Search Computing Blog

Google Shopping API

Google announced the release of the Shopping API, a new set of Web Application Programming Interfaces that are meant to substitute the existing Google Base APIs. The new Shopping Application Programming Interfaces (APIs) have two main components: Content and Search. Those components are part of a unique CRUD infrustructure for product data management.

On one hand, the Content API enables retailers to upload their product data to Google, and to make incremental updates to frequently changing attributes like price and availability.

On the other hand, the Search API provides access to product data. After creating a new project in the APIs console, a developer can issue JSON queries as the following one:

https://www.googleapis.com/shopping/search/v1/public/products?key=key&country=US&q=digital+camera&alt=atom

This query will return a feed pf products sold in the United States which are all matching the keywords digital and camera. With a registered account, the new Google Shopping API feature a default limit: 2,500 queries/day

The API supports both structured and free text search. Results can be ordered according to relevance, novelty, or price. It is possible to increase diversity in the set of products matching a query by using the APIs crowding mechanism to restrict the number of products with an equivalent property.

The Google Base API will be fully deactivated on June 1, 2011. Some non-shopping data types (such as jobs, real estate, events, and activities) won’t be supported anymore.

Yebol is a meteaseach engine that utilizes a combination of algorithms paired with human knowledge to build a Web directory for each query and each user. Instead of the common “listing” of Web search queries, Yebol automatically clusters and categorizes search terms, Web sites, pages and contents.

Yebol claims to allow for multi-dimensional search results, but the actual meaning is that it can explore at once several web sources (web, images, twitter, videos, and so on). So: is it just a metasearch engine with a nice interface or something more?

Search engines are exploiting more and more named entity identification in the query analysis phase.
Besides increasing the precision of the results, this enables the generation of result pages more suitable to the typical needs of users with respect to the identified entities.

The attached document considers queries returning mono-domain results, where the domain represents a specific field of interests such as City, People, Movies, etc. and characterizes the problem for the definition of the layout of such results. In particular, it analyzes the behaviour of the main current search engines (Google, Bing, Yahoo) according to the result page layout definition issue.

search engines: page layout analysis

The final objective is to describe a conceptual definition of the Web search result layout problem, by identifying: the parameters involved in the layout design, the tuning dimensions available for optimizing the result layout, and the possible strategies that can be adopted for producing such layouts.

This has been the topic of a position paper at the DataView workshop within the OTM conference 2010, Crete, Grece.

While most of the people identify Web search with Google, experts of the field and Web addict know that there is much more than that around.
New search engines are coming out every day. Here are a few examples that somehow relate to Search Computing:

1. Goby (http://www.goby.com/)

The payoff tells you all: “Create your own adventure”. Pretty similar application to our “plan a night out” scenario and demo. Several kinds of results are returned, categorized and associated with several additional details (photos, address, purchase info). I see three big differences with respect to SeCo:

• no clear and configurable ranking criteria are exposed
• only items of one single type are returned (e.g., a list of concerts)
• no exploration towards other item types is allowed

Goby is available also for mobile:

2. FanSnap (http://www.fansnap.com)

This is a vertical search engine for celebrities and events. It’s focused on ticket purchase. Again, just one concept at time (in this case, the event) with fixed sorting criterion (the date of the event).

3. SurfCanyon (http://surfcanyon.com)

SurfCanyon aims at improving the understanding of user intents by asking for suggestions while the search is performed.  While the first searches retrieve diversified results to try to catch all the intents, after some executions (and explicit declarations of the user) the system learns the typical user intents and exploits them in the subsequent searches .

4. Blekko (http://www.blekko.com)

This one appears as a classical keyword based search engine, but it provides a new feature: slashing the web. This means that search keywords can be combined with slash commands (e.g., /liberal, …) that tell the engine to retrieve only contents that contain opinions in line with the viewpoint or political orientation specified by the slash.

5. Siri (http://siri.com/)

This engine is a good representative of the specific category of question answering / task solver systems. It lets the user state his need and tries to accommodate it. Categories and concepts are recognized and retrieved. Context from previous searches is also considered.

6.  Mobile search engines

Here is a shortlist of the search engines now available also for mobile platforms:

With the advent of the Web, search has become the prominent paradigm for information seeking, both across the online space and within enterprises. Search frameworks and components can be used to build search-based applications in diverse vertical fields. However, no precise engineering methods and approaches have been devised for this class of applications.

This tutorial, offered at the 10th International Conference on Web Engineering, presents the peculiarities of advanced Web search applications, describes some tools and techniques that can be exploited, and offers a methodological approach to development. The approach proposed in this tutorial is based on the paradigm of Model Driven Development (MDD), where models are the core artifacts of the application life-cycle and model transformations progressively refine models to achieve an executable version of the system. To cope with the process-intensive nature of the main interactions (i.e., content analysis, query management, etc.), we describe the use of Process Models (e.g., BPMN models). Indeed, search-based applications are considered as process- and content-intensive applications, due to the trends towards exploratory search and search as a process visions.

SearchMonkey allows developers and site owners for making Yahoo! Search results more useful and visually appealing, and drive more relevant traffic to their sites.

• Technology analysis
  • Site owners are invited to make structured data available as Microformats, RSS, RDF or any data feed
  • Developers are invited to program SearchMonkey application that uses the structured data made available by site owners in making results more useful and visually appealingFinal users are invited to add SearchMonkey applications to their Search Gallery in order to get more useful and visually appealing results
• Business model analysis
  • site owners can gain more traffic for their sites, thus more visibility of their contents and selling advertisement spaces to a higher price.
  • developers can gain by offering consultancy services
  • Yahoo! gains in terms of traffic (thus higher price for sponsored links) more useful and visually appealing results

[Website http://developer.yahoo.com/searchmonkey/]

LeapFish is a search aggregator that retrieves results from other portals and search engines, including Google, Yahoo, Live Search, Blogs, Videos etc…. It is a registered trademark of Dotnext Inc.

Leapfish is a type of metasearch site known as a search aggregator. Search aggregators compile and list the results taken from other search engines, in addition to providing their own content (generally in the form of advertising or result positioning based on internal algorithms.

In the case of LeapFish, the top results of any given search can be advertisements, where an individual or company can pay a registration fee to LeapFish to be positioned at the top of the result list in response to certain keyword searches.

[Surce Wikipedia http://en.wikipedia.org/wiki/Leapfish]

[Website http://www.leapfish.com/]

Powerset is a company based in San Francisco, California that is developing a natural language search engine for the Internet.

Powerset is working on building a natural language search engine that can find targeted answers to user questions (as opposed to keyword based search). For example, when confronted with a question of the form ‘which U.S. state has the highest income tax?’, conventional search engines ignore the question and instead do a search on the keywords ‘state, income and tax’. Powerset’s product, on the other hand, attempts to use natural language processing to understand the nature of the question and then to search and return a subset of the web that contains the answer to the question. If it works, results from Powerset’s search engine would have a higher relevance than results from a keyword search engine. From a commercial standpoint, advertising on the results page could also be more relevant and could have a higher revenue potential than that of keyword search engines.

Currently, the company is in the process of “building a natural language search engine that reads and understands every sentence on the Web.” The company has licensed natural language technology from PARC, the former Xerox Palo Alto Research Center.

On May 11, 2008, the company unveiled a tool for searching a fixed subset of Wikipedia using conversational phrases rather than keywords.

On July 1, 2008, Microsoft signed an agreement to acquire Powerset

[Source Wikipedia http://en.wikipedia.org/wiki/Powerset_(company) ]

[Website http://www.powerset.com/]

Cuil (pronounced [ku?l], “cool“, according to the creators) is a search engine that organizes web pages by content and displays relatively long entries along with thumbnail pictures for many results. It claims to have a larger index than any other search engine, with about 120 billion web pages. It went live on July 28, 2008.

Cuil is managed and developed largely by former employees of Google: Anna Patterson, Russell Power. The CEO and co-founder, Tom Costello, has worked for IBM and others. The company raised $33 million from venture capital firms including Greylock.

[Source Wikipedia http://en.wikipedia.org/wiki/Cuil]

[Website http://www.cuil.com/]