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Triplifying a real dictionary

Semantic Web Company - Wed, 2016-11-16 07:07

The Linked Data Lexicography for High-End Language Technology (LDL4HELTA) project was started in cooperation between Semantic Web Company (SWC) and K Dictionaries. LDL4HELTA combines lexicography and Language Technology with semantic technologies and Linked (Open) Data mechanisms and technologies. One of the implementation steps of the project is to create a language graph from the dictionary data.

The input data, described further, is a Spanish dictionary core translated into multiple languages and available in XML format. This data should be triplified (which means to be converted to RDF – Resource Description Framework) for several purposes, including to enrich it with external resources. The triplified data needs to comply with Semantic Web principles.

To get from a dictionary’s XML format to its triples, I learned that you must have a model. One piece of the sketched model, representing two Spanish words which have senses that relate to each other, is presented in Figure 1.

Figure 1: Language model example (click to enlarge)

This sketched model first needs to be created by a linguist who understands both the language complexity and Semantic Web principles. The extensive model [1] was developed at the Ontology Engineering Group of the Universidad Politécnica de Madrid (UPM).

Language is very complex. With this we all agree! How complex it really is, is probably often underestimated, especially when you need to model all its details and triplify it.

So why is the task so complex?

To start with, the XML structure is complex in itself, as it contains nested structures. Each word constitutes an entry. One single entry can contain information about:

  • Pronunciation
  • Inflection
  • Range Of Application
  • Sense Indicator
  • Compositional Phrase
  • Translations
  • Translation Example
  • Alternative Scripting
  • Register
  • Geographical Usage
  • Sense Qualifier
  • Provenance
  • Version
  • Synonyms
  • Lexical sense
  • Usage Examples
  • Homograph information
  • Language information
  • Specific display information
  • Identifiers
  • and more…

Entries can have predefined values, which can recur but their fields can also have so-called free values, which can vary too. Such fields are:

  • Aspect
  • Tense
  • Subcategorization
  • Subject Field
  • Mood
  • Grammatical Gender
  • Geographical Usage
  • Case
  • and more…

As mentioned above, in order to triplify a dictionary one needs to have a clear defined model. Usually, when modelling linked data or just RDF it is important to make use of existing models and schemas to enable easier and more efficient use and integration. One well-known lexicon model is Lemon. Lemon contains good pieces of information to cover our dictionary needs, but not all of them. We started using also the Ontolex model, which is much more complex and is considered to be the evolution of Lemon. However, some pieces of information were still missing, so we created an additional ontology to cover all missing corners and catch the specific details that did not overlap with the Ontolex model (such as the free values).

An additional level of complexity was the need to identify exactly the missing pieces in Ontolex model and its modules and create the part for the missing information. This was part of creating the dictionary’s model which we calledontolexKD.

As a developer you never sit down to think about all the senses or meanings or translations of a word (except if you specialize in linguistics), so just to understand the complexity was a revelation for me. And still, each dictionary contains information that is specific to it and which needs to be identified and understood.

The process used in order to do the mapping consists of several steps. Imagine this as a processing pipeline which manipulates the XML data. UnifiedViews is an ETL tool, specialized in the management of RDF data, in which you can configure your own processing pipeline. One of its use cases is to triplify different data formats. I used it to map XML to RDF and upload it into a triple store. Of course this particular task can also be achieved with other such tools or methods for that matter. In UnifiedViews the processing pipeline resembles what appears in Figure 2.

Figure 2: UnifiedViews pipeline used to triplify XML (click to enlarge)

 

The pipeline is composed out of data processing units (DPUs) which communicate iteratively. In a left-to-right order the process in Figure 2 represents:

  • A DPU used to upload the XML files into UnifiedViews for further processing;
  • A DPU which transforms XML data to RDF using XSLT. The style sheet is part of the configuration of the unit;
  • The .rdf generated files are stored on the filesystem;
  • And, finally, the .rdf generated files are uploaded into a triple store, such as Virtuoso Universal server.

Basically the XML is transformed using XSLT.

Complexity increases also through the URIs (Uniform Resource Identifier) that are needed for mapping the information in the dictionary, because with Linked Data any resource should have a clearly identified and persistent identifier! The start was to represent a single word (headword) under a desired namespace and build on it to associate it with its part of speech, grammatical number, grammatical gender, definition, translation – just to begin with.

The base URIs follow the best practices recommended in the ISA study on persistent URIs following the pattern:http://{domain}/{type}/{concept}/{reference}.

An example of such URIs for the forms of a headword is:

These two URIs represent the singular masculine and singular feminine forms of the Spanish word entendedor.

If the dictionary contains two different adjectival endings, as with entendedor which has different endings for the feminine and masculine forms (entendedora and entendedor), and they are not explicitly mentioned in the dictionary than we use numbers in the URI to describe them. If the gener would be explicitly mentioned the URIs would be:

In addition, we should consider that the aim of triplifying the XML was for all these headwords with senses, forms and translations, to connect and be identified and linked following Semantic Web principles. The actual overlap and linking of the dictionary resources remains open. A second step for improving the triplification and mapping similar entries, if possible at all, still needs to be carried out. As an example, let’s take two dictionaries, say German, which contain a translation into English and an English dictionary which also contains translations into German. We get the following translations:

Bank – bank – German to English

bank – Bank – English to German

The URI of the translation from German to English was designed to look like:

And the translation from English to German would be:

In this case both represent the same translation but have different URIs because they were generated from different dictionaries (mind the translation order). These should be mapped so as to represent the same concept, theoretically, or should they not?

The word Bank in German can mean either a bench or a bank in English. When I translate both English senses back into German I get again the word Bank, but I cannot be sure which sense I translate unless the sense id is in the URI, hence the SE00006110 and SE00006116. It is important to keep the order of translation (target-source) but later map the fact that both translations refer to the same sense, same concept. This is difficult to establish automatically. It is hard even for a human sometimes.

One of the last steps of complexity was to develop a generic XSLT which can triplify all the different languages of this dictionary series and store the complete data in a triple store. The question remains: is the design of such a universal XSLT possible while taking into account the differences in languages or the differences in dictionaries?

The task at hand is not completed from the point of view of enabling the dictionary to benefit from Semantic Web principles yet. The linguist is probably the first one who can conceptualize “the how to do this”.

As a next step we will improve the Linked Data created so far and bring it to the status of a good linked language graph by enriching the RDF data with additional information, such as the history of a term or additional grammatical information etc.

References:

[1] J. Bosque-Gil, J. Gracia, E. Montiel-Ponsoda, and G. Aguado-de Cea, “Modelling multilingual lexicographic resources for the web of data: the k dictionaries case,” in Proc. of GLOBALEX’16 workshop at LREC’15, Portoroz, Slovenia, May 2016.

Categories: Blogroll

Visualize PoolParty project data with SKOS Play! in four steps

Semantic Web Company - Tue, 2016-10-04 02:16

There is a new functionality in PoolParty 5.5 that allows users to manage the skos:inScheme relationship of their concepts.

When you activate the skos:inScheme functionality for your PoolParty project you can create input data for SKOS Play! very easy. SKOS Play! is a free application that lets you render and visualize SKOS taxonomies in different formats (html, pdf) and different graphical representations (tree tabular, etc.).

With four steps you can generate such a representation based on PoolParty data: 

1) Activate skos:inScheme in your PoolParty project:

2) Apply skos:inScheme settings for concepts in your taxonomy.

For existing concepts, user can select the subtree in which the skos:inScheme setting should be applied. For new concepts you can define a behavior to automatically apply the inScheme setting on the active subtree.

This is a screenshot of a small PoolParty subtree, showing beverages that are used for cocktail creation:

Like usual, you can see the skos:ConceptScheme in purple. The narrower nodes in green represent skos:Concepts. All skos:Concepts in this subtree have a skos:inScheme relation to the skos:ConceptScheme with title “Beverages”.

 

 

3) SKOS Play!

When your PoolParty project is publicly available (help page explaining user groups in PoolParty), you can simply copy the URL of the corresponding SPARQL endpoint and paste it into the SKOS Play! input field during the upload process: http://labs.sparna.fr/skos-play/upload. In this example I simply used the SPARQL endpoint of the Cocktails thesaurus: http://vocabulary.semantic-web.at/PoolParty/sparql/cocktails. As an alternative you could also export you PoolParty project and import the resulting file in SKOS Play! A corresponding file you could retrieve from http://vocabulary.semantic-web.at/cocktails/export/cocktails.trig

For simplicity you can skip the advanced options.

4) Get results

After you hit the Next button you receive feedback that concept data was processed successfully on the top of the page. When you scroll down you have options to select the skos:ConceptScheme and language that should be further processed. In addition you have the option to print and to visualize your data. Printing lets you select between alphabetical index and tree. Both version are clickable and can be created in html or pdf format. Visualization offers different types like a collapsible tree, zoomable square or circle representations and also an autocomplete form.

I chose the tree visualization which results in a nice interactive tree. Users can click circles to unfold the tree. When a label is clicked, the user is directed to this concept URI. In this use case the user is directed to PoolParty Linked Data Frontend.

And the cool thing is that you can simply download the generated tree by right hand mouse button > Save as…

You simply have to edit the downloaded raw html file to have a fully working visualization: delete the svg element completely to generate an empty div element (id=”body”).

The generated html code can be downloaded here: SKOSPlay_blogpost.zip

By the way, you can also see a PoolParty thesaurus visualization, powered with SKOS Play! on this page: http://www.reegle.info/glossary

 

 

 

Categories: Blogroll

ICML 2016 videos and statistics

Machine Learning Blog - Fri, 2016-08-26 15:04

The ICML 2016 videos are out.

I also wanted to share some statistics from registration that might be of general interest.

The total number of people attending: 3103.

Industry: 47% University: 46%

Male: 83% Female: 14%

Local (NY, NJ, or CT): 27%

North America: 70% Europe: 18% Asia: 9% Middle East: 2% Remainder: <1% including 2 from Antarctica

Categories: Blogroll

Attend and contribute to the SEMANTiCS 2016 in Leipzig

Semantic Web Company - Tue, 2016-08-16 10:58

The 12th edition of the SEMANTiCS, which is a well known platform for professionals and researchers who make semantic computing work, will be held in the city of Leipzig from September 12th till 15th. We are proud to announce the final program of the SEMANTiCS conference. The program will cover 6 keynote speakers, 40 industry presentations, 30 scientific paper presentations, 40 poster & demo presentations and a huge number of satellite events. Special talks will given by Thomas Vavra from IDC and Sören Auer, who will feature the LEDS track. On top of that there will be a fishbowl session ‘Knowledge Graphs – A Status Update’ with lightning talks from Hans Uszkoreit (DFKI) and Andreas Blumenauer (SWC). This week, the set of our distinguished keynote speakers has been fixed and we are quite excited to have them at this years’ edition of SEMANTiCS. Please join us to listen to talks from representatives from IBM, Siemens, Springer Nature, Wikidata, International Data Corporation (IDC), Fraunhofer IAIS, Oxford University Press and the Hasso-Plattner-Institut, who will share their latest insights on applications of Semantic technologies with us. To register and be part of the SEMANTiCS 2016 in Leipzig, please go to: http://2016.semantics.cc/registration.

Share your ideas, tools and ontologies, last minute submissions
Meetup: Big Data & Linked Data – The Best of Both Worlds  

On the first eve of the SEMANTiCS conference we will discuss how Big Data & Linked Data technologies could become a perfect match. This meetup gathers experts on Big and Linked Data to discuss the future agenda on research and implementation of a joint technology development.

  • Register (free)

  • If you are interested to present your idea, approach or project which links Semantic technologies with Big Data in an ad-hoc lightning talk, please get in touch with Thomas Thurner (t.thurner@semantic-web.at).

WORKSHOPS/TUTORIALS

This year’s SEMANTiCS is starting on September 12th with a full day of exciting and interesting satellite events. In 6 parallel tracks scientific and industrial workshops and tutorials are scheduled to provide a forum for groups of researchers and practitioners to discuss and learn about hot topics in Semantic Web research.

How to find users and feedback for your vocabulary or ontology?

The Vocabulary Carnival is a unique opportunity for vocabulary publishers to showcase and share their work in form of a poster and a short presentation, meet the growing community of vocabulary publishers and users to build useful semantic, technical and social links. You can join the Carnival Minute Madness on the 13th of September.

How to submit to ELDC?

The European Linked Data Contest awards prizes to stories, products, projects or persons presenting novel and innovative projects, products and industry implementations involving linked data. The ELDC is more than yet another competition. We envisage to build a directory of the best European projects in the domain of Linked Data and the Semantic Web. This year the ELDC is awarded in the categories Linked Enterprise Data and Linked Open Data, with €1.500,- for each of the winners. Submission deadline is August 31, 2016.

7th DBpedia Community Meeting in Leipzig 2016

Co-located with SEMANTiCS, the next DBpedia meeting will be held at Leipzig on September 15th. Experts will speak about topics such as Wikidata: bringing structured data to Wikipedia with 16.000 volunteers. The 7th edition of this event covers a DBpedia showcase session, breakout sessions and a DBpedia Association meeting where we will discuss new strategies and which direction is important for DBpedia. If you like to become part of the DBpedia community and present your ideas, please submit your proposal or check our meeting website: http://wiki.dbpedia.org/meetings/Leipzig2016

Sponsorship  opportunities

We would be delighted to welcome new sponsors for SEMANTiCS 2016. You will find a number of sponsorship packages with an indication of benefits and prices here: http://semantics.cc/sponsorship-packages.

Special offer: You can buy a special SEMANTiCS industry ticket for €400 which includes a poster presentation at our marketplace. So take the opportunity to increase the visibility of your company, organisation or project among an international and high impact community. If you are interested, please contact us via email to semantics2016@fu-confirm.de.  

Categories: Blogroll

Introducing a Graph-based Semantic Layer in Enterprises

Semantic Web Company - Mon, 2016-08-15 08:34

Things, not Strings
Entity-centric views on enterprise information and all kinds of data sources provide means to get a more meaningful picture about all sorts of business objects. This method of information processing is as relevant to customers, citizens, or patients as it is to knowledge workers like lawyers, doctors, or researchers. People actually do not search for documents, but rather for facts and other chunks of information to bundle them up to provide answers to concrete questions.

Strings, or names for things are not the same as the things they refer to. Still, those two aspects of an entity get mixed up regularly to nurture the Babylonian language confusion. Any search term can refer to different things, therefore also Google has rolled out its own knowledge graph to help organizing information on the web at a large scale.

Semantic graphs can build the backbone of any information architecture, not only on the web. They can enable entity-centric views also on enterprise information and data. Such graphs of things contain information about business objects (such as products, suppliers, employees, locations, research topics, …), their different names, and relations to each other. Information about entities can be found in structured (relational databases), semi-structured (XML), and unstructured (text) data objects. Nevertheless, people are not interested in containers but in entities themselves, so they need to be extracted and organized in a reasonable way.

Machines and algorithms make use of semantic graphs to retrieve not only simply the objects themselves but also the relations that can be found between the business objects, even if they are not explicitly stated. As a result, ‘knowledge lenses’ are delivered that help users to better understand the underlying meaning of business objects when put into a specific context.

Personalization of information
The ability to take a view on entities or business objects in different ways when put into various contexts is key for many knowledge workers. For example, drugs have regulatory aspects, a therapeutical character, and some other meaning to product managers or sales people. One can benefit quickly when only confronted with those aspects of an entity that are really relevant in a given situation. This rather personalized information processing has heavy demand for a semantic layer on top of the data layer, especially when information is stored in various forms and when scattered around different repositories.

Understanding and modelling the meaning of content assets and of interest profiles of users are based on the very same methodology. In both cases, semantic graphs are used, and also the linking of various types of business objects works the same way.

Recommender engines based on semantic graphs can link similar contents or documents that are related to each other in a highly precise manner. The same algorithms help to link users to content assets or products. This approach is the basis for ‘push-services’ that try to ‘understand’ users’ needs in a highly sophisticated way.

‘Not only MetaData’ Architecture
Together with the data and content layer and its corresponding metadata, this approach unfolds into a four-layered information architecture as depicted here.

Following the NoSQL paradigm, which is about ‘Not only SQL’, one could call this content architecture ‘Not only Metadata’, thus ‘NoMeDa’ architecture. It stresses the importance of the semantic layer on top of all kinds of data. Semantics is no longer buried in data silos but rather linked to the metadata of the underlying data assets. Therefore it helps to ‘harmonize’ different metadata schemes and various vocabularies. It makes the semantics of metadata, and of data in general, explicitly available. While metadata most often is stored per data source, and therefore not linked to each other, the semantic layer is no longer embedded in databases. It reflects the common sense of a certain domain and through its graph-like structure it can serve directly to fulfill several complex tasks in information management:

  • Knowledge discovery, search and analytics
  • Information and data linking
  • Recommendation and personalization of information
  • Data visualization

Graph-based Data Modelling
Graph-based semantic models resemble the way how human beings tend to build their own models of the world. Any person, not only subject matter experts, organize information by at least the following six principles:

  1. Draw a distinction between all kinds of things: ‘This thing is not that thing’
  2. Give things names: ‘This thing is my dog Goofy’ (some might call it Dippy Dawg, but it’s still the same thing)
  3. Categorize things: ‘This thing is a dog but not a cat’
  4. Create general facts and relate categories to each other: ‘Dogs don’t like cats’
  5. Create specific facts and relate things to each other: ‘Goofy is a friend of Donald’, ‘Donald is the uncle of Huey, Dewey, and Louie’, etc.
  6. Use various languages for this; e.g. the above mentioned fact in German is ‘Donald ist der Onkel von Tick, Trick und Track’ (remember: the thing called ‘Huey’ is the same thing as the thing called ‘Tick’ – it’s just that the name or label for this thing that is different in different languages).

These fundamental principles for the organization of information are well reflected by semantic knowledge graphs. The same information could be stored as XML, or in a relational database, but it’s more efficient to use graph databases instead for the following reasons:

  • The way people think fits well with information that is modelled and stored when using graphs; little or no translation is necessary.
  • Graphs serve as a universal meta-language to link information from structured and unstructured data.
  • Graphs open up doors to a better aligned data management throughout larger organizations.
  • Graph-based semantic models can also be understood by subject matter experts, who are actually the experts in a certain domain.
  • The search capabilities provided by graphs let you find out unknown linkages or even non-obvious patterns to give you new insights into your data.
  • For semantic graph databases, there is a standardized query language called SPARQL that allows you to explore data.
  • In contrast to traditional ways to query databases where knowledge about the database schema/content is necessary, SPARQL allows you to ask “tell me what is there”.

Standards-based Semantics
Making the semantics of data and metadata explicit is even more powerful when based on standards. A framework for this purpose has evolved over the past 15 years at W3C, the World Wide Web Consortium. Initially designed to be used on the World Wide Web, many enterprises have been adopting this stack of standards for Enterprise Information Management. They now benefit from being able to integrate and link data from internal and external sources with relatively low costs.

At the base of all those standards, the Resource Description Framework (RDF) serves as a ‘lingua franca’ to express all kinds of facts that can involve virtually any kind of category or entity, and also all kinds of relations. RDF can be used to describe the semantics of unstructured text, XML documents, or even relational databases. The Simple Knowledge Organization System (SKOS) is based on RDF. SKOS is widely used to describe taxonomies and other types of controlled vocabularies. SPARQL can be used to traverse and make queries over graphs based on RDF or standard schemes like SKOS.

With SPARQL, far more complex queries can be executed than with most other database query languages. For instance, hierarchies can be traversed and aggregated recursively: a geographical taxonomy can then be used to find all documents containing places in a certain region although the region itself is not mentioned explicitly.

Standards-based semantics also helps to make use of already existing knowledge graphs. Many government organisations have made available high-quality taxonomies and semantic graphs by using semantic web standards. These can be picked up easily to extend them with own data and specific knowledge.

Semantic Knowledge Graphs will grow with your needs!
Standards-based semantics provide yet another advantage: it is becoming increasingly simpler to hire skilled people who have been working with standards like RDF, SKOS or SPARQL before. Even so, experienced knowledge engineers and data scientists are a comparatively rare species. Therefore it’s crucial to grow graphs and modelling skills over time. Starting with SKOS and extending an enterprise knowledge graph over time by introducing more schemes and by mapping to other vocabularies and datasets over time is a well established agile procedure model.

A graph-based semantic layer in enterprises can be expanded step-by-step, just like any other network. Analogous to a street network, start first with the main roads, introduce more and more connecting roads, classify streets, places, and intersections by a more and more distinguished classification system. It all comes down to an evolving semantic graph that will serve more and more as a map of your data, content and knowledge assets.

Semantic Knowledge Graphs and your Content Architecture
It’s a matter of fact that semantics serves as a kind of glue between unstructured and structured information and as a foundation layer for data integration efforts. But even for enterprises dealing mainly with documents and text-based assets, semantic knowledge graphs will do a great job.

Semantic graphs extend the functionality of a traditional search index. They don’t simply annotate documents and store occurrences of terms and phrases, they introduce concept-based indexing in contrast to term based approaches. Remember: semantics helps to identify the things behind the strings. The same applies to concept-based search over content repositories: documents get linked to the semantic layer, and therefore the knowledge graph can be used not only for typical retrieval but to classify, aggregate, filter, and traverse the content of documents.

PoolParty combines Machine Learning with Human Intelligence

Semantic knowledge graphs have the potential to innovate data and information management in any organisation. Besides questions around integrability, it is crucial to develop strategies to create and sustain the semantic layer efficiently.

Looking at the broad spectrum of semantic technologies that can be used for this endeavour, they range from manual to fully automated approaches. The promise to derive high-quality semantic graphs from documents fully automatically has not been fulfilled to date. On the other side, handcrafted semantics is error-prone, incomplete, and too expensive. The best solution often lies in a combination of different approaches. PoolParty combines Machine Learning with Human Intelligence: extensive corpus analysis and corpus learning support taxonomists, knowledge engineers and subject matter experts with the maintenance and quality assurance of semantic knowledge graphs and controlled vocabularies. As a result, enterprise knowledge graphs are more complete, up to date, and consistently used.

“An Enterprise without a Semantic Layer is like a Country without a Map.

Categories: Blogroll

PoolParty Academy is opening in September 2016

Semantic Web Company - Thu, 2016-08-04 02:16

PoolParty Academy offers three E-Learning tracks that enable customers, partners and individual professionals to learn Semantic Web technologies and PoolParty Semantic Suite in particular.

You can pre-register for the PoolParty Academy training tracks at the academy’s website or join our live class-room at the biggest European industrial Semantic Web conference – SEMANTiCS 2016.

read more

Categories: Blogroll

Web 2: But Wait, There's More (And More....) - Best Program Ever. Period.

Searchblog - Thu, 2011-10-13 12:20
I appreciate all you Searchblog readers out there who are getting tired of my relentless Web 2 Summit postings. And I know I said my post about Reid Hoffman was the last of its kind. And it was, sort of. Truth is, there are a number of other interviews happening... (Go to Searchblog Main)
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Help Me Interview Reid Hoffman, Founder, LinkedIn (And Win Free Tix to Web 2)

Searchblog - Wed, 2011-10-12 11:22
Our final interview at Web 2 is Reid Hoffman, co-founder of LinkedIn and legendary Valley investor. Hoffman is now at Greylock Partners, but his investment roots go way back. A founding board member of PayPal, Hoffman has invested in Facebook, Flickr, Ning, Zynga, and many more. As he wears (at... (Go to Searchblog Main)
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Help Me Interview the Founders of Quora (And Win Free Tix to Web 2)

Searchblog - Tue, 2011-10-11 12:54
Next up on the list of interesting folks I'm speaking with at Web 2 are Charlie Cheever and Adam D'Angelo, the founders of Quora. Cheever and D'Angelo enjoy (or suffer from) Facebook alumni pixie dust - they left the social giant to create Quora in 2009. It grew quickly after... (Go to Searchblog Main)
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Help Me Interview Ross Levinsohn, EVP, Yahoo (And Win Free Tix to Web 2)

Searchblog - Tue, 2011-10-11 11:46
Perhaps no man is braver than Ross Levinsohn, at least at Web 2. First of all, he's the top North American executive at a long-besieged and currently leaderless company, and second because he has not backed out of our conversation on Day One (this coming Monday). I spoke to Ross... (Go to Searchblog Main)
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I Just Made a City...

Searchblog - Mon, 2011-10-10 13:41
...on the Web 2 Summit "Data Frame" map. It's kind of fun to think about your company (or any company) as a compendium of various data assets. We've added a "build your own city" feature to the map, and while there are a couple bugs to fix (I'd like... (Go to Searchblog Main)
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Help Me Interview Vic Gundotra, SVP, Google (And Win Free Tix to Web 2)

Searchblog - Mon, 2011-10-10 13:03
Next up on Day 3 of Web 2 is Vic Gundotra, the man responsible for what Google CEO Larry Page calls the most exciting and important project at this company: Google+. It's been a long, long time since I've heard as varied a set of responses to any Google project... (Go to Searchblog Main)
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Help Me Interview James Gleick, Author, The Information (And Win Free Tix to Web 2)

Searchblog - Sat, 2011-10-08 20:16
Day Three kicks off with James Gleick, the man who has written the book of the year, at least if you are a fan of our conference theme. As I wrote in my review of "The Information," Gleick's book tells the story of how, over the past five thousand or... (Go to Searchblog Main)
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I Wish "Tapestry" Existed

Searchblog - Fri, 2011-10-07 14:34
(image) Early this year I wrote File Under: Metaservices, The Rise Of, in which I described a problem that has burdened the web forever, but to my mind is getting worse and worse. The crux: "...heavy users of the web depend on scores - sometimes hundreds - of services,... (Go to Searchblog Main)
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Help Me Interview Steve Ballmer, CEO of Microsoft (And Win Free Tix to Web 2)

Searchblog - Fri, 2011-10-07 12:17
Day Two at Web 2 Summit ends with my interview of Steve Ballmer. Now, the last one, some four years ago, had quite a funny moment. I asked Steve about how he intends to compete with Google on search. It's worth watching. He kind of turns purple. And not... (Go to Searchblog Main)
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Me, On The Book And More

Searchblog - Thu, 2011-10-06 12:05
Thanks to Brian Solis for taking the time to sit down with me and talk both specifically about my upcoming book, as well as many general topics.... (Go to Searchblog Main)
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Help Me Interview Michael Roth, CEO of Interpublic Group (And Win Free Tix to Web 2)

Searchblog - Thu, 2011-10-06 11:37
What's the CEO of a major advertising holding company doing at Web 2 Summit? Well, come on down and find out. Marketing dollars are the oxygen in the Internet's bloodstream - the majority of our most celebrated startups got that way by providing marketing solutions to advertisers of all stripes.... (Go to Searchblog Main)
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Help Me Interview Mary Meeker of KPCB (And Win Free Tix to Web 2)

Searchblog - Wed, 2011-10-05 14:00
For the first time in eight years, Mary Meeker will let me ask her a few questions after she does her famous market overview. Each year, Mary pushes the boundaries of how many slides she can cram into one High Order Bit, topping out at 70+ slides in ten or... (Go to Searchblog Main)
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Help Me Interview Dennis Crowley, CEO, Foursquare (And Win Free Tix to Web 2)

Searchblog - Wed, 2011-10-05 12:06
Foursquare co-founder and CEO Dennis Crowley will give his first 1-1 interview on the Web 2 stage on the conference's second day, following a morning of High Order Bits and a conversation on privacy policy with leaders from government in both the US and Canada. After Crowley will be a... (Go to Searchblog Main)
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Help Me Interview Michael Dell, CEO, Dell (And Win Free Tix to Web 2)

Searchblog - Tue, 2011-10-04 11:35
Not unlike Steve Jobs back in the 1990s, Michael Dell returned to the helm of his company at a crucial moment, when his namesake was seemingly rudderless. Back in 2007, Dell was losing marketshare to HP, Apple had not yet proven the monster it has since become in mobile, and... (Go to Searchblog Main)
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