According to MIT's Technology Review, the program allows users to select email recipients by creating a search query as opposed to typing in names, addresses, or the name of a mailing list. For example, a user could use SEAmail to send a message to a group - created on the fly - like "all professors who graduated from Harvard University since 1960."

Being able to pinpoint your recipients in this way would not only be helpful, it could also stem the overflow of email that creates information overload by making sure that only the exact recipients who need to get the message are contacted.

A Semantic Email Chooser

In SEAmail, a email addressing interface (as shown here) could be used to build a list on the fly, without necessarily needing to know a single name. Instead, all an email user would need to do is fill in the parts of the form using the drop-down boxes to guide their selections along the way. The system would then take care of the rest.

Pulling from Databases

Obviously, in order for a semantically-based email system to work, there needs to be a rich database on the back-end that contains relevant information about the people sending email to each other and their interests. In the Stanford tests, this data is being retrieved from already existing databases which are then integrated into the system.

While such a thing may work well at a university like Stanford, it may be less useful to real-world businesses where there aren't numerous existing databases to draw upon, only Active Directory or some other resource management system.

Drawbacks and Concerns

1) Does this solve real problems?

There's also the small question as to whether SEAmail is solving a problem that really needs to be solved. Take for instance one of the touted benefits of the system: name resolution. The article provided an example where people wanting to send a message to "Michael Genesereth" could simply type his name as a recipient, and his most recent email address would automatically be selected. Sounds excellent, right? Except for one minor problem - that technology has existed in most email systems, and certainly within Microsoft Exchange, for years on end.

To continue the comparison with Microsoft Exchange, the advancements SEAmail makes have more to do with putting the power of creating these queries into the hands of users, who often don't get involved with the business of creating distribution lists, leaving it up to I.T. to do it for them. Exchange's built in ability to create query-based distribution lists already make it dead simple for admins to create lists based on almost any requirement you can dream up: city, state, organization, company, department, title, floor, supervisor, etc. In order for SEAmail to be revolutionary, it would need to do more than make it easy to create relevant lists - it would need to so while using much more detailed data about the intended recipients than any system allows for today.

2) Or Does it Create More Problems (like spam)?

Although in theory, the system's ability to pinpoint users could cut back on unintended email, there's also the possibility that the system could lead to more email. Oren Etzioni, director of the Turing Center at the University of Washington, has some concerns about the potential for misuse of the system. "This technology has clear benefits, but it's also ripe for misuse," he says. "The technical issues are solvable. The tricky things are the social issues. How do we create a workable system, given the vagaries of human nature?" Etzioni worries that a system that makes it too easy would lead to some people receiving overwhelming amounts of mail and no good way to limit it.

3) What about Social Networking?

Technology analyst Craig Roth thinks that the most glaring issue here is that the system doesn't take into account how social networks are being used by those who need to contact, market to, or inform others. He notes that consumers today can use LinkedIn, Facebook, MySpace, and Xing while enterprise customers have IBM Lotus Connections, MySites in Microsoft SharePoint, and the social search capabilities that were in BEA Pathways were merged into Oracle's secure enterprise search.  

Arriving Later this Year

Of course, it's hard to get a real feel for the potential of a system like this until it's actually put into practice. As it turns out, that will happen very soon. SEAmail will be launched at Stanford later this year, initially in the computer science department. It will later be rolled out to the rest of the university over time.

The basic premise of Cogito is that it transforms unstructured information into structured data. Out of this process comes a "semantic network", which is much the same thing as what rival company Cognition called a "semantic map" in our September '08 interview with them. It's important to point out that Cogito isn't necessarily a 'Semantic Web' application, but it does use things like natural language processing and other semantic analysis. It can output RDF, but that isn't a fundamental part of Cogito.

Brooke Aker described the system to us as being like an "electronic dictionary". There are 350,000 words and 2.8 million "relationships" in Cogito. Cognition claimed 10 million "semantic connections" in its map back in September, but Aker suggested that it wasn't quite an apples and oranges comparison. According to Aker, Cogito's semantic network is "richer" than Cognition's.

Expert System's new semantic advertising solution, Cogito Semantic Advertiser (CSA), came about because the company saw that traditional contextual keyword advertising is resulting in a lot of inaccuracies and mistakes when matching ads to page content. The classic example is the jaguar one, where a story about a jaguar (the animal) is accompanied by a 'contextual' advert for jaguar the car. Expert System told us that this kind of scenario won't occur with their semantic advertising system.

Expert System claims to have come up with an advertising solution that understands "the real meaning" of words, based on theories of human comprehension. For example, their system analyzes the semantic meaning of words and their context. So in the jaguar example, Expert System would 'understand' that jaguar is an animal in the context of the story - and therefore it would not serve up ads about the jaguar car.

One feature of Cogito Semantic Advertiser that stood out for us was the granular categorization, which allows for very fine grained targeting of ads. Brooke Aker told ReadWriteWeb that their product has already created around 2 million niches for advertisers to target, which is something that many Long Tail publishers will find appealing.

We're impressed by the semantic software that Expert System is producing, not just with advertising but in other commercial and government applications. Let us know in the comments if you've come across this company before and if so, what your thoughts are.

Specifically, Calais 4.0 goes beyond metatagging and enables publishers to integrate their content with Linked Data assets from Wikipedia, GeoNames, the Internet Movie Database (IMDB), Shopping.com and others. Calais 4.0 also lets publishers share semantic metadata about their content with "content consumers" such as search engines, news aggregators, related stories recommendation services and more.

ReadWriteWeb named Calais as one of our top 10 Semantic Web Apps of 2008, due to the progress it made last year. Since launching the Open Calais API early in 2008, over 9,000 developers have registered with it and Calais has processed 200+ million articles.

What's New in 4.0

We spoke with Thomas Tague, Calais lead at Thomson Reuters, about what specifically is new with Calais 4.0 and what use cases we might see over the coming year for it.

Tague explained to ReadWriteWeb that there are 3 pillers to the Calais initiative:

1. Getting semantic data out of text; which is what the first 3 versions of Calais focused on.
2. Connecting that semantic data to the linked data world.
3. Providing some way for people to share metadata, for example syndicating it - which Tague termed the "transport" piller.

Calais 4.0, explained Tague, fills in the final 2 of those pillers. It supports approximately 25 entity types in Linked Data - URIs are de-referencable to Calais RDF pages. Thomson Reuters is also publishing their ontology in RDFS. Calais will contribute data too, which Thomson Reuters claims is "the first contribution to the Linked Data cloud made by a major publisher." The data that Thomson Reuters is giving to the Linked Data world includes company descriptions, stock tickers, management teams and more. This data will be available to external developers to programmatically use in their apps.

Thomas Tague told ReadWriteWeb that Thomson Reuters has some big data assets and that over time "we're going to populate linked data endpoints with Thomson Reuters data". We asked Tague whether he thinks Calais 4.0 is the biggest commercial use of the Linked Data standard yet? He thinks it is; in his opinion, Linked Data has mostly been used so far for open data projects and relatively small sets of data. Tague said that "we fundamentally believe that companies need to jump into this [Linked Data]".

The Linking Open Data dataset cloud; by Richard Cyganiak

In terms of piller 3, the metadata transportation, Tague explained to us that a document gets a unique identifier - and to syndicate content, publishers just need to make available that unique identifier to external parties.

Conclusion

It will be interesting to see what companies make use of Calais over 2009. Last year we noted that IBM was using Calais - and we presume that with the extra Linked Data and transport functionality, other big companies will want to make use of Calais data too. Thomas Tague told us that they hope to announce 2 big product partners soon. He also said that they're seeing major traction around Drupal. Healthcare IT News from MedTech Publishing, a site developed in Drupal, features the full Calais suite for publishers including "More Like This", their related content plugin.

As we noted at the beginning of this post, we've been impressed with the progress Calais has made since its launch at the start of 2008. With 4.0, we expect to see it gain more traction among commercial publishers in 2009. Indeed as a (we like to think) ahead-of-the-curve 'new media' company ourselves, we're about to embark on our own project using Calais! Stay tuned for more information on that.

The Noesis search engine (PDF) is different than regular search engines because it employs the use of semantics to help its users better shape their search queries. The results of this lead to better, more accurate, and more complete sets of search results. Those results can then be refined even further by Noesis' end users if necessary.

The goal of the Noesis project is to provide scientists working in the field of Atmospheric Science a way to better search through the "hidden web" of scientific catalogs that traditional search engines cannot reach. Because these catalogs are built using a standard vocabulary, the most efficient searches on the catalogs involve using specific terminology.

To create Noesis, researchers simply annotated those specific vocabulary terms with ontologies - the machine-readable definitions for the words that help computers understand the concept of the term and its relationship to other terms. Of course, annotations alone do not make a semantic web search engine. The ontologies must be coupled with a tool that's capable of searching through them. To that end, Noesis employs something they call the Ontology Interface Service (OIS), a SOAP-based web service interface to an inference engine. When a user performs a search, the OIS is also immediately searched for associated concepts. The Specializations and Generalizations discovered are returned in a tree structure which the user can navigate further. Synonyms and related terms are also shown, and, using checkboxes, they can be appended to the original query to refine it further.

Although the project was designed for use in one select area of science, its framework could easily be replicated in other scientific fields of study.

The Semantic Web: Better in Niches?

The main problem with the semantic web today is that the assignment of those above-mentioned ontologies - the pieces of code that allow machines to grasp meanings that humans innately understand - is that there's no solid way to automate their assignment. At the present time, no automatic or semi-automatic processes to do so have been achieved...at least, not to the point that a true vision of a new, intelligent web can be realized.

Most of the time, annotating web resources must be done using manually inserted bits of code placed into various web pages. Obviously, that's a challenge when you consider the size of the internet - it would be impossible to manually annotate this ever-growing resource. Unfortunately, without automated methodologies, a true semantic web will remain an unrealized dream.

However, in smaller communities, the semantic web can easily become a reality. Scientific data catalogs only represent small portions of the web as whole. Because of their limited size, manually annotating the resources they contain is a manageable feat. This is the case with Noesis. It shows there is promise for the semantic web after all - if only in small niches.

Image credit: rule100

We at ReadWriteWeb are tracking the Semantic Web space closely - so far we've identified 20 products (see our first 10, then 10 more) that we're paying particular close attention to. But we know there is a lot of opportunity yet for commercializing the Semantic Web, so we encourage you to add your wish list in the comments.

1. Microsoft makes a very bold play with Powerset technology and starts to challenge Google in search (despite Google's attempts to use semantic web technology, we'd love Microsoft to ramp it up in search - competition is good for consumers!).
2. Semantic Web advertising apps for publishers - we have our eye on Dapper MashupAds in this sector, but we'd like to see others take up this challenge too.
3. Semantic apps for managing your finances - makes connections between transactions, things that you wouldn't normally pick up.
4. Semantic apps for health industry - there are many opportunities here, but in general there is much the Semantic Web could do to organize the maze of data in the health indsutry.
5. A Personalized Memetracker - Techmeme founder Gabe Rivera would be astonished if this happens, but we'd love to see a product that can give a Techmeme-like organization and layout to one's RSS feeds. So at a glance, you can see which stories in your own set of RSS feeds are hot and who's linking to them. Whether Semantic Web technology can achieve that, we don't know ;-)

Zoltán Andrejkovics, who suggested this topic, is a PhD student at Corvinus University of Budapest and his 5 wishes as a researcher are:

1. Smart notes; easy to find/browse notes, using NLP search.
2. Smart RSS; automatic article-collecting app based on my own interests.
3. Mind writing; using not only words, but "thought" objects, that the NLP engine puts into words.
4. Assistant; "my mirror", learns from my words, behavior on the net, and supports my work, handles calendar, etc.
5. Smart bookmarks; works like smart notes.

Here are some reactions from RWW readers on Twitter (it was very short notice before this post was published, so if we missed you please add your wish list to the comments):

superphoebe: "I'd love to see more semantic blogging tools like Zemanta, but with more sources, really great search and a super simple dashboard."

Marcelo Sánchez: "Zigtag for bringing real semantic tagging and Freebase as the next Wikipedia"

garlin: "I'd like an app that uses semantic tech to identify/analyze the political bias in a particular article/piece of writing."

kevin grandia: "would love to see a better way of submitting content to conversion services like Calais."

Rama Mamuaya: "language based search engines like Hakia or Powerset should be rising fast. Should evolve from search engine, to answer engine."

Jean-Jacques Halans: "mobile safari reading microformats, for adding to calendar, contacts, lookup address on map"

Stephen Edgar: "More on the Semantic Wiki app's and API's such as http://tinyurl.com/27vnno"

Chris Saad: "my hope is to see APML import AND export from more apps ;)"

Tell us your Semantic Web App Wish List for 2009.

The first demonstration of the OpenKnowlege system is aimed to enhance the abilities of those seeking health-related information on the web. Instead of solely relying on a doctor to prescribe a course of treatment, people today tend to seek out medical information on their own using the web. Unfortunately, that data is often inaccurate and misleading. What OpenKnowledge intends to do is provide patients with structured information that has been checked for accuracy. To test this system, OpenKnowledge is working with Cancer Research UK on a project related to treatment methods.

2) Emergency Response

When there's an emergency situation, there is often a centralized point that disseminates critical information to people in need. But if that system itself breaks down, people are out of luck. OpenKnowledge aims to decentralize those systems so that a "backup" decentralized network of peers could be put into place. There, people could help each other out when the centralized system failed. This is currently being testing with emergency response authorities in Trentino, Italy.

3) Protemoics Research

Protemoics research (the study of the structure and function of proteins) can also benefit from the OpenKnowledge framework. In this area of science, many researchers worldwide rely on a small number of databases, creating a bottleneck of sorts which stresses the infrastructure of the databases themselves as well as those that maintain them. Researchers also find it hard to share data and results directly with other groups. In addition, the quality of the information in those databases is very mixed.

OpenKnowledge aims to solve all three problems by letting the researchers share data with each other directly, peer-to-peer style. This relieves the burden on the databases while the feedback will continually improve the quality of the data shared. This is currently being tested in an existing proteomics network in Spain called ProteoRed.

So...What Is It Exactly?

Understanding how a system like this works is difficult and the Open Knowledge web site doesn't make the process of comprehension any easier. Even despite the cute, Harry Potter-themed slideshow meant to describe the process, the actual details are hard to grasp. Obviously written by brainy researchers, they can't even call the slideshow a "slideshow," instead referring to it as a "simple pictorial introduction."

Still, if you can wade through the academic speech on the site, what you may find is a creative idea for sharing information. Basically, through open source downloadable code, OpenKnowledge sets up a peer-to-peer network where users can trade in information and data similar to how BitTorrent users trade mp3s and video files.

In the OpenKnowledge system, anyone can easily become a peer or even create their own peer by sharing existing code or writing their own. In order to become an OpenKnowledge user, you simply need to download the OpenKnowledge kernel from here together with some additional components that you might want to use. In addition to users, services, such as WSDL services, can also be made into peers on the OpenKnowledge network.

OpenKnowledge is more of a framework for decentralizing the systems on the web. It's not so much of a consumer-friendly web app than it is a model for information sharing that can help advance areas of science and research. You may not ever use OpenKnowledge yourself on your home computer, but your life may very well be impacted one day by the innovations it made possible.

For those of you who don't know, Zigtag is another entry in the social bookmarking collection of tools. Like delicious, Diigo, and Ma.gnolia, Zigtag helps you categorize your bookmarks and share them with others. When Zigtag went into development, bookmarking was all the rage. The company's goal was to make bookmarking easier by adding a layer of semantics to the tags themselves.

Zigtag, you see, understands the meaning of the words you assign to a tag. When you tag to a page, Zigtag actually assigns it meaning rather than just a simple word. If that sounds revolutionary...well, that's because it is. Sort of.

Not the Only Semantic Tagging Service

Because of Zigtag's slow progress, they can no longer claim to be the only semantic tagging application available today. Another, Faviki, also offers an intelligent tagging service based on structured data. Both services attempt to address the problem of user-generated tags. That is, even though what you tag "NY" may be the same link that I tagged "New York," no bookmarking service ever knew the tags were related.

Zigtag and Faviki attack this problem in different ways. Faviki suggests tags for you to use, not from a community of users and their tagging history, but from structured information extracted from DBpedia, a community-maintained database created by extracting information from Wikipedia.

Zigtag, however, eschews suggestions and lets you tag items as you wish. It doesn't matter what personal system you use for tagging (one word, two words, underscores, plus signs, etc.) because Zigtag understands the meaning of the tags. In Zigtag, a link tagged "New York" is returned along with other links tagged "New_York." Zigtag also understands that one tag may have different meanings and groups those items accordingly. For example, there's a New York and Company clothing store and a New York in England that may have been tagged "new york." That level of understanding is something that's unique to Zigtag and sets it apart from other bookmarking services.

Thanks to the service's ability to understand meaning, Zigtag users can join groups related a shared interest. Since Zigtag knows what you mean by your tags, it is, in theory, easier to find links you would be interested in on Zigtag than with other bookmarking services.

Is This Really Web 3.0?

Zigtag may be one of the first tools to step out of the Web 2.0 box. Where "Web 2.0" implies there is a social element to a service, it's generally speculated that Web 3.0 will bring about the intelligent web. Zigtag delivers this intelligence, but is it enough?

The only downside to Zigtag is that it requires you, the user, to manually insert the tags. In fact, it even relies on user-generated tagging and has built its entire service around that concept. That may be where Zigtag went wrong. Although two years ago, what it offered was ground-breaking and unique, as we enter 2009, we're asking the question: "Is tagging dead?"

At first, collaborative tagging, also known as a folksonomy, appeared to be the future of the web. It was a rejection of the search engine in favor of the community. It was our collective intelligence harnessed for the purpose of applying meaning and order to the pieces of the web in ways that computer-based tools could not.

As time went on, though, the one thing that made a folksonomy appealing - it was made by people! - was also the very thing that gave it problems. User-generated tags were likely to produce unreliable results. Zigtag addresses that problem, but it does not address what may end up being the true source of failure for folksonomy-based systems: people are lazy.

Now that there are myriads of services using tagging, thanks to the explosion of Web 2.0, we're getting sick of all the manual labor involved. Tag your links, tag your photos, tag your blog entries, tag your RSS feeds, etc.

While at one time, a semantic-based tagging system like Zigtag may have seemed like a vision of Web 3.0, we've now come to a point where we wonder if it does enough. It's possible the next revolution of the web won't be a system that understands the meaning of the tags we created, but knows how we would have tagged things if we had bothered to do so and then does it for us. And if that's not the future of the web...well...perhaps it should be.

We're impressed by the offering and excited to see what will come of it.

Idiomag has been pulling in user attention data since early this year. The idea is that users can enter their username from sites like Last.fm, Pandora, iLike, Strands or imeem and Idiomag grabs their public listening profile from those sites. Those past interests are used to recommend playlists of music and videos that a user would probably like. Idiomag then brings in semantically indexed articles from syndication partners about those artists, and photos from around the web from live concerts, etc. The coolest part is that the pages of these magazines are created dynamically so that the background color behind the text, for example, works well with whatever the images are on the page.

Content is changed day by day according to which artists the user has liked or disliked. It's all pretty fascinating.

Now all of that content is available to be placed on 3rd party websites through API calls. Music social networks MOG and TheFilter are already using the API to offer up-to-date blog posts about artists on their profile pages.

Data is available in xml, json, rss, apml, xspf and foaf.

What's Coming Next

Idiomag says it's taking its semantic content discovery and social recommendation technologies into new verticals and onto new platforms next. Mobile and HTML interfaces will extend beyond the current Flash magazine format. Film and gaming content pipelines are already built and will soon be integrated along with music content. Gossip content is next. (Who does web content but leaves out gossip? Only people uninterested in monetization or overly concerned about the dignity of the human spirit.)

Idiomag has been around for several years but just two months ago built out a new semantic indexing technology that parses the full text of articles to determine their relevance. The company says it's seeing 80% accuracy in non-music verticals and will reach parity soon.

The advertising on Idiomag is personalized along with the content and the company says it's seeing 8 to 10% click through rates. That's covering 40% of their very slow burn rate (they also have some seed funding) and they hope to soon start cutting some B2B deals.

The Mashups to Come

What will we see built with this new API? We're excited to find out. We expect to see 3rd party sites in all kinds of verticals, but first in music, pulling in bundles of personalized multimedia content that they will then add further value to on their sites. It's exciting to think about. Maybe you, dear reader, will be so inspired. Let us know what you build!

The emphasis of the application is on its ability to search the full text of your bookmarked sites, even if those were originally hidden behind paywalls or if you had to log in to the site. This is great if you want to save bookmarks for research, or even if you just want to keep a copy of a social network profile.

Features

In Qitera, you can organize your bookmarks around 'topics.' These topics can also be shared with your contacts on Qitera, though it doesn't seem like Qitera allows for collaborative bookmarking.

You can also see an Iterasi-like live copy of your bookmarked site, though Qitera sadly doesn't recognize when you save the same page twice.

Among Qitera's other interesting features are a Cover Flow-like view of your screenshots, the ability to rate other users' shared items, and a news feed with updates from all your contacts.

Qitera works with every browser. Firefox users can install a small extension, while IE, Safari, or Chrome users get a bookmarklet with the same functionality.

What's Missing?

One feature we are missing from Qitera is the ability to share a certain topic directly on the web. While Qitera is currently a great private research tool, being able to share your topics publically would be also be very useful.

It would also be nice if you could share items not just with other Qitera users, but also with your friends on FriendFeed, Twitter, or Facebook. While Qitera allows you to bookmark and search sites hidden behind paywalls, the site itself does feel a bit like a walled garden (which, for a lot of users, may not even be a bad thing).

Verdict

At first, we were skeptical about yet another social bookmarking tool, but after testing it for a while, we can see how Qitera can be a highly useful research tool - especially thanks to its ability to bookmark and search pages from the 'deep web.' Instead of just having to rely on tags, you can now create small, custom search engines for all of your bookmarks.

This is the first in a series of posts we'll publish over December, listing our choices for the top web products of the year. Then at the end of December, we'll post a Top 100 list - which we'll be promoting over 2009 and opening up at some point for public voting. Without further ado, let's jump into the top 10 Semantic Web products of 2008.

You may disagree with our selections, so do tell us in the comments what you think.

Note: the products listed below are in no particular order

Yahoo! SearchMonkey

In May this year Yahoo! launched an open developer platform for search called SearchMonkey. Yahoo hasn't had the happiest of years, but its willingness to innovate in search is to be commended. As we reported at the Web 2.0 Expo in April, SearchMonkey is a component of a major overhaul at Yahoo! across all of its properties to "rewire" for the social graph and data portability. SearchMonkey allows developers to build applications on top of Yahoo! search, including allowing site owners to share structured data with Yahoo!, using semantic markup (microformats, RDF), standardized XML feeds, APIs (OpenSearch or other web services), and page extraction.

We think this is the best use of Semantic Web by an Internet bigco this year. So for that reason SearchMonkey makes our top 10 list. Related: The Story of SearchMonkey.

Powerset (acquired by Microsoft in '08)

Powerset (see our initial coverage here and here) is a natural language search engine. It's fair to say that Powerset has had a great 2008, having been acquired by Microsoft in July this year.

At the time of the acquisition, Powerset said that it needed a bigger partner to expand its product beyond its current state of only searching Wikipedia - something we had speculated about when the rumors of the acquisition first appeared. In its own statement, Microsoft stressed how useful Powerset's technology will be for improving Microsoft's own search products and to "take Search to the next level." In our analysis of the deal, we noted that it was a "bold play requiring exact execution" by Microsoft.

Open Calais (Thomson Reuters)

At the end of 2007, ClearForest had been recently acquired by Reuters and at that point it had a Web Service and a Firefox extension. What a change a year brings! ClearForest went on to release Calais, a toolkit of products that enable users to incorporate semantic functionality within their blog, content management system, website or application.

Since launching the Open Calais API early this year, over 6,000 developers have registered with it and the service is doing more than 1 million transactions a day. Version 3.0 was released earlier this month and version 4 is expected by January 09.

Dapper MashupAds

In November we wrote about the recent improvement in Dapper MashupAds, a product we first spotted over a year ago. The idea is that publishers can tell Dapper: this is the place on my web page where the title of a movie will appear, now serve up a banner ad that's related to whatever movie this page happens to be about. That could be movies, books, travel destinations - anything. We remarked that the UI for this has grown much more sophisticated in the past year.

The company believes that its new ad network will provide monetary incentive for publishers to have their websites marked up semantically. We think this has plenty of promise, so it makes our year-end list.

Hakia

Hakia is a search engine focusing on natural language processing methods to try and deliver 'meaningful' search results. Hakia attempts to analyze the concept of a search query, in particular by doing sentence analysis. Over the past year Hakia has been busy extending its reach - licensing its proprietary OntoSem technology to other companies in March and announcing a Semantic API in June. It was also one of the first companies to utilize Yahoo! BOSS, by integrating their semantic parsing with the Yahoo! search index.

We think Hakia has made good progress getting its technology into the hands of third parties and making use of Yahoo's broader index, so for that reason it's among our top 10 for the year.

TripIt

Tripit is an app that manages your travel planning. With TripIt, you forward incoming bookings to plans@tripit.com and the system manages the rest.

Over the past year TripIt has continued to iterate on its feature set - introducing LinkedIn integration, better mobile functionality, more social networking features, and other goodies. In short, it's user experience continues to rock!

BooRah

BooRah is a restaurant review site that we first reviewed earlier this year and has come on in leaps and bounds over 2008. BooRah uses semantic analysis and natural language processing to aggregate reviews from food blogs. Because of this, BooRah can recognize praise and criticism in these reviews and then rates restaurants accordingly. BooRah also gathers reviews from Citysearch, Tripadvisor and other large review sites.

BooRah also announced last month the availability of an API that will allow other web sites and businesses to offer online reviews and ratings from BooRah to their customers. The API will surface most of BooRah's data about a given restaurant, including ratings, menus, discounts, and coupons.

BlueOrganizer (AdaptiveBlue)

Disclosure: AdaptiveBlue's founder Alex Iskold is a feature writer at RWW.

AdaptiveBlue are makers of the Firefox plugin, BlueOrganizer. As we wrote in January this year, the basic idea behind BlueOrganizer is that it gives you added information about webpages you visit and offers useful links based on the subject matter.

Over the past year the company has been working on a new product, called Glue. Launched last month, Glue is a more social networking oriented version of BlueOrganizer - it connects you to your friends based around things like books, music, movies, stars, artists, stocks, wine, restaurants, and more. We think the company has diversified smartly in 2008, by integrating social networking and mobile functionality into its products.

Zemanta

Zemanta is a blogging tool which harnesses semantic technology to add relevant content to your posts. While it didn't make either of our 'Semantic Apps to Watch' lists in November, a number of commenters pointed it out as something they use. In September we covered a major upgrade to Zemanta's service, allowing users to specify the sources they want to see in the suggestions list that Zemanta provides. Users can now incorporate their own social networks, RSS feeds, and photos into their blog posts. As we noted, this makes Zemanta a lot more appealing to established bloggers who are in less need of suggestions and more in need of automation.

Zemanta's API is also being used by startups, including semantic bookmarking service Faviki - which we mentioned in our second Watch-list. So all up, we think Zemanta has done enough this year to be included in our top 10 list.

UpTake

Semantic search startup UpTake (formerly Kango) aims to make the process of booking travel online easier. In our review in May, we explained that UpTake is a vertical search engine that has assembled what it says is the largest database of US hotels and activities - over 400,000 of them - from more than 1,000 different travel sites. Using a top-down approach, UpTake looks at its database of over 20 million reviews, opinions, and descriptions of hotels and activities in the US and semantically extracts information about those destinations.

And now please let us know in the comments what you think of our selections. Do you think we've picked the best 10 Semantic Web products of the year?