Growing the Business with Search, Semantic, and Recommendation Technologies

Suggested Answers:

1. Grimes (2010) provides a list of practical benefits that could result from semantic search technology:

Related searches/queries. The engine suggests alternative search queries that may produce information related to the original query. Search engines may also ask you, “Did you mean: [search term]?” if it detects a misspelling. (This already happens with some.)

Reference results. The search engine suggests reference material related to the query, such as a dictionary definition, Wikipedia pages, maps, reviews, or stock quotes.

Semantically annotated results. Returned pages contain highlighting of search terms, but also related words or phrases that may not have appeared in the original query. These can be used in future searches simply by clicking on them.

Full-text similarity search. Users can submit a block of text or even a full document to find similar content.

Search on semantic/syntactic annotations. This approach would allow a user to indicate the “syntactic role the term plays—for instance, the part-of-speech (noun, verb, etc.)—or its semantic meaning—whether it’s a company name, location, or event.” For instance, a keyword search on the word “center” would produce too many results. Instead, a search query could be written using a syntax such as the following:

<organization> center </organization>

This would only return documents where the word “center” was part of an organization’s name. Google currently allows you to do something similar to specify the kind of files you are looking for (e.g., filetype:pdf)

Concept search. Search engines could return results with related concepts. For instance, if the original query was “Tarantino films,” documents would be returned that contain the word “movies” even if not the word “films.”

Ontology-based search. Ontologies define the relationships between data. An ontology is based on the concept of “triples”: subject, predicate, and object. This would allow the search engine to answer questions such as “What vegetables are green?” The search engine would return results about “broccoli,” “spinach,” “peas,” “asparagus,” “Brussels sprouts,” and so on.

Semantic Web search. This approach would take advantage of content tagged with metadata as previously described in this section. Search results are likely to be more accurate than keyword matching.

Faceted search. Faceted search provides a means of refining results based on predefined categories called facets. For instance, a search on “colleges” might result in options to “refine this search by. . .” location, size, degrees offered, private or public, and so on. Faceted search tools available today tend to focus on a specific domain, such as Wikipedia or Semidico, a search tool for biomedical literature.

Clustered search. This is similar to a faceted search, but without the predefined categories. Visit Carrot2.org to better understand this concept. After conducting a search, click on the “foamtree” option to see how you can refine your search. The refining options are extracted from the content in pages of the initial search.

Natural language search. Natural language search tools attempt to extract words from questions such as “How many countries are there in Europe?” and create a semantic representation of the query. Initially, this is what people hoped search engines would evolve toward, but Grimes wonders if we have become so accustomed to typing just one or two words into our queries that writing out a whole question may seem like too much work.

2. Much of the world’s digital information is stored in files structured so that they can only be read by the programs that created them. With metadata, the content of these files can be labeled with tags describing the nature of the information, where it came from, or how it is arranged, essentially making the Web one large database that can be read and used by a wide variety of applications.

The semantic Web will make it possible to access information about real things (people, places, contracts, books, chemicals, etc.) without worrying about the details associated with the nature or structure of the data files, pages, and databases where these things are described or contained (Hendler and Berners-Lee, 2010).

3. The first stage was Web 1.0 (The Initial Web) - A Web of Pages. Pages or documents are “hyperlinked,” making it easier than ever before to access connected information.

The first stage was Web 2.0 (The Social Web) - A Web of Applications. Applications are created that allow people to easily create, share, and organize information.

The third stage is Web 3.0 (The Semantic Web) - A Web of Data. Information within documents or pages is tagged with metadata, allowing users to access specific information across platforms, regardless of the original structure of the fi le, page, or document that contains it. It turns the Web into one giant database.

4. Context defines the intent of the user; for example, trying to purchase music, to find a job, to share memories with friends and family

Personalization refers to the user’s personal characteristics that impact how relevant the content, commerce, and community are to an individual.

Vertical search, as you have read, focuses on finding information in a particular content area, such as travel, finance, legal, and medical.

The current Web is disjointed, requiring us to visit different websites to get content, engage in commerce, and interact with our social networks (community). The future Web will use context, personalization, and vertical search to make content, commerce, and community more relevant and easier to access (Mitra, 2007).

5. The semantic Web utilizes additional languages that have been developed by the W3C. These include resource description framework (RDF), Web ontology language (OWL), and SPARQL protocol and RDF query language (SPARQL).

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A Search for Meaning—Semantic Technology

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

Faceted search

Faceted search provides a means of refining or filtering results based on predefined categories called facets

For instance, a search on “colleges” might result in options to “refine this search by. . .” location, size, degrees offered, private or public, and so on

Many ecommerce websites provide users with faceted search features, allowing shoppers to filter search results by things like price, average rating, brand name, and product features

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