Maybe you have noticed it already; today in the morning something new appeared at Google’s search engine interface: A bunch of corresponding search-suggestions based on your search query. Google spoke about this enhancement:

Starting today, we’re deploying a new technology that can better understand associations and concepts related to your search, and one of its first applications lets us offer you even more useful related searches (the terms found at the bottom, and sometimes at the top, of the search results page).

I tried it. So, if you type in “time travel” you also get search proposals like “theory of relativity time travel” or “wormhole time travel”. Google annouced, that the service is available in various languages. The direct test with German is a little disillusioning: Searching for “zeit reise” (which is the same concept as above, in german) leads to alternative searches like “reisen 50er jahren” (travel 50ies) and “reisen im mittelalter” (travel in the medieval).

Even if this semantic-like extension of the basis search function still needs some tuning, the point is getting clearer: Also Google is doing developments to get more meaningful results into their search algorithms. And parts of the semantic methodology are finding their way into mainstream services like search engines – as we have seen with Wolfram Alpha some days ago. So keep your eyes open – maybe next morning you’ll find another piece of the semantic puzzle embedded into one of your favorite web-apps.

Another service which came up recently is Quintura, which provides search capabilities for your own blog with a visual map of tags or hints based on an index created of the own blog entries. It is easy to customize for the own blog’s style with the use of a simple interface. Quintura offers code-snippets to copy to your blog-post or sidebar. Even if it is no semantic search engine in the narrow sense, Quintura provide a fine semantic-like interface for a meaning-sensitive search. See how Quintura is implemented into The Semantic Puzzle at our sidebar.

Jinni provides recommendations, answering a free given search. Based on semantic technologies Jinni uses Natural Language Processing on plot, mood, style, setting, soundtrack and more in combination with an ontology, created by film professionals (like Jinni says). When it launched in December, Jinni had 10,000 movie, TV and video titles.

In Jinni you don’t need to know about exact title, actor, director, place or year of production to get an result, you can enter simply a phrase describing the mood, genre or place the movie is about, and you will guided through a facilitated search to narrow your search and get at the end what you want. Or alternative, if you search for a movie and you have only a vague idea of the plot, you can formulate a plot’s description in free phrasing. As it also offers APIs for Internet and TV content providers you can make your way direct to an online store to download or purchase the movie.

Another idea how to develop business orientated semantic web services comes with BooRah. BooRah is a service targeting restaurant owners to provide them reports of positive and negative reviews of food, service and ambiance at their restaurants. For that the service monitors negative and positive trends across hundreds of online review sites. Now restaurant owners can subscribe to receive a PDF of their monthly reports for an introductory price of $15 and a regular price of $25 per month. This PDFs came with charts, trends, rankings, summaries and some quotes from users, month by month. The reports may enable those restaurant owners to react and improve their services in the specific field. A simple but straight forward way to make money with semantic technologies

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What’s it about? Semantifind is an IE and FF browser plug-in that extends Google’s search functionalities, most notably through a typeahead functionality that allows you to refine your search results before hitting ‘enter’. ReadWriteWeb wasn’t too impressed though:

Unfortunately, SemantiFind is one of those tools that’s good in theory, but not so good in practice. When performing some test searches, results were not as precise as they should have been. For example, in the above-mentioned search for “Georgia,” a search for the U.S. state returned Google results for the country as well.

Ambiguities due to homonyms such as Georgia vs Georgia, or Java vs Java are among the faves of people who are trying to pitch a semantic tool to you – but I really wonder whether the effects of homonyms aren’t highly overrated? How often do people really search for these, and in particular search for these without context, i.e. further search terms such as in ‘Georgia Tech’, ‘Georgia war’, ‘Java Coffee’ or ‘Java bugs’?

I must say I was quite impressed by the choice of search terms offered, and if you (like me) are easy prey for the serendipity effect, then SemantiFind can please and distract you endlessly. Here is a preview of what appears if you enter ‘serendipity’ – please note the preview of possible descriptions and definitions which you get on the Google homepage with the plugin (click > big):

Once you pick a term it turns into a kind of button (just slightly annoying: you cannot edit a term after it’s turned into a button, but would have to delete the whole thing and type again if you want to change your search query):

And then, what happens? On the search results page, you see results filtered by SemantiFind’s user-generated, user-approved labels on top of the other search results – which irritated me at first as it comes across as a search engine within the search engine. Admittedly: I’d rather sift through 13 results than through 10,900,00 search results (even though I never make it to the end of Google’s search list anyway; does anybody?) – but does the article about trees doing their best work with thermostats at 70° really deserve the second rank in SemantiFind’s list of recommended search results?

So while I agree with RWW that this “just goes to show why search engines that rely on people to filter the results might not work. Human error shouldn’t be a factor in web searches”, I am still quite fond of the suggestions and definition previews. I would probably use SemantiFind regularly if they allowed me to configure the plugin in such a way that I’d get the suggestions on the input page, but not the recommended results on the results page.

What’s the source of these results anway? SemantiFind’s recommended results seem to rely entirely on input generated by users – to add input, you need to install their toolbar and start adding labels to websites; if a website has been labeled before, you can confirm or reject existing labels. What’s nice: a label recommender (only presumably the same one that’s used for search queries) reduces ambiguity. What’s curious: You can also browse the pages you have already labeled in what they call your “catalogue” – which makes the service even more reminiscent of a bookmarking service, and which makes me wonder whether one shouldn’t possibly link this with a del.icio.us/Mr.Wong/Bibsonomy/Faviki account (Faviki would probably be the best, considering their tag recommendations are based on DBpedia, and considering that Faviki just added 1 million new tags and now holds more than 5 million tags across all languages)

Questions that remain: I’d really like to know how they maintain their list of suggested labels – ambiguity, typos, plurals forms, i.e. the usual folksonomy issues must be a big challenge. Also, I’d like to know where they get their definitions in the preview from – from Google? Or are these user-generated as well? There must, after all, be some use for the “request a new definition” form?

Too bad they don’t have a blog to which one could send a track back, and there is nothing much on their company page either.

Personally I like the category “Bizarre / Strange Search Engines” most. Ever used Ms. Dewey, a Microsoft search engine …

that sings to you, insults you and lives under a highway flyover in a futuristic cityscape?

At first sight a really good laugh. Discussing it from a gender perspective would definitely be worth another post.

Rather than rely on superficial popularity metrics, Cuil searches for and ranks pages based on their content and relevance. When we find a page with your keywords, we stay on that page and analyze the rest of its content, its concepts, their inter-relationships and the page’s coherency.

Then we offer you helpful choices and suggestions until you find the page you want and that you know is out there. We believe that analyzing the Web rather than our users is a more useful approach, so we don’t collect data about you and your habits, lest we are tempted to peek. With Cuil, your search history is always private.

And of course there is the promise of the biggest index on the web ever, three times as big as Google’s, ten times as big as Microsoft’s… but size isn’t everything, I say, so let’s rather have a look at the (semantic) user experience.

Cuil did receive some praise for it’s (web 2.0.ish) appearance – meaning that the search field has, well, rounded edges and the search button comes with a shine (and the background is pitch black, so if Cuil manages to become as popular as Google, this might even reduce global warming:-). But the presentation of the search results is indeed pleasing to the semantic eye: The so-called “drilldown” shows related categories and instances for these categories and roll-over definitions for these terms. The search field recommends popular search terms, the results are organized in paragraphs, alternative searches are suggested in tabs – “Jamie Oliver” yields several alternative tabs, “RDF”, by comparison, doesn’t yield any.

On top of that, Cuil is REALLY fast – or at least today it is. Yesterday on Twitter, people were complaining about the page not loading – probably no surprise after the media frenzy that ensued after its launch. Lest I forget: Cuil was launched by former Google employees – and who would be in a better position to attack and cull (cheap pun) Google if not former Googlistas? Hence the frenzy.

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