What Does "Semantic Web" Mean?

The 2009 Semantic Technology Conference kicked off yesterday. Between now and Thursday, there'll be a total geek-out covering all that's coming in Web 3.0 (whatever that actually means!).
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Of special interest to me is the semantic search day. It includes a fabulous keynote panel featuring some of the top figures in semantic search research. I'm particularly looking forward to hearing Andrew Tomkins of Yahoo and Peter Norvig of Google. I've been following their work for a long time, and they are top-class thinkers. Also on the panel will be Scott Prevost of the Powerset division of Microsoft's Bing, Tomasz Imielinski of Ask, Riza Berkan of Hakia, and William Tunstall-Pedoe of True Knowledge.

I spend a lot of time researching information retrieval on the Web and how it may affect search marketing's future. A frequent question I get on my travels is: "What is the semantic Web?"

Mostly, people seem to think there's another Web being built somewhere, getting ready for launch. That's not the case. It's more about bringing meaning to the Web we already have. Now you're scratching your head and thinking, "I know what Web pages mean." And you're probably right. Point is, computers don't. Computers can figure out syntax, but that doesn't say they understand meaning.

There's a lot of research going on around sentiment analysis, as in, "I know people are talking about my brand, but are they saying nice things?" Let's face it, we all know there are two ways you can think about the word "bad." Sometimes it means good. And, not so mysteriously, sometimes it can mean not very good at all.

You can take that to another level. In New York, many tourists buy "I Love New York" T-shirts. However, the word "love" is replaced by a heart. You and I know what that means. But how would a computer?

The Web as we now know it was conceived more as a library in which anyone and everyone is able to contribute. It's built on the notion of being able to gather the world's knowledge and have it in one place. But there are no real rules.

And when it comes to search, the Web is essentially syntactic. This is why information retrieval on the Web is such a fascinating science. If you know the name of something, it's typically not too difficult to find it on the Web using a search engine. But when you don't know the name of something and have to use a description, search usually falls over.

Tell any New Yorker you had a sandwich made with rye bread, corned beef, Swiss cheese, sauerkraut, and Russian dressing but can't remember what it's called. He'll tell you it's a Reuben. But just throwing a few ingredients into a search engine may not get you such a quick or even correct response.

This is where the semantic Web comes in. It's more about Web services, where machines can work together to perform inferences in the way people do. The idea behind the semantic Web is to try to turn information on the Web into something with a much more clearly defined meaning.

At this time, maybe the earliest attempts involve using XML to embed structured data into a document alongside unstructured text. And here's where I opt out of too much talk about the underlying technology for fear of you falling asleep.

Let's go back to meaning. In most languages, syntax is how you say something. Semantics is about the meaning of what you said. So, even though the Web as we know it was developed to allow all computers to talk to each other, these computers don't actually know the meaning of what they're talking about.

The semantic Web isn't about artificial intelligence, with computers learning how to understand human language. We're talking about a concept whereby computers will have enough semantics to allow them to solve well-defined problems through the sequential processing of operations.

It may be that a software agent doesn't even come close to the conclusions that a human is capable of. But it may contribute to building a better Web than the one we have today.

And all I can say after that is: I hope you get my meaning!

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BY Mike Grehan
Source：ClickZ

Mike Grehan is global KDM officer with New York based Acronym Media. He has been involved in online marketing since 1995 and is recognized in the industry as an expert in the search marketing field. He has written multiple books and white papers on the subject. His second edition of "Search Engine Marketing: The Essential Best Practice Guide" gained plaudits from the industry's leading figures. His newsletter has attracted over 17,000 online marketers. Mike is a sought-after speaker for the world's major online marketing conferences.

Incisive Interactive Marketing LLC. 2009 All rights reserved.

Microsoft Bing rides open source to semantic search

As it turns out, Powerset's open-source-happy semantic talents are only a small part of Bing, Microsoft's freshly-minted decision engine search engine.
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Microsoft acquired Powerset last July in a reported $100m deal, and after a conspicuous Tweet from Powerset co-founder Barney Pell, many assumed that the semantic search outfit would play a major role in Redmond's latest attempt to catch the uncatchable Google.

According to a blog post from Scott Prevost, general manager of Microsoft's Powerset division, the division has tweaked Microsoft's primary search engine in certain "subtle" ways. But its main contribution is a secondary engine that searches nothing but Wikipedia. In essence, Microsoft's has taken Powerset's existing Wikitool and latched it to the Bing torso.

"The Powerset division has contributed to Bing in both subtle and more conspicuous ways. While the subtle contributions are important, they are much harder to showcase. This post will focus on how the features that our users have come to love on Powerset.com have evolved and have been integrated into Bing," Prevost says, before detailing Bing's "Reference" tab.

As we reported yesterday, the Reference tab reproduces Wikipedia articles in their entirety. When you search on, say, Albert Einstein, the tab will appear on the left hand side of the page, and if you click on it, you're taken to a reproduction of Einstein's Wikipedia entry (licensed at no cost from the "free encyclopedia anyone can edit").

Yes, Microsoft has solidified Wikipedia's place as the web's number-one source of truthiness.

But from that Reference tab you can also tap into Powerset's semantic Wikisearch, which the company originally unfurled in May of last year, before the Microsoft acquisition. This vertical search engine is designed to accept natural-language queries, such as "Was Einstein married?" - though that's not immediately obvious from Bing's layout. In a video attached to Prevost's blog post, Powerset founder Lorenzo Thione acknowledge that some of Bing's Powerset tools are "a little bit hidden. Over time, we'll definitely work on making it more accessible and visible to users."

In the same video, Senior Program Manager Mark Johnson says that in a few cases, Microsoft has hooked Powerset's natural-language platform into some of Bing's other search verticals, including the "Business" tab. But Thione calls these "pilot tests."

"There are a subset of queries where you use a more natural-language oriented syntax or you ask questions, similar to what Powerset.com used to support, we will get you answers right there on the page and a link back to the Reference vertical," he says.

Despite its limited role in the new search engine, Powerset's Bingification is a Microsoft milestone. Powerset's platform leans heavily on open-source code. Most notably, its search index is generated via Hadoop, the same open-source distributed computing platform that juices Yahoo!'s search engine. Powerset originated Hadoop's HBase project, an effort to mimic Google's famous distributed storage system, BigTable, and two of its employees, Michael Stack and Jim Kellerman, are full-time HBase committers.

According to Sam Ramji, the director of Microsoft's open-source development lab, "This is the first time we have acquired a company with committers to a key open source project who have been able to continue to commit to that project in their old capacity as part of their new role."

And thanks to its integration with Powerset's platform, Bing is one of the few Microsoft "shipping" products to actually incorporate open-source code. Ramji points out that from the early to late 90s, Microsoft's Windows TCP/IP stack included BSD code, and today Windows HPC includes code developed at Microsoft that was then offered up to Argonne National Lab (ANL) for open-sourcing. But since the arrival of Windows Vista, Bing is certainly the most high-profile Microsoft product to go the open-source route.

Ramji calls it part of Microsoft's "strategic shift and cultural change" towards the open-source world. And it's certainly nice to see. But on another level, it's rather amusing that the company that once called Linux a cancer and spent untold millions on Encarta is now resting its search-engine on Hadoop and Wikipedia.

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BY Cade Metz in San Francisco
Source：The Register

© Copyright 1998–2009.

Sematic technology pulls a gun on the blogosphere

Since Internet terminology has the lifespan of a housefly, I figured it wouldn't take long to see what form the term "Web 3.0" would take and what it would define. Here's a clue: Soylent Green is people.
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According to Paul Sweeney, Founder and CTO of semantic technology firm Primal Fusion, the future of the Web won't rely on pesky things like social media, citizen journalism, or professional reporting. Instead expect to see Web content generated by semantic technologies that will render actual writers obsolete. Uh-oh.

"Today, most [Web] content is still created by hand, the best of it by highly skilled artisans drawing on centuries of scholarship and experience. Recently, we've seen significant innovations in social approaches to content creation. But Web 3.0 industrialization takes content manufacturing to an entirely different level. Instead of users manually creating content, machines automate the heavy lifting," says Sweeny.

"Billions are being spent worldwide on semantic technologies to create the factories and specialized machinery for manufacturing content, ...and the most productive information services in the world are those that leverage Web 3.0 industrial processes and technologies," he says.

Sweeney goes on to point out that some of our Internet experiences already have an automated undercurrent. Google News, for example, spits out stories that computers -- not humans -- have ranked as Best of Breed. The new search engine Wolfram Alpha does our thinking for us by relying on "knowledge computation" to provide singular results it thinks we should have, not an assortment of results from which we can choose.

Now, I get that one of Sweeney's goals is to promote his company and the technology behind it. While that might account for some of his (hopefully overstated) suggestions, it doesn't eliminate the possibility that his theory has some potential. Media outlets are outsourcing content left and right, or just killing off some of it completely. Is it such a stretch to think that if publishers can find a way to automate the content-production process completely, they just might do it?

Of course, I'm being tounge-in-cheek when I suggest this vision of Web 3.0 is likely to come to fruition, but tell me there isn't just a tiny corner of your mind wondering if the concept has any potential at all? After all, it wasn't all that long ago that your local news was brought to you by on-the-scene reporters. Nowadays? Not so much.

What do you think Web 3.0 will look like? Will semantic technology take today's wordsmiths and turn them into soylent green wafers? More importantly, will you remember the cadre of Computerworld bloggers fondly when we've all been assimilated?

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BY Lisa Hoover
Source：COMPUTERWORLD

Copyright © 1994 - 2009 Computerworld Inc. All rights reserved.

Ted Slater’s Semantic Technologies

The semantic web doesn’t exist, but Pfizer’s Slater believes semantic technologies are paving the way.
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The theoretical benefits of the semantic web for life sciences have been debated for a few years now (see, “Masters of the Semantic Web, Bio•IT World, October 2005), but practical examples within pharma remain scarce. Pfizer’s Ted Slater is an interesting exception. Slater heads a small group of four informatics scientists in St. Louis called the Indications and Pathways Center of Emphasis (IPCoE), which supports Pfizer research efforts in identifying and validating inflammation targets.

Slater trained as a molecular biologist, but made the mistake of buying his first computer shortly after starting his Ph.D. at UC Riverside. “72 straight hours later, I realized that I’d made a vocational error and maybe I wanted to learn more about computer science instead.” He took a Master’s in computer science, and went on to work for a string of genomics companies in the ’90s, including Sequana Therapeutics, GCG, and Paradigm Genetics. He was the founding vice president for knowledge engineering at Genstruct, joining Pfizer in 2004.

Slater tends not to use the term “semantic web”. “There just isn’t a semantic web,” he says candidly. “It doesn’t mean there won’t be one in the future, but there isn’t one now.” He prefers “semantic technologies,” so as not to provoke critics who would otherwise argue that he should move on to something else.

At CHI’s Bridging Pharma & IT conference last October*, Slater outlined a project focused on pathway data analysis that showed how structuring data in a semantic network could provide substantial benefits over traditional pathways solutions, such as automated hypothesis generation. The effort has gone from idea to reality over the past 6 months.

Using semantic technologies in this way helps to eliminate long-standing problems in informatics like data silos, where information is not interoperable with other necessary information, and data tombs, which simply make information very difficult to retrieve. Semantic technologies shift the focus from collecting information and making it safe to actually using the information in its proper context to solve research problems. “The computer should be a way of enhancing our own natural ability to reason, in the same way a bicycle enhances our ability to move ourselves around,” says Slater.

Be Reasonable
Ideally, Slater says you would like users to be able to reason with the data in silico: using “if-then rules,” let the computer generate a hypothesis, then let the scientist decide the potential implications and leverage knowledge to test whether they are supported with data.

“We constantly hear that the Holy Grail is complete data integration,” says Slater. “I have bad news—it will never happen! Users are able to set up and start building new, independent repositories of data faster than we can integrate existing data. You will never be able to get it all in one place where it is integrated and useable. The goal instead should be data that are interoperable, even if they are not integrated.”

Slater’s group helps scientists to study gene expression and signaling pathways in order to identify alternative indications for drugs in development. “There is no easy way to understand what is going on if you look at a list of 1,000 genes that are significantly up- or down-regulated,” he says. Even if those genes are mapped onto pathways using a commercial pathways tool, one is forced to work with what amounts to a reference tool. Much information is available on individual genes, but you have to try to tell a story about physiology by painstakingly going through the annotation for each gene one at a time.

An alternative approach lets the computer generate hypotheses based on available data, which would distill the range of possibilities to a few key relationships. If the data are represented correctly, you can use data from disparate databases, such that users can create ‘boutique’ knowledge bases for their own needs and easily link them together.

Adapting the familiar “triple” semantic RDF format—representing information as a subject, predicate, and object—Slater represents the data as a mathematical graph, with subject and object as nodes and the predicate (the relationship between them) as an edge. One triple’s subject can be another triple’s object, and so on, until a very large graph of everything known in some domain is created. In this format, the information can be handled with software to build inferences and test hypotheses. His group uses open source ontology development tools to build OWL ontologies and another open-source tool, Cytoscape, to view the data in graph format. For persistent storage, knowledge graphs can be managed in Oracle’s built-in RDF data model.

PEKE Performance
One of the goals in data analysis, says Slater, is to use heuristics over the knowledge bases to tell a story. Semantic representations of knowledge allow you to apply expert reasoning to experimental data, which may help explain a particular outcome and in turn suggest a testable hypothesis. “You don’t get inferences in a traditional structured database,” says Slater. “We have our share of traditional databases, and we are getting better at data warehousing. For many scientific applications, representing the data as an RDF graph and building for interoperability make the information much more usable. If the description of your problem solution ends with, ‘and then the user can query it,’ then you haven’t thought it through enough.” How you structure the information can either lock up the information in a data tomb or set it free.

The experimental system that Slater and his group have developed is called the “Pfizer Environment for Knowledge Engineering”, or PEKE. “PEKE handles all of the usual storage and querying capacities of traditional databases, but because of its architecture it has some surprising emergent properties,” says Slater. Among these are the ability to create, with just a couple of mouse clicks, new knowledge bases that essentially automatically interoperate with other PEKE knowledge bases.

Another capability of PEKE is that, because the semantics of each knowledge base are explicit in its OWL ontology rather than implicit in a relational database schema, PEKE supports knowledge bases containing any kind of knowledge with no changes to the architecture. While most PEKE knowledge bases are currently about molecular pathways, the ontology Slater uses to demonstrate how easy it is to create PEKE knowledge bases is the OWL pizza ontology from Stanford’s Protégé Team.

Slater says, “PEKE is world-class stuff. We think we can now build knowledge bases faster and cheaper than anyone else in the industry, and do much more with them once they’re built.” We may still be waiting on the semantic web, but semantic technologies are already paving the way for the next wave of informatics innovation.

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BY Kevin Davies and Phillips Kuhl
Source：Bio-IT World.com

©2002-2009 Cambridge Bio Collabrative Inc.

Google 3.0: Semantic search

If you aren’t following changes in search technology, you could have almost missed today’s announcement from Google – which seems to be insignificant at first, but at a closer look a dramatic improvement that effectively rings in Google 3.0.
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The original Google was good enough to fend of rivals until 2007, when the company announced its first major improvement called Universal Search. Back then, the company combined its search silos - text, images, videos, shopping and others – but already hinted at a future feature called contextual search. Contextual search, commonly referred to as semantic search, links related topics to better predict what users may be looking for.

This contextual search is now live and includes algorithms that Google claims understand what other search terms may be useful to understand users, queries and content. At least in theory, search results should be broader and, in Google speak, more universal. There was no information how the company’s page ranking will be impacted by the additional search results that will now be provided.

While Google said that semantic search is now available in 37 language versions of Google, a previously described feature the company has been working on is not available – contextual search across different languages, or, as Google calls it, “cross-language information retrieval.” In 2007, Google said that this feature will translate search queries in twelve different languages and return search results from websites in those twelve languages to provide a more comprehensive search experience. Google did not say when this feature will become available.

There are, however, two other minor improvements – a better handling of more complex search queries as well as the display of longer snippets in search results.

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BY Wolfgang Gruener
Source：TG DAILY

© 2009 DD&M Inc.

Web site created for semantic cloud API

John Willis of the IT Management blog is reporting that Reuven Cohen has created a new web site in support of the development and promotion of a Universal Cloud Interface. The concept, as Reuven reported today, revolves around some of the good work being done to address cloud taxonomy and ontology:
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We are in a sense defining what cloud computing is by describing it's "components" and their relationships to one another. One that is capable of expressing cloud computing and its subsequent parts in terms of a consensus data model.

So in this effort we may actually be defining a dynamic computing model that can, under certain conditions, be 'trained' to appropriately 'learn' the meaning of related cloud & infrastructure resources based on an common ontology / taxonomy. In a sense, we are talking about the Semantic Web applied to API's or more broadly, a unified cloud interface.

The web site, created on the Google Code infrastructure, provides a central point for the definition and development of the UCI. Remember all that Simon Wardley has been saying about the need for open sourced standards? This is probably as good an example of a community supported effort as there is to date.

So far the membership is small, and consists of people who have been working closely with Reuven for the past few months on everything from CloudCamp to organizing the Cloud Computing Interoperability Forum. (Disclosure: My collegue, David Berenstein from Cisco, is among them.) However, I believe this list will grow steadily over the next few months, as people realize that there is something to what Reuven is saying; what we in fact may be looking for is an API that can describe other APIs. I, for one, want to see this concept explored in much more depth.

The only serious question I have is how the finished specification and sample code becomes an accepted standard, so to speak. Are we talking submission to an existing organization? Creation of a new one? (Please don't.) Acceptance without an official standards body (i.e. as open source)?

Truthfully, however, at this point those logistics don't really matter. What matters is that the cloud community is pushing for standard ontologies and taxonomies (very quickly, I might add), and there is a serious effort to translate all that into an API that will promote interoperability and choice in the cloud marketplace.

This may be the beginning of one of the most important standard APIs of the Intercloud. Interested? Join the community and get involved.

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BY James Urquhart
Source：cnet

James Urquhart is a seasoned field technologist with almost 20 years of experience in distributed systems development and deployment, focusing on service-oriented architectures, cloud computing, and virtualization. James is currently market manager for the Data Center 3.0 strategy at Cisco Systems. He is a member of the CNET Blog Network and is not an employee of CNET.

©2009 CBS Interactive Inc. All rights reserved.

Did Google Just Expose Semantic Data in Search Results?

In what appears to us to be a new addition to many Google search results pages, queries about birth dates, family connections and other information are now being responded to with explicitly semantic structured information. Who is Bill Clinton's wife? What's the capital city of Oregon? What is Britney Spears' mother's name? The answers to these and other factual questions are now displayed above natural search results in Google and the information is structured in the traditional subject-predicate-object format, or "tripples," of semantic web parlance.
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The answers aren't found structured that way on the web pages they come from - Google appears to be parsing the semantic structure from semi or unstructured data. That's something Microsoft paid over $100 million to try to do this summer when it acquired Powerset. Check out these screen shots below.



We're sure that Google's been doing this analysis for some time behind the scenes, but for the company to expose the data in this structured way and to include a link to view other sources appears new to everyone we've asked about it so far. We've got inquiries in with some people who specialize in search but our semantic web contacts say they've not seen it before. (Update:

Our first comment comes from someone who claims this has been the case for years already! We'll see how feedback goes.)

It appears that the feature isn't being bucket tested, either, it is globally available. Could 3rd parties make use of the data now that it's available in a structured format? Possibly. The search results pages aren't being marked up in HTML, which is a shame.

Yahoo, Ask.com and Live.com are all unable to answer these questions so clearly.

Many of the data points are being pulled in from the structured part of Wikipedia entries, which is interesting. Other sources are wide ranging, from a license plate website to Jason Calacanis's Mahalo.

We're not sure what to make of this - have readers seen it before? We think it's new and we think it's pretty interesting.

Why is This Important?

As we've said about the semantic web before: Once our software is capable of deriving meaning from web pages it looks at for us, there's a whole lot of work that will already be done, allowing our human, creative minds to reach new heights. Structured data is a layer of standardized abstraction upon which new innovation can be created.

That's why we're interested to see what Google is doing.

The answers aren't always accurate - try searching the birth date of Jesus Christ, for example. Yahoo! has far more clearly articulated what they intend to do with semantic data. None the less, Google now appears to be doing something that no one else is doing. Maybe readers here search for "Britney Spears' mother" all the time, though, and have already seen this. It's new to us, though.

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BY Marshall Kirkpatrick
Source：ReadWriteWeb

The Semantic Web is Coming

Wow! It’s amazing at how fast the World Wide Web and the ability for information to be exchanged and communicated between individuals, groups and the masses have evolved. It was only a little more than 15 years ago that the World Wide Web finally began to pick up steam and grab viewers and contributors. However, in such a short period of time billions of people have utilized the web and today it is considered by many their most important information and entertainment resource. So with the WWW finally a major resource in our life and with web 2.0 peaking, many are looking to the next big thing for the web and most in the industry simply call it Web 3.0. So what will Web 3.0 be defined as? While many of the aspects of Web 3.0 such as cloud computing and artificial intelligence are already available in a limited form, one of core technologies that Web 3.0 will involve is the semantic web.
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The Semantic Web

While you can find a whole host of technological definitions on what exactly is the semantic web, for most web surfers trying to grasp this concept, the semantic web can be explained in the following ways:

Currently, the vast majority of people search the web via text. For instance if you are looking to find information on a HD TV, you would simply head over to Google or your favorite search engine and enter text such as: “HDTV” or for more detail and better results, you might want to include: “Sony HDTV 52 inches”. The search engine would then use its algorithms and incredibly large capacity of databases of submitted links to return perhaps the most closely related searches to your text input. It should be noted with all this technology and powerful computers and database the search for HDTV is pretty rudimentary and it is due to semantics or the lack there of.

Unfortunately, all the computers and databases that help you conduct a search for HDTV don’t know what an HDTV is and are only looking for tags and other basic info to help you conduct your search. While your search results are usually decent, imagine how much better they could be if the computers, databases and servers involved in your search actually had the intelligence to understand what an HDTV is- your searches would be far more superior and the potential of the Web’s reach exponentially greater. This is what the semantic web is about- finally giving computers, servers, machines, etc enough artificial intelligence so that they understand what you are actually searching for and not just fetching data in simple tag form that fits in its extremely small parameters.

Media Finally Becomes Recognizable

When we search for images, music, video, etc we are using text to conduct search. This is why it is very difficult to search for an image using an image. For instance, if you have an image of a blue ball with distinct markings, you don’t usually copy and paste this image into your search engine to find others like it, you would first have to describe the image in text form and then search for it via conventional ways. With the semantic web, this can all change. Whether it is music, video, images, etc the web will understand what you are searching for and what others offer and whether they are a close enough match.

What makes the semantic web so difficult up until know is not only the artificial intelligence aspect of the web- having enough brain power to understand what an object is in order to conduct a thorough search, but also to understand the relationship between objects, ideas, etc. With the semantic web, there is a very strong need for data mining and computer languages that can easily communicate with one another on a higher level that is much more capable than today.

The Ability to Share Information and Media in New Ways

There is plenty of theorizing available as what the semantic web will be like once it finally arrives. And while many aspects of the semantic web and of web 3.0 are just theory, many expect big changes to help us share information not only easier, but more intuitively. Today, our media and data are stored on computers that are considerably much more convenient and wireless than just a decade ago, but there is so much more we can do to make the experience of accessing and sharing our media and data with others much more fulfilling and instant. Wifi and wireless communications will definitely help to make this idea a reality, whether in its current 3G, 4G, Bluetooth form or other technologies such as so called Wimax. Not only transferring data more quickly will be helpful, but the ease in which we locate our data that we have stored more intuitively and the ability to share it with others easier without technological barriers will be a byproduct of the semantic web.

The Semantic Web and Search Engine Optimization

For SEO’s looking to gain a better understanding of the semantic web in order to give their clients a jump on the upcoming technology, the best advice is to be patient. The semantic web is still in its infancy- probably at its conception, so in which direction the semantic web takes us is hard to guess.

However, if there is a strategy that one can utilize to take advantage of the up and coming semantic web it would be the strategy that is the simplest of them all- continue to create web pages that are relevant to the information and products that you sell. While there will no doubt be new and innovative computer languages and data mining techniques evolving, the fact of the matter is that you always want visitors coming to your site that are interested in the information and products that you offer. There is no use trying to offer information about apples, when a web surfer is looking for oranges and vice versa, so with the upcoming innovations in the web the most prudent strategy for right now is a wait and see attitude.

Obviously, as this technology matures, there should be plenty of ways to optimize content and make it easier for computers to read and understand what your websites offer- but right now it is still too early to predict the variety of directions that this amazing new technology will take us.

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BY Roger Janik
Source：PromotionWorld