Local Search Engine Marketing using and understanding Google Maps & Yahoo Local Search is more complex than traditional backlinks from websites: Listed below are terms and definitions that infleunce Local Search.
Local Search Engine influencing SEO Factors:
Document Identification – The search engine looks for previously indexed relevant documents in a search database in response to a query. This document data can include a universal resource locator (URL) that provides a link to a document, web page, or to a location from which a document or web page can be retrieved or otherwise accessed by the user, data indicating one or more locations with which documents are associated, and data corresponding to the text of the documents.
Topic Score – Various information retrieval and other techniques used by conventional search engines are used to determine the relevance of a document, such as text information, link information and link structure, personalized information, etc. This topical score is generated from various sources and signals other than location information. A topic score is also used to find advertisements relevant to a target document.
Distance Score – One or more locations is determined to be associated with each of the identified documents, and a distance score is calculated for each based, at least in part, on the distance between the location(s) associated with the document and the location associated with the search query
Combined relevance score – The topical scores and distance scores could be merged to yield a combined relevance score for a document. The combined relevance score may result in different ranking orders than if documents were ranked by relevance to a topic or by distance alone.
Location Extraction – During a Web search, the search terms may indicate the name of a geographic area, and a local search might be done when that geographic area is unambiguous enough.
Ambiguous Search Query – The names of some geographic areas correspond to common words (e.g., Mobile), and it can be hard to tell if a searcher was referring to a location in their search.
Authoritative Document – The identification of a document or web page (URL) that is associated with a business at a location. This system determines documents that are associated with a location, identifies a group of signals associated with each of the documents, and determines authoritativeness of the documents for the location based on the signals.
Candidate Documents – Documents associated with a particular location, they may be analyzed to identify snippets of text (where a snippet of text may be defined as a portion of a document or the entire document) that include information associated with the location, such as a full or partial address of the location, a full or partial telephone number associated with the location, and/or a full or partial name of a business associated with the location. Links from these may point to the authoritative document. Other signals may be viewed to determine which candidate document is the authoritative document amongst the group of candidates, such as domain names, business name used in anchor text, etc.
Document Segmentation – A document may be segmented based on a visual model of the document. The visual model is determined according to an amount of visual white space or gaps that are in the document. The visual model is used to identify a hierarchical structure of the document, which may then be used to segment the document.
Geographic Signals – Information related to a geographic locations, such as full or partial mailing address or telephone number, or name of a business. A page may be filled with different geographical signals, which are segmented from each other by visual gaps. Example: a web page may include a list of restaurants in a particular neighborhood and a short synopsis or review of each restaurant. Or, a page may be filled with multiple reviews of the same restaurant, and segmentation may be used to separate those.
Indexing by Geographical Relevance – Indexing documents relevant to a geographical area by indexing, for each document, multiple location identifiers that collectively define an aggregate geographic region. When creating the index, the search engine may determine a set of geographical areas surrounding a geographical area relevant to a document and associate references to the set of geographical areas with the document index.
Geographical Regions – With some local search engines, the local geographic region of interest is a region defined by a certain distance or radius from a starting location, such as a certain number of miles from a zip code or street address. Ideally, the local search engine should efficiently locate and return relevant results in the desired geographic region.
Location Identifiers – Documents in a database may each be associated with a geographical region. The region may be specified by a location identifier associated with the document. Location identifiers might be derived from a model of the Earth’s surface using a hierarchical grid, such as the well known Hierarchical Triangular Mesh (HTM) model.
Geographically Relevant Documents – Any document that, in some manner, has been determined to have particular relevance to a geographical location. Business listings, such as yellow page listings, for example, may each be considered to be a geographically relevant document that is relevant to the geographic region defined by the address of the business. Other documents, such as web pages, may also have particular geographical relevance. Example: a business may have a home page, may be the subject of a document that comments on or reviews the business, or may be mentioned by a web page that in some other way relates to the business. The particular geographic location for which a document is associated may be determined from postal address or from other geographic signals.
Aggregate Geographic Region – A local search engine efficiently indexes documents relevant to a geographical area by indexing, for each document, multiple location identifiers that collectively define an aggregate geographic region. When the index is used to respond to individual search queries, the aggregate geographic region may be efficiently searched by merely adding a location identifier to the search query.
Geo-Relevance Profile – A geographic location may be associated with a string of text in a document by looking at a geo-relevance profile that contains that geographic information. A geo-relevance profile is built by looking at a number of documents relating to a business at a specific location.
Known Geographic Signals – A known geographic signal may include, for example, a complete address that unambiguously specifies a geographic location. The geographic signal can be located by, for example, pattern matching techniques that look for sections of text that are in the general form of an address. For example, location classifier engine 100 may look for zip codes as five digit integers located near a state name or state abbreviation and street names as a series of numerals followed by a string that includes a word such as “street,” “st.,” “drive,” etc. In this manner, Location classifier may locate the known geographic signals as sections of text that unambiguously reference geographic addresses.
ocation Identifier Fields – Collected Information based upon types of geographic signals which are filled with text selected for each geographic signal. An example may be zip codes corresponding to the geographic signals.
Zip Codes – Postal codes, which can be used as a geographic signal. They tend to be particularly useful to use as an identifier for a geographic location because zip codes that are close to one another numerically tend to correspond to locations that are close to one another geographically.
Confidence Scores – When a system identifies a document that includes an address and locates business information, that system may assign a confidence score to the business information, where the confidence score relates to a probability that the business information is associated with the address. The system determines whether to associate the business information with the address based on the assigned confidence score.
Local Item Extraction – When looking at a document, attempting to assign a location and assign confidence scores to that document by looking at the business information on the page, at terms that preceed the address to see if any are a business name, and if there are telephone numbers, whether or not the numbers are associated with that business. Landmarks associated with the business may also be identified and assigned a confidence score.
Business Information – A business name (also referred to as a “title”), a telephone number associated with the address, other information related to a business.
Yellow Pages Data – Information commonly associated with a business that is taken from a telecom directory. Some addresses may not have associated yellow pages data or possibly incorrect yellow pages data. Businesses with associated yellow pages data may be used as part of a training set used to extract location information from pages that don’t have associated yellow pages data. The documents in the training set may be analyzed to collect features regarding how to recognize business information in a document when the document includes an address.
Landmarks – Information about the location of a business, such as a postal address. This information is tied to attributes of the landmarks such as business name, telephone number, business hours, or a link to a web site or a map) in a document. In other implementations, the above processing may apply to other landmarks and attributes, such as finding the price (attribute) or a product identification number (attribute) associated with a product (landmark).
Geographic Location Identifier – may be a partial or complete postal address, telephone number, area code, etc or any other suitable value associated with a physical geographic position, such as longitude and latitude. The geographic location identifier may be based on links, such as hyperlinks, that connect the nodes in the collection of documents – based upon a relevancy of the web documents to each other.
Geographic Relevancy Criteria – Geographic location identifiers included within web pages may be assigned to other web pages that may or may not contain that information, if certain relevancy criteria is in place. This means that web pages that either do not include geographic descriptive information or include unrefined or incomplete geographic location information could be searched or identified based on an assigned geographic location identifier. Document relevancy may be determined based on several factors, such as relative distance between documents, terminology used, and local or web site determination. Example: a home page for a Web site doesn’t contain any address information, but the site has that information on an “About us” page, a “contact page,” and a “directions” page – if certain critieria as defined in the patent application is met, then the home page is seen by the search engine as being relevant for the address information on those other pages.
Forward or Outbound Link – A link originating from a first page and leading to a second page may be called a forward or outbound link relative to the first page and indicate that the first page is a linking document.
