Search Computing Blog

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?

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.

Apple has recently announced the acquisition of Siri, an application that includes voice recognition and search capabilities.

As some experts suggest (see John Battelle’s blog), Apple could be looking for a search engine for its App Store. As of April 8, 2010, there are at least 185,000 third-party applications available on the App Store.

The engine should not crawl the web and will not be a competitor of Google; it will suggests applications and services to be download/bought, according to the user interaction with the device. continue reading…

Today we discuss on an article published by the Search Engine Land blog, where the author is making a strong case for Search Computing.

The author says:

Business buyers use search engines throughout the entire research and buying process.

We couldn’t agree more. Next Generation Web Search will be a switch in user behavior paradigms: asRicardo Baeza-Yates and Prabhakar Raghavan state in the Search Computing: Challenges and Directions book:

… people do not really want to search, they want to get tasks done…

So, What are business buyers looking for?

…  According to a B2B survey conducted by Enquiro and Marketing Sherpa, buyers are primarily looking for:

1. Pricing information
2. Product information
3. Reviews and comparisons

As buyers move into the research and comparison phases, they start to formalize their needs and evaluate alternatives. Product information related to features and functionality, technical spec sheets, buyer guides and comparison charts are valuable.

An holistic view over such a variegated type of information requires machineries able to query the Web and (meaningfully) join correlated data. This is one of the goals of Search Computing, and you may enjoy a first example of our vision by trying our first demonstration application.