| 词条 | Vertical search |
| 释义 |
A vertical search engine is distinct from a general web search engine, in that it focuses on a specific segment of online content. They are also called specialty or topical search engines. The vertical content area may be based on topicality, media type, or genre of content. Common verticals include shopping, the automotive industry, legal information, medical information, scholarly literature, job search and travel. Examples of vertical search engines include the Library of Congress, Mocavo, Nuroa, Trulia and Yelp. In contrast to general web search engines, which attempt to index large portions of the World Wide Web using a web crawler, vertical search engines typically use a focused crawler which attempts to index only relevant web pages to a pre-defined topic or set of topics. Some vertical search sites focus on individual verticals, while other sites include multiple vertical searches within one search engine. BenefitsVertical search offers several potential benefits over general search engines:
Vertical search can be viewed as similar to enterprise search where the domain of focus is the enterprise, such as a company, government or other organization. In 2013, consumer price comparison websites with integrated vertical search engines such as FindTheBest drew large rounds of venture capital funding, indicating a growth trend for these applications of vertical search technology.[1][2] Domain-specific searchDomain-specific verticals focus on a specific topic. John Battelle describes this in his book The Search (2005):
In the domain-specific setting one can combine the tf-idf approach implemented via an inverse index with semantic approaches of semantic headers and semantic skeletons. Instead of most frequent keywords, a set of entities is extracted from a portion of text to be matched against a potential question. This allows much more flexibility due to real-time reasoning capabilities while matching questions and answers in the form of semantic headers.[4] Any general search engine would be indexing all the pages and searches in a breadth-first manner to collect documents. The spidering in domain-specific search engines more efficiently searches a small subset of documents by focusing on a particular set. Spidering accomplished with a reinforcement-learning framework has been found to be three times more efficient than breadth-first search.[5] References1. ^{{cite web|last=Rao|first=Leena|title=Data-Driven Comparison Shopping Platform FindTheBest Raises $11M From New World, Kleiner Perkins And Others|url=https://techcrunch.com/2013/03/05/data-driven-comparison-shopping-platform-findthebest-raises-11m-from-new-world-kleiner-perkins-and-others/|publisher=TechCrunch|accessdate=27 May 2013}} 2. ^{{cite web|last=HO|first=VICTORIA|title=Asian Price Comparison Site Save 22 Gets Angel Round Of “Mid Six Figures”|url=https://techcrunch.com/2013/05/11/asian-price-comparison-site-save-22-gets-angel-round-of-mid-six-figures/|accessdate=27 May 2013}} 3. ^{{cite book |last=Battelle |first=John |title= The Search: How Google and its Rivals Rewrote the Rules of Business and Transformed Our Culture |publisher= Portfolio |location= New York |year= 2005 }} 4. ^{{cite journal |last=Galitsky |first=Boris |title=Building a Repository of Background Knowledge Using Semantic Skeletons|publisher=AAAI |journal = AAAI Spring Symposium: Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering|year=2006 }} 5. ^{{cite journal |last=McCallum |first=Andrew|title=A Machine Learning Approach to Building Domain-Specific Search Engines|url=http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.3818&rep=rep1&type=pdf|journal = IJCAI|volume = 99|pages=662-667|year=1999}} 2 : Internet search engines|Domain-specific search engines |
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