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Semantic Search Features and Benefits

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Reference results

Semantically annotated results

Full-text similarity search

Search on semantic/syntactic annotations

Concept search

Ontology-based search

Semantic Web search

Faceted search

Clustered search

Natural language search


Copyright ©2021 John Wiley & Sons, Inc.

Practical search features based on semantic search technology:




Semantic Web for Business


The most immediate challenge faced by businesses is the need to optimize their websites for semantic search.

Businesses need to think about the issue of context when creating content on their websites.

Page titles and keywords used in content should be selected for their ability to help search engines match the information with a user’s intent or the context of the search.

Copyright ©2021 John Wiley & Sons, Inc.



A Search for Meaning—Web 3.0 and Semantic Technology: Questions

List five different practical ways that semantic technology is enhancing the search experience of users.

How do metadata tags facilitate more accurate search results?

Briefly describe the three phases of the World Wide Web’s development.

Define the words “context,” “personalization,” and “vertical search.” Explain how they make for better information search results.

What are three languages developed by the W3C and associated with the Semantic Web?


Copyright ©2021 John Wiley & Sons, Inc.


Learning Objectives (5 of 5)


Copyright ©2021 John Wiley & Sons, Inc.



Web 2.0 Technologies



Using Search Technology for Business Success



A Search for Meaning—Web 3.0 and Semantic




Recommendation Engines



Social Web Tools and Applications





Recommendation Engines


Three widely used approaches to creating useful recommendations:

content-based filtering,

collaborative filtering, and

hybrid strategies

Copyright ©2021 John Wiley & Sons, Inc.



Content-based filtering

Recommends products based on the product features of items the customer has interacted with in the past.



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Collaborative filtering

Recommendations based on a user’s similarity to other people.

Copyright ©2021 John Wiley & Sons, Inc.





Other Approaches to Recommendation Engines


Knowledge-based systems use information about a user’s needs to recommend products.

Demographic systems base recommendations on demographic factors corresponding to a potential customer (i.e., age, gender, race, income, etc.).

Copyright ©2021 John Wiley & Sons, Inc.




Limitations of Recommendation Engines


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Cold start or new user






Limited feature content








Hybrid recommendation engines

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Recommendations based on some combination of the methodologies (content-based filtering, collaboration filtering, knowledge-based and demographic systems):

Weighted hybrid

Mixed hybrid

Cascade hybrid

Compound hybrid


Recommendation Engines: Questions

How is a recommendation engine different from a search engine?

Besides e-commerce websites that sell products, what are some other ways that recommendation engines are being used on the Web today?

What are some examples of user information required by recommendation engines that use collaborative filtering?

Before implementing a content-based recommendation engine, what kind of information would website operators need to collect about their products?

What are the four limitations or challenges that recommendation systems sometimes face?

What is a recommendation engine called that combines different methodologies to create recommendations? What are three ways these systems combine methodologies?


Copyright ©2021 John Wiley & Sons, Inc.



Copyright © 2021 John Wiley & Sons, Inc.

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