Linked Data: Moving Towards Consumption. Earlier this month 16 out of 42 papers were accepted for the upcoming Linked Data on the Web (LDOW) 2012 Workshop in Lyon, France in April. What might be discerned from the tenor of the submissions is something of a shift in focus in the Linked Data space, according to workshop chair Dr. Michael Hausenblas, Linked Data Research Centre, DERI, NUI Galway, Ireland. Other organizing committee members include Tim Berners-Lee, Christian Bizer and Tom Heath. “In 2008 to 2010 it was more like we were establishing the field, getting people to talk about what they do in terms of publishing and best practice around Linked Data, Open Linked Data and Linked Enterprise Data,” says Hausenblas. Now, with the web of Linked Data having grown to about 32 billion RDF triples last year, “we’re moving more towards the consumption – publishing is a necessary precondition but not an end in itself.” Beyond the workshop, Hausenblas had some other Linked Data thoughts to share.
Faceted Wikipedia Search. ProgrammableWeb - Mashups, APIs, and the Web as Platform. Thinkmap SDK. The Thinkmap SDK enables organizations to incorporate data-driven visualization technology into their enterprise Web applications. Thinkmap applications allow users to make sense of complex information in ways that traditional interfaces are incapable of.
The Thinkmap SDK (v. 2.8) includes a set of out-of-the-box configurations for solving common visualization problems, as well as new visualization techniques for customizing data displays. We have designed Thinkmap to be lightweight, fast, easily extensible, and able to connect seamlessly to a wide variety of data sources. Thinkmap is composed of two primary components: an extremely lightweight and fast browser-based Visualization Component that renders the visualizations and allows for interactive exploration a Data Source API that enables connection to many different types of data sources Thinkmap's flexible architecture allows developers to configure applications to address a wide range of retrieval and discovery issues.
Maltego 3 > Community Edition. Software Packages for Graphical Models / Bayesian Networks. NetSeer Pushes Concepts, Not Keywords, for Contextual Targeting. Written on Aug 11, 2011 Author Gavin Dunaway | Share ♻ Retweet ADOTAS – At an abstract level, graph theory is about representing the relationship between nodes (interconnected objects) and edges (the lines that connect them). Considering its Herculean task, it doesn’t seem surprising that Content Mapper, the technology base of NetSeer’s concept-based contextual advertising, grew out of technology developed by UCLA scientists for studying the complexity of the human genome. “Content Mapper is complicated but intuitive,” says the NetSeer CEO.
A computer science major from MIT, Mracek been around just about every tech block, including a stint with Apple during the tumultuous 90s (i.e., before the return of Steve Jobs). He thinks about Content Mapper this way: When a person views a web page, the brain uses all his/her collected knowledge and experience to analyze what’s on that page. The system recognize “off-page” concepts and weighs the value of conceptual connections — direct, indirect, etc. New York Times - Linked Open Data.
Exhibit 3.0 Project. Getting Involved Join us on IRC on freenode or browse the SIMILE Widgets mailing list archives to ask questions about Exhibit. Chances are others may have similar questions, and the list is a great place to share answers. Background The Exhibit 3 project was supported by the Library of Congress. It is a partnership among MIT Libraries, MIT CSAIL and Zepheira, including personnel from the original SIMILE project. See the Exhibit 3.0 launch press release (PDF). Exhibit 3.0 development work proceeds from the proposed architecture (PDF) released in early 2011. Demos Exhibit 3.0: What's New? Exhibit 3.0 is available for community input. Exhibit 3.0 Scripted (client-side) Exhibit 3.0 Staged Beta 2(client/server): Scalability: Browse hundreds of thousands of records Persistence: Pick up where you left off browsing an Exhibit Export data in HTML + RDFa format Community Involvement Developers in the community are encouraged to contribute code.
Thomas Neumann: D5: Databases and Information Systems (Max-Planck-Institut für Informatik) Tetherless World Demos. Home. Schema.org. Most webmasters are familiar with HTML tags on their pages. Usually, HTML tags tell the browser how to display the information included in the tag. For example, <h1>Avatar</h1> tells the browser to display the text string "Avatar" in a heading 1 format. However, the HTML tag doesn't give any information about what that text string means—"Avatar" could refer to the hugely successful 3D movie, or it could refer to a type of profile picture—and this can make it more difficult for search engines to intelligently display relevant content to a user.
Schema.org provides a collection of shared vocabularies webmasters can use to mark up their pages in ways that can be understood by the major search engines: Google, Microsoft, Yandex and Yahoo! 1. How to mark up your content using microdata 1a. Your web pages have an underlying meaning that people understand when they read the web pages. 1b. itemscope and itemtype Let's start with a concrete example. Back to top 1c. itemprop 1d. 2. 2b. 2c. 3. 3a. 3b. How Schema.org Will Change Your Search Results & What it Means for Marketers. Ontology Instances. Welcome to OneSource - OneSource. SIMILE: Practical Metadata for the Semantic Web - Ésta es una idea de Google Docs. Semantic tools. Why the Semantic Web Will Fail. Don't get too excited by the title. But I do want to share a few thoughts... It was running through my head just now, the work that we were doing here in Moncton to work on an e-learning cluster.
Because I saw that 'cluster building' is still one of the major pillars of NRC's strategy, and I was wondering whether our work would ever be a part of that again. And I was thinking about some of the things that didn't go so well in our first few years. And they weren't interested. And I thought about where we're right today and where we might be wrong, and why. And I'm saying the semantic web won't work. But how do you explain that intuition?
And I was thinking about the edgy things of Web 2.0, and where they're working, and more importantly, where they're beginning to show some cracks. A few of key things today: - Yahoo is forcing people to give up their Flickr identities and to join the mother ship, and - the rumour that Google is turning off the search API And that's when I realized: So... Internet Semantic Web Web 3.0.
Microformats vs. RDF: How Microformats Relate to the Semantic Web. Update: Joe from the Squio blog has posted a response to this entry. Microformats are a wildly popular set of formats for embedding metadata within normal XHTML. The primary advantage Microformats offer over RDF (including its embedded serializations) is that you can embed metadata directly in the XHTML, reducing the amount of markup you need to write (e.g. you don't have to write XHTML and additional RDF).
Many people have contended that Microformats are a possible replacement for RDF, however Microformats were not designed to cover the same scope as RDF was. While both Microformats and RDF make it possible to store data about data, they simply do not work to solve the same set of problems. A quick comparison I don't blame the Microformats people for this confusion over what Microformats are or are not.
Rather, I blame the sensationalists and know-nots that tend to jump on any new standard, format, or design pattern. What Microformats were not intended to be: Conclusions. Microformat. A microformat (sometimes abbreviated μF) is a web-based approach to semantic markup which seeks to re-use existing HTML/XHTML tags to convey metadata[1] and other attributes in web pages and other contexts that support (X)HTML such as RSS. This approach allows software to process information intended for end-users (such as contact information, geographic coordinates, calendar events, and similar information) automatically.
Although the content of web pages is technically already capable of "automated processing", and has been since the inception of the web, such processing is difficult because the traditional markup tags used to display information on the web do not describe what the information means.[2] Microformats can bridge this gap by attaching semantics, and thereby obviate other, more complicated, methods of automated processing, such as natural language processing or screen scraping. Background[edit] Neither CommerceNet nor Microformats.org operates as a standards body. Class rel. Microdata (HTML5) Microdata is a WHATWG HTML specification used to nest metadata within existing content on web pages.[1] Search engines, web crawlers, and browsers can extract and process Microdata from a web page and use it to provide a richer browsing experience for users.
Search engines benefit greatly from direct access to this structured data because it allows search engines to understand the information on web pages and provide more relevant results to users.[2][3] Microdata uses a supporting vocabulary to describe an item and name-value pairs to assign values to its properties.[4] Microdata is an attempt to provide a simpler[citation needed] way of annotating HTML elements with machine-readable tags than the similar approaches of using RDFa and microformats.
Microdata vocabularies provide the semantics, or meaning of an Item. Web developers can design a custom vocabulary or use vocabularies available on the web. Here is the same markup with added Schema.org[5][6][7] Microdata: Open Mind. ConceptNet | Common Sense Computing Initiative. ConceptNet aims to give computers access to common-sense knowledge , the kind of information that ordinary people know but usually leave unstated. The data in ConceptNet is being collected from ordinary people who contributed it on sites like Open Mind Common Sense .
ConceptNet represents this data in the form of a semantic network, and makes it available to be used in natural language processing and intelligent user interfaces. ConceptNet is an open source project, with a Python implementation and a REST API that anyone can use to add computational common sense to their own project. A great tool to help you use ConceptNet in your software is Divisi . Some of the nodes and links in ConceptNet. Places to go next ConceptNet Development Team Current developers Project alumni Papers Papers about ConceptNet itself Havasi, C., Speer, R. & Alonso, J. (2007) ConceptNet 3: a Flexible, Multilingual Semantic Network for Common Sense Knowledge. Liu, H. & Singh, P. (2004). Ontology construction from text. Gellish. Gellish is a formal language that is natural language independent, although its concepts have 'names' and definitions in various natural languages. Any natural language variant, such as Gellish Formal English is a controlled natural language.
Information and knowledge can be expressed in such a way that it is computer-interpretable, as well as system-independent and natural language independent. Each natural language variant is a structured subset of that natural language and is suitable for information modeling and knowledge representation in that particular language. All expressions, concepts and individual things are represented in Gellish by (numeric) Unique Identifiers (Gellish UID's).
This enables a software to automatically generate expressions that are created in one formal natural language into any other formal natural language. Etymologically speaking, "Gellish" is originally derived from "Generic Engineering Language. " Overview[edit] - query: what <is located in> Paris 1. Common Sense Computing Initiative | at the MIT Media Lab. Years ago, we made a decision to put all our Python packages in a common namespace called csc . To put it simply, this did not work well. Today, we have finally undone this decision by deprecating the csc namespace and renaming every single one of our modules. Python programmers, please learn from our mistake and never make a namespace package. If you upgrade our software, you'll get more straightforward names for our modules. The new releases are ConceptNet 4.0.0 , Divisi2 2.2.0 , csc-utils 0.6 , and a new package called simplenlp 0.9 .
The new names to import are: csc.conceptnet → conceptnet csc.divisi2 → divisi2 csc.nl → simplenlp csc.util → csc_utils csc.corpus → conceptnet.corpus csc.lib → conceptnet.lib csc.django_settings → conceptnet.django_settings csc.pseudo_auth → conceptnet.pseudo_auth csc.webapi → conceptnet.webapi There's still a package called csc , and basically what it's there for is to make your old code keep working. From Taxonomy to Ontology: Laying the GroundWork for the Semantic Web.
Folksonomies - Cooperative Classification and Communication Through Shared Metadata. The Creation of Metadata: Professionals, Content Creators, Users Metadata is often characterized as “data about data.” Metadata is information, often highly structured, about documents, books, articles, photographs, or other items that is designed to support specific functions. These functions are usually to facilitate some organization and access of information. Administrative, structural, and descriptive metadata are three broad categories of metadata (Taylor, 2004). This paper focus primarily on descriptive metadata which identifies and functions to organize information based on its intellectual content. Traditionally metadata is created by dedicated professionals.
Catalogers create metadata, often in the form of Machine-Readable Cataloging (MARC) records for books and other intellectual creations, and this is the basis of most Online Public Access Catalogs (OPAC) in libraries and other institutions. Tagging Content in Del.icio.us and Flickr “a social bookmarks manager. Limitations. YAGO-NAGA - D5: Databases and Information Systems (Max-Planck-Institut für Informatik) Overview YAGO is a huge semantic knowledge base, derived from Wikipedia WordNet and GeoNames. Currently, YAGO has knowledge of more than 10 million entities (like persons, organizations, cities, etc.) and contains more than 120 million facts about these entities. YAGO is special in several ways: The accuracy of YAGO has been manually evaluated, proving a confirmed accuracy of 95%. Every relation is annotated with its confidence value.YAGO combines the clean taxonomy of WordNet with the richness of the Wikipedia category system, assigning the entities to more than 350,000 classes.YAGO is an ontology that is anchored in time and space.
YAGO attaches a temporal dimension and a spacial dimension to many of its facts and entities.In addition to a taxonomy, YAGO has thematic domains such as "music" or "science" from WordNet Domains.YAGO extracts and combines entities and facts from 10 Wikipedias in different languages. Demo of a Semantic Web Portal. Inc. - Semantic Web Technologies.
Omeka | Home. Associative model of data. The associative model of data is an alternative data model for database systems. Other data models, such as the relational model and the object data model, are record-based. These models involve encompassing attributes about a thing, such as a car, in a record structure. Such attributes might be registration, colour, make, model, etc. In the associative model, everything which has “discrete independent existence” is modeled as an entity, and relationships between them are modeled as associations. The granularity at which data is represented is similar to schemes presented by Chen (Entity-relationship model); Bracchi, Paolini and Pelagatti (Binary Relations); and Senko (The Entity Set Model). A number of claims made about the model by Simon Williams, in his book The Associative Model of Data, distinguish the associative model from more traditional models.
Discussion[edit] In an associative database management system, data and metadata (data about data) are stored as two types of things: XML Introduction - What is XML? Kif. RDF and Jena. RDF-Gravity. Why triples are not enough. OpenRDF.org: Home. Describing Copyright in RDF - Creative Commons Rights Expression Language. RDFa Basics. Using Dublin Core - The Elements. Primer - Getting into the semantic web and RDF using N3. RDFa Primer. The Friend of a Friend (FOAF) project | FOAF project.
RDF Vocabulary Description Language 1.0: RDF Schema. RDFa, Drupal and a Practical Semantic Web. Open Archives Initiative - Protocol for Metadata Harvesting - v.2.0. Open Archives Initiative Protocol for Metadata Harvesting. Cross-media: Controlling your Language: a Directory of Metadata Vocabularies. How to publish Linked Data on the Web.
The Linking Open Data cloud diagram. SweoIG/TaskForces/CommunityProjects/LinkingOpenData - ESW Wiki. Linked Data | Linked Data - Connect Distributed Data across the Web.