Publish and Search Relational DatabasesEnth blends database publishing and search to unlock the value of Internet-connected databases and dramatically increase the ease and speed of finding information. Our patented software platform allows you to easily share structured data online and provide users with a quick way to find information using plain text, achieving time savings, cost savings and increased productivity for your organization.
"Discovery Hub is an exploratory search engine built on top of the famous encyclopedia on the web, Wikipedia. The exploratory search is a new way to search the web, not to find what you are searching, but to find what you are not searching, and might be intersting for y"
"Jot's specialty is finding types of keywords on web pages like people, companies, countries and many others. Save this bookmarklet AskJot to your bookmarks toolbar, click it while your at a page and Jot will find keywords on that page for you."
search engine to locate a college by location (california, tampa, portland or, etc), by major (biology, engineering, veterinary medicine, law) by degree (masters, phd) or all of the above (law degree in portland oregon).
FactForge represents a reason-able view to the web of data. It aims to allow users to find resources and facts based on the semantics of the data, like web search engines index WWW pages and facilitate their usage.
Real-Time Faceted Search is a journey; a journey that enables you to easily tell the internet what you're looking for and let the internet find it for you. It starts with the same key word everyone is familiar with, but then guides the user through a series of contextual prompts, or 'facets', that eventually build out a more 'complex query' more powerful than that of typical keyword search. Through the use of a number of GUI techniques, the user is able to either select or fill in elements of this query in intuitive ways with all the code crunching being hidden in the background. The query can then be run over mashed up linked data sources. This enables the everyday user to build out these deep queries over linked data and receive much more relevant results.
Find relevant images of Trending Topics in realtime / Nachofoto is a Semantic Time-based Vertical Image Search Engine. Our focus has been to deliver Fresh and Relevant image results for High Volume and Trending queries. Our goal is to change the way people interact with image search engines.
"Dataset Search enables users to find datasets stored across the Web through a simple keyword search. The tool surfaces information about datasets hosted in thousands of repositories across the Web, making these datasets universally accessible and useful."
'Semantic Web search engines are applications for finding ontologies that require reasonable effort: queries are usually written as natural language keywords and results are ranked. Some additional information is often provided.'
SenseBot (Beta) is a semantic search engine that generates a text summary of multiple Web pages on the topic of your search query. It uses text mining and multidocument summarization to extract sense from Web pages and present it to the user in a coherent manner. A "Semantic Cloud" of concepts is displayed above the summary, allowing to steer the focus of the results. To learn about our approach, go to the About SenseBot page, or browse Samples.
Any technology that stands a chance to dethrone Google is of great interest
to all of us, particularly one that takes advantage of long-awaited and much-hyped semantic technologies. But no matter how much progress has been made, most of us are still underwhelmed by the results.
In head-to-head comparisons with Google, the results have not come out much different.
We all know that semantic technologies are powerful, but how and why?
The mistake is that semantic search engines present us with Google-like search box and allow us to enter free form queries.
So we type the things that we are used to asking - primitive queries.
The situation is made more difficult by the fact that
right now there is only a thin range of problems where semantic search can clearly do better. This range is
complex queries involving inferencing and reasoning over a complex data set.
Sadly, natural language
processing gives little advantage when it comes to this category of problems.
Before looking at the problems that are perfect for semantic search, lets look at the hardest problems.
These are computationally challenging problems that really have nothing to do with understanding semantics.
There are fundamental
limits to what we can compute, and a class of problems that have an exponential number of possible solutions
is not going to be magically solved because we represent data as RDF.
The good news is that there is a set of problems that are great for semantic search. These are the problems we have been solving so wonderfully with relational database.
At its most structured extreme we find
Freebase - the semantic database of everything. Freebase is accessible via
free text search, but more importantly via MQL (Metaweb Query Language).
Companies like Hakia and Powerset are probably working the hardest. These companies are
trying to simultaneously build Freebase-like structures on the fly and then do natural language
queries on top of them. The difference is that Hakia is using (likely similar) technology to query over
the entire web, while Powerset has (probably shrewdly) chosen to restrict the search to Wikipedia.
Here is the problem - the natural language interface has nothing to do with the underlying data representation.
Fundamentally, Hakia, Powerset, and Freebase
are databases. Fundamentally, all of them have some kind of Natural Language Processing that translates the question
into a canonical query over the database.
Having a simplistic search interface hurts Powerset and Hakia, and
to a lesser extent Freebase, which is not positioning itself as generic search.
Instead, the expectation should really be to solve the
problems that can not be solved by Google today.