information, such as hotel rules, available amenities, and quantities, were impossible to include. Therefore, it was up to the authors to come up with an extension that allowed for ten new properties and 12 new types that would help improve hotel websites to be machine-readable and understandable. Through the research by Karle et al. (2017), the extension’s implementation will enable webmasters to describe their hotels in depth. Considering the popularity of schema.org, chances are high that the extension was adopted highly by most hotel website designers.
While schema.org vocabularies have been explored in the hotel industry, big data is one area where the practice has been extensively exploited. Not long ago, all of the web design relied on HTML. This extended to structured databases that held reservation engines, web searches, and price comparisons. Unfortunately, this method was cumbersome and unreliable since a change in structure could cause the site to break (Katumba, 2015). On top of that, extracting data had to be done by custom build extractors that would convert HTML to structured data. The introduction of schema.org helped eradicate such issues since it provided for a standardized scheme for planning databases (Guha, Brickley, and MacBeth, 2015). This has led to the ability to extract big data and manipulate it for added advantage. Webmasters are now able to integrate data warehousing within websites to support their appropriate enterprises. According to Abdelmgeid et al. (2013), web data is mapped to the vocabulary of schema.org. This leads to show that schema.org vocabulary is not only vital to planning but also data transformation, which is a powerful tool in today’s world.
Training is essential for webmasters or information architects when they need to implement schema.org vocabularies. A lot of entails schema.org metadata, such as everyday life metadata and the different types of metadata (Riley, 2017). The first lesson a webmaster learns about metadata is that it is key to content holding systems (). Platforms such as Instagram, Spotify, or YouTube rely on metadata for users to locate data. This is the everyday life of metadata. It is because of metadata that entities are connected to actions. When users online search for things, places and people, they can get results that form a relationship to what they are searching (Riley, 2017). But, for a webmaster to perform these functions, they need to understand the cultural and heritage world that encompasses museums, archives, and libraries. Learning the difference between these worlds enables a webmaster to create and share robust and structured metadata.
Moreover, webmasters need to understand the metadata types in schema.org vocabularies to create different use cases in information systems. Once an information architect can distinguish between markup languages, descriptive, administrative, and structural metadata, it becomes easy for them to work around diverse systems (Riley, 2017). Descriptive data is essential for understanding resources while administrative data is technical, essential for preservation, and inclusive of rights. Administrative data focuses on decoding files, managing files in the long term and observing intellectual property rights. Markup languages are essential for highlighting structural or semantic features that are part of a larger content. Structural metadata connects the parts of resources with each other hence creating a relationship. All in all, these are essential fields that a webmaster should be trained about to understand schema.org vocabulary and how to use it effectively.
In conclusion, schema.org vocabulary has, over the years, been able to improve search engine results as it formulated a way for webmasters to use standardized schemas that support diverse web designs. The hotel industry and big data analytics are two enterprises that give examples of how vocabulary is used to enhance web designs. Unlike traditional approaches that called for HTML and extractors to retrieve data, schema.org vocabulary has formed a foundation for easy extraction of data that can be transformed into insight. Besides that, schema.org has given webmasters the ability to extend vocabularies to find the world’s different enterprises. However, there is a need for information architects and webmasters to train adequately since schema.org vocabularies offer a wide range of data that needs to be understood first. Once achieved, webmasters can manipulate schema.org vocabularies to reap maximum benefits.
Ali, A. A., Abdelrahman, T. A., & Mohamed, W. M. (2013). Using schema matching in data transformation for warehousing web data. International Journal of Information Technologies and Knowledge, 7, 230-240.
Guha, R. V., Brickley, D., & MacBeth, S. (2015). Schema. org: Evolution of structured data on the web. Queue, 13(9), 10-37.
Kärle, E., Simsek, U., Akbar, Z., Hepp, M., & Fensel, D. (2017). Extending the schema. org vocabulary for more expressive accommodation annotations. In Information and Communication Technologies in Tourism 2017 (pp. 31-41). Springer, Cham.
Katumba, S. K. (2017). Empirical tests using search engine optimisation techniques to compare the effectiveness of two metadata vocabularies for geospatial data discovery on the Web (Doctoral dissertation, University of Pretoria).
Loewenstein, J., Ocasio, W., & Jones, C. (2012). Vocabularies and vocabulary structure: A new approach linking categories, practices, and institutions. Academy of Management Annals, 6(1), 41-86.
Riley, J. (2017). Understanding metadata. Washington DC, United States: National Information Standards Organization (http://www. niso. org/publications/press/UnderstandingMetadata. pdf), 23.
Stavrakantonakis, I., Fensel, A., & Fensel, D. (2014, September). Matching Web Entities with Potential Actions. In SEMANTICS (Posters & Demos) (pp. 35-38).