Huzzah! for Local Government Open Data, Transparency, Community Indicators and Citizen Journalism

While the Knight News Challenge is still working its way through the screening details, Structured Dynamics‘ Citizen DAN proposal remains in the hunt. Listen to this:

To date, we have been the most viewed proposal by far (2x more than the second most viewed!!! Hooray!) and are in the top five of highest rated (have also been at #1 or #2, depending. Hooray!). Thanks to all of you for your interest and support.

There is much to recommend this KNC approach, not the least of which being able to attract some 2,500 proposals seeking a piece of the 2010 $5 million potential grant awards. Our proposal extends SD’s basic structWSF and conStruct Drupal frameworks to provide a data appliance and network (DAN) to support citizen journalists with data and analysis at the local, community level.

None of our rankings, of course, guarantees anything. But, we also feel good about how the market is looking at these frameworks. We have recently been awarded some pretty exciting and related contracts. Any and all of these initiatives will continue to contribute to the open source Citizen DAN vision.

And, what might that vision be? Well, after some weeks away from it, I read again our online submission to the Knight News Challenge. I have to say: It ain’t too bad! (Plus many supporting goodies and details.)

So, I repeat in its entirety below, the KNC questions and our formal responses. This information from our original submittal is unchanged, except to add some live links where they could not be submitted as such before. (BTW, the bold headers are the KNC questions.) Eventual winners are slated to be announced around mid-June. We’re keeping our fingers crossed, but we are pursuing this initiative in any case.

Describe your project:

Citizen DAN is an open source framework to leverage relevant local data for citizen journalists. It is a:

• Appliance for filtering and analyzing data specific to local community indicators
• Means to visualize local data over time or by neighborhood
• Meeting place for the public to upload and share local data and information
• Web data portal that can be individually tailored by any local community
• Node in a global network of communities across which to compare indicators of community well-being.

Good decisions and good journalism require good information. Starting with pre-loaded government data, Citizen DAN provides any citizen the framework to learn and compare local statistics and data with other similar communities. This helps to promote the grist for citizen journalism; it is also a vehicle for discovery and learning across the community.

Citizen DAN comes pre-packaged with all necessary deployment components and documentation, including local data from government sources. It includes facilities for direct upload of additional local data in formats from spreadsheets to standard databases. Many standard converters are included with the basic package.

Citizen DAN may be implemented by local governments or by community advocacy groups. When deployed, using its clear documentation, sponsors may choose whether or what portions of local data are exposed to the broader Citizen DAN network. Data exposed on the network is automatically available to any other network community for comparison and analysis purposes.

This data appliance and network (DAN) is multi-lingual. It will be tested in three cities in Canada and the US, showing its multi-lingual capabilities in English, Spanish and French.

How will your project improve the way news and information are delivered to geographic communities?

With Citizen DAN, anyone with Web access can now get, slice, and dice information about how their community is doing and how it compares to other communities. We have learned from Web 2.0 and user-generated content that once exposed, useful information can be taken and analyzed in valuable and unanticipated ways.

The trick is to get information that already exists. Citizen journalists of the past may not have either known:

1. Where to find relevant information, or
2. How to ‘slice-and-dice’ that information to extract meaningful nuggets.

By removing these hurdles, Citizen DAN improves the ways information is delivered to communities and provides the framework for sifting through it to extract meaning.

How is your idea innovative? (new or different from what already exists)

Government public data in electronic tabular form or as published listings or tables in local newspapers has been available for some time. While meeting strict ‘disclosure’ requirements, this information has neither been readily analyzable nor actionable.

The meaning of information lies in its interpretation and analysis.

Citizen DAN is innovative because it:

1. Is a platform for accessing and exposing available community data
2. Provides powerful Web-based tools for drilling down and mining data
3. Changes the game via public-provided data, and
4. Packages Citizen DAN in a Web framework that is available to any local citizen and requires no expertise other than clicking links.

What experience do you or your organization have to successfully develop this project?

Structured Dynamics has already developed and released as open-source code structWSF and conStruct , the basic foundations to this proposal. structWSF provides the network and dataset “backbone” to this proposal; conStruct provides the Drupal portal and Web site framework.

To this foundation we add proven experience and knowledge of datasets and how to access them, as well as tools and converters for how to stage them for standard public use. A key expertise of Structured Dynamics is the conversion of virtually any legacy data format into interoperable canonical forms.

These are important challenges, which require experience in the semantics of data and mapping from varied forms into useful and common frameworks. Structured Dynamics has codified its expertise in these areas into the software underlying Citizen DAN.

Structured Dynamics’ principals are also multi-lingual, with language-neutral architectures and code. The company’s principals are also some of the most prominent bloggers and writers in the semantic Web. We are acknowledged as attentive to documentation and communication.

Finally, Structured Dynamics’ principals have more than a decade of track record in successful data access and mining, and software and venture development.

To this strong basis, we have preliminary city commitments for deploying this project in the United States (English and Spanish) and Canada (French and English).

What unmet need does your proposal answer?

ThisWeKnow offers local Census data, but no community or publishing aspects. Data sharing is in DataSF and DataMine (NYC), but they lack collaboration, community networks and comparisons, or powerful data visualization or mapping.

Citizen DAN is a turnkey platform for any size community to create, publish, search, browse, slice-and-dice, visualize or compare indicators of community well-being. Its use makes the Web more locally focused. With it, researchers, watchdog groups, reporters, local officials and interested citizens can now discover hard data for ‘new news’ or fact-check mainstream media.

What tasks/benchmarks need to be accomplished to develop your project and by when will you complete them?

There are two releases with feedback. Each task summary, listing of task hours (hr) and duration in months (mo), in rough sequence order with overlaps, is:

1. Dataset Prep/Staging: identify, load and stage baseline datasets; provide means for aggregating data at different levels; 420 hr; 2.5 mo
2. Refine Data Input Facility: feature to upload other external data, incl direct from local sources; XML, spreadsheet, JSON forms; dataset metadata; 280 hr; 3 mo
3. Add Data Visualization Component: Flex mapping/data visualization (charts, graphs) using any slice-and-dice; 390 hr; 3 mo
4. Make Multi-linguality Changes: English, French, Spanish versions; 220 hr; 2 mo
5. Refine User Interface: update existing interface in faceted browse; filter; search; record create, manage and update; imports; exports; and user access rights; 380 hr; 3 mo
6. Standard Citizen DAN Ontologies: the coherent schema for the data; 140 hr; 3 mo
7. Create Central Portal: distribution and promotion site for project; 120 hr; 2 mo
8. Deploy/Test First Release: release by end of Mo 5 @ 3 test sites; 300 hr; 4 mo
9. Revise Based on Feedback: bug fixing and 4 mo testing/feedback, then revision #2; 420 hr
10. Package/Document: component packaging for easier installs; increased documentation; 310 hr; 2 mo
11. Marketing/Awareness: see next question; 240 hr; 12 mo
12. Project Management: standard PM/interact with test communities, partners; 220 hr; 12 mo.

See attached task details.

What will you have changed by the end of your project?

"Information is the currency of democracy." Thomas Jefferson (n.b.)

We intuitively understand that an informed citizenry is a healthy polity. At the global level and in 250 languages, we see how Wikipedia, matched with the Internet and inexpensive laptops, is bringing unforeseen information and enrichment to all. Across the board, we are seeing the democratization of information.

But very little of this revolution has percolated to the local level.

Only in the past decade or so have we seen free, electronic access to national Census data. We still see local data only published in print or not available at all, limiting both awareness but more importantly understanding and analysis. Data locked up in municipal computers or available but not expressed via crowdsourcing is as good as non-existent.

Though many citizens at the local level are not numeric, intuition has to tell us that the absense of empirical, local data hurts our ability to understand, reason and debate our local circumstances. Are we doing better or worse than yesterday? Than in comparison with our peers? Under what measures does this have meaning about community well being?

The purpose of the Citizen DAN project is to create an appliance — in the same sense of refrigerators keeping our food from spoiling — by which any citizen can crack open and expose relevant data at the local level. Citizen DAN is about enrichening our local information and keeping our communities healthy.

How will you measure progress and ultimately success?

We will measure the progress of the project by the number of communities and local organizations that use the Citizen DAN platform to create and publish community data. Subsidiary measures include the number of:

• Individual users across all installations
• Users contributing uploaded datasets
• Contributed datasets
• Contributed applications based on the platform
• Interconnected sites in the network
• Different Citizen DAN networks
• Substantive articles and blog posts on Citizen DAN
• Mentions of ‘Citizen DAN’ (and local naming or variants, which will be tracked) in news articles
• Contributed blog posts on the central Citizen DAN portal
• Software package downloads, and
• Google citations and hits on ‘Citizen DAN’ (and prominent variants).

These measures, plus active sites with profiles of each, will be monitored and tracked on the central Citizen DAN portal.

‘Ultimate success’ is related to the general growth in transparent government at the local level. Growth in Citizen DAN-related measures on a year-over-year basis or in relation to Gov2.0 would indicate success.

Do you see any risk in the development of your project?

There is no technical risk to this proposal, but there are risks in scope, awareness and acceptance. Our system has been operational for one year for relevant use cases; all components have been integrated, debugged, and put into production.

Scope risks relate to how much data the Citizen DAN platform is loaded with, and how much functionality is included. We balance the data question by using common public datasets for baseline data, then add features for localities to “crowdsource” their own supplementary data. We balance the functionality question by limiting new development to data visualization/mapping and to upload functions (per above), and then to refine what already exists.

Awareness risks arise from a crowded attention space. We can overcome this in two ways. The first is to satisfy users at our test sites. That will result in good recommendations to help seed a snowball effect. The second way is to use social media and our existing Web outlets aggressively. We have been building awareness for our own properties in steady, inch-by-inch measures. While a notable few Web efforts may go viral, the process is not predictable. Steady, constant focus is our preferred recipe.

Acceptance risk is intimately linked with awareness and use. If we can satisfy each Citizen DAN community, then new datasets, new functionality and new awareness will naturally arise. More users and more contributions through the network effect are the best way to broad acceptance.

What is your marketing plan? How will people learn about what you are doing?

Marketing and awareness efforts will include our use of social media, dedicated Web sites, support from test communities, and outreach to relevant community Web sites.

Our own blogs are popular in the semantic Web and structured data space (~3K uniques daily); we have published two posts on Citizen DAN and will continue to do so with more frequency once the effort gets underway.

We will create a central portal (http://citizen-dan.org) based on the project software (akin to our other project sites). The model for this apps and deployments clearinghouse is CrimeReports.com. Using social aspects and crowdsourcing, the site will encourage sharing and best practices amongst the growing number of Citizen DAN communities.

We will blog and post announcements for key releases and milestones on relevant external Web sites including various Gov 2.0 sites, Community Indicators Consortium, GovLoop, Knight News Challenge, the Sunlight Foundation, and so forth. In addition, we will collate and track individual community efforts (maintained on the central Citizen DAN site) and make specific outreach to community data sites (such as DataSF or DataMine at NYC.gov). We will use Twitter (#CitizenDAN, etc) and the social networks of LinkedIn, Facebook, and Meetup to promote Citizen DAN activity.

We will interact with advocates of citizen journalism, and engage civic organizations, media, and government officials (esp in our three test communities) to refine our marketing plan.

Is this a one-time experiment or do you think it will continue after the grant?

Citizen DAN is not an experiment. It is a working framework that gives any locality and its citizenry the means to assemble, share and compare measures of its community well-being with other communities. These indicators, in turn, provide substance and grist for greater advocacy and writing and blogging (”journalism”) at the local level.

Granted, there are unknowns: How many localities will adopt the Citizen DAN appliance? How essential will its data be to local advocacy and news? How active will each Citizen DAN installation be in attracting contributions and local data?

We submit the better way to frame the question is the degree of adoption, as opposed to will it work.

Web-based changes in our society and social interaction are leading to the democratization of information, access to it, and channels for expression. Whether ultimately successful in the specific form proposed herein, Citizen DAN and its open source software and frameworks will surely be adopted in one form or another — to one degree or another — in the unassailable trend toward local government transparency and citizen involvement.

In short, Yes: We believe Citizen DAN will continue long after the grant.

If it is to be self-sustainable, what is the plan for making that happen?

Our plan begins with the nature of Citizen DAN as software and framework. Sustainability is a question of whether the appliance itself is useful, and how users choose to leverage it.

Mediawiki, the software behind Wikipedia, is an analog. Mediawiki is an enabling infrastructure. Some sites using it are not successful; others wildly so. Success has required the combination of a good appliance with topicality and good management. The same is true for Citizen DAN.

Our plan thus begins with Citizen DAN as a useful appliance, as free open source with great documentation and prominent initial use cases. Our plan continues with our commitment to the local citizen marketplace.

We are developing Citizen DAN because of current trends. We foresee many hundreds of communities adopting the system. Most will be able to do so on their own. Some others may require modifications or assistance. Our self-interest is to ensure a high level of adoption.

An era of citizen engagement is unfolding at the local level, fueled by Web technologies and growing comfort with crowdsourcing and social networks. Meanwhile, local government constraints and pressures for transparency are unleashing locked-up data. These forces will create new opportunities for data literacy by the public, that will itself bring new understanding and improvements in governance and budgeting. We plan on Citizen DAN and its offspring to be one of the catalysts for those changes.

140 Tools: 20 Must Haves, 70 Possible Usefuls, and 50 Has Beens and Marginals

Well, for another client and another purpose, I was goaded into screening my Sweet Tools listing of semantic Web and -related tools and to assemble stuff from every other nook and cranny I could find. The net result is this enclosed listing of some 140 or so tools — most open source — related to semantic Web ontology building in one way or another.

Ever since I wrote my Intrepid Guide to Ontologies nearly three years ago (and one of the more popular articles of this site, though it is now perhaps a bit long in the tooth), I have been intrigued with how these semantic structures are built and maintained. That interest, in no small measure, is why I continue to maintain the Sweet Tools listing.

As far as I know, the following is the largest and most comprehensive listing of ontology building tools available. I broadly interpret the classification of ‘ontology building’; I include, for example, vocabulary extraction and prompting tools, as well as ontology visualization and mapping.

There are some 140 tools, perhaps 90 or so are still in active use. (Given the scope, not every tool could be inspected in detail. Some listed as being perhaps inactive may not be so, and others not in that category perhaps should be.) Of the entire roster of tools, somewhere on the order of 12 to 20 are quite impressive and deserving of local installation, test runs, and close inspection.

There are relatively few tools useful to non-specialists (or useful to engaging knowledgeable publics in the ontology-building exercise). There appear to be key gaps in the entire workflow from domain scoping and initial ontology definition and vocabulary candidates, to longer-term maintenance and revision. For example, spreadsheets would appear to be a possible useful first step in any workflow process (which is why irON is listed), but the spreadsheet tool per se is not listed herein (nor are text editors).

I surely have missed some tools and likely improperly assigned others. Please drop me an email or comment on this post with any revisions or suggestions.

Some Worth A Closer Look

In my own view, there are some tools that definitely deserve a closer look. My favorite candidates — for very different reasons and for very different places in the workflow — are (in no particular order): Apelon DTS, irON, FlexViz, Knoodl, Protégé, diagramic.com, BooWa, COE, ontopia, Anzo, PoolParty, Vine (and voc2rdf), Erca, Graphl, and GrOWL. Each one of these links is more fully described below. Also, all tools in the Vocabulary Prompting Tools category (which also includes extraction) are worth reviewing since all or nearly all have online demos.

Other tools may also be deserving, depending on use case. Some of the more specific analysis and conversion tools, for example, are in the Miscellaneous category.

Also, some purists may quibble with why some tools are listed here (such as inclusion of some stuff related to Topic Maps). Well, my answer to that is there are no real complete solutions, and whatever we can pragmatically do today requires glueing together many disparate parts.

Comprehensive Ontology Tools

• Altova SemanticWorks is a visual RDF and OWL editor that auto-generates RDF/XML or nTriples based on visual ontology design. No open source version available
• Amine is a rather comprehensive, open source platform for the development of intelligent and multi-agent systems written in Java. As one of its components, it has an ontology GUI with text- and tree-based editing modes, with some graph visualization
• The Apelon DTS (Distributed Terminology System) is an integrated set of open source components that provides comprehensive terminology services in distributed application environments. DTS supports national and international data standards, which are a necessary foundation for comparable and interoperable health information, as well as local vocabularies. Typical applications for DTS include clinical data entry, administrative review, problem-list and code-set management, guideline creation, decision support and information retrieval.. Though not strictly an ontology management system, Apelon DTS has plug-ins that provide visualization of concept graphs and related functionality that make it close to a complete solution
• DOME is a programmable XML editor which is being used in a knowledge extraction role to transform Web pages into RDF, and available as Eclipse plug-ins. DOME stands for DERI Ontology Management Environment
• FlexViz is a Flex-based, Protégé-like client-side ontology creation, management and viewing tool; very impressive. The code is distributed from Sourceforge; there is a nice online demo available; there is a nice explanatory paper on the system, and the developer, Chris Callendar, has a useful blog with Flex development tips
• Knoodl facilitates community-oriented development of OWL based ontologies and RDF knowledge bases. It also serves as a semantic technology platform, offering a Java service-based interface or a SPARQL-based interface so that communities can build their own semantic applications using their ontologies and knowledgebases. It is hosted in the Amazon EC2 cloud and is available for free; private versions may also be obtained. See especially the screencast for a quick introduction
• The NeOn toolkit is a state-of-the-art, open source multi-platform ontology engineering environment, which provides comprehensive support for the ontology engineering life-cycle. The v2.3.0 toolkit is based on the Eclipse platform, a leading development environment, and provides an extensive set of plug-ins covering a variety of ontology engineering activities. You can add these plug-ins or get a current listing from the built-in updating mechanism
• ontopia is a relative complete suite of tools for building, maintaining, and deploying Topic Maps-based applications; open source, and written in Java. Could not find online demos, but there are screenshots and there is visualization of topic relationships
• Protégé is a free, open source visual ontology editor and knowledge-base framework. The Protégé platform supports two main ways of modeling ontologies via the Protégé-Frames and Protégé-OWL editors. Protégé ontologies can be exported into a variety of formats including RDF(S), OWL, and XML Schema. There are a large number of third-party plugins that extends the platform’s functionality
  • Protégé Plugin Library – frequently consult this page to review new additions to the Protégé editor; presently there are dozens of specific plugins, most related to the semantic Web and most open source
  • Collaborative Protégé is a plug-in extension of the existing Protégé system that supports collaborative ontology editing as well as annotation of both ontology components and ontology changes. In addition to the common ontology editing operations, it enables annotation of both ontology components and ontology changes. It supports the searching and filtering of user annotations, also known as notes, based on different criteria. There is also an online demo
• TopBraid Composer is an enterprise-class modeling environment for developing Semantic Web ontologies and building semantic applications. Fully compliant with W3C standards, Composer offers comprehensive support for developing, managing and testing configurations of knowledge models and their instance knowledge bases. It is based on the Eclipse IDE. There is a free version (after registration) for small ontologies.

Not Apparently in Active Use

• Adaptiva is a user-centred ontology building environment, based on using multiple strategies to construct an ontology, minimising user input by using adaptive information extraction
• Exteca is an ontology-based technology written in Java for high-quality knowledge management and document categorisation, including entity extraction. Though code is still available, no updates have been provided since 2006. It can be used in conjunction with search engines
• IODT is IBM’s toolkit for ontology-driven development. The toolkit includes EMF Ontolgy Definition Metamodel (EODM), EODM workbench, and an OWL Ontology Repository (named Minerva)
• KAON is an open-source ontology management infrastructure targeted for business applications. It includes a comprehensive tool suite allowing easy ontology creation and management and provides a framework for building ontology-based applications. An important focus of KAON is scalable and efficient reasoning with ontologies
• Ontolingua provides a distributed collaborative environment to browse, create, edit, modify, and use ontologies. The server supports over 150 active users, some of whom have provided us with descriptions of their projects. Provided as an online service; software availability not known.

Vocabulary Prompting Tools

• AlchemyAPI from Orchestr8 provides an API based application that uses statistical and natural language processing methods. Applicable to webpages, text files and any input text in several languages
• BooWa is a set expander for any language (formerly known as SEALS); developed by RC Wang of Carnegie Mellon
• Google Keywords allows you to enter a few descriptive words or phrases or a site URL to generate keyword ideas
• Google Sets for automatically creating sets of items from a few examples
• Open Calais is free limited API web service to automatically attach semantic metadata to content, based on either entities (people, places, organizations, etc.), facts (person ‘x’ works for company ‘y’), or events (person ‘z’ was appointed chairman of company ‘y’ on date ‘x’). The metadata results are stored centrally and returned to you as industry-standard RDF constructs accompanied by a Globally Unique Identifier (GUID)
• Query-by-document from BlogScope has a nice phrase extraction service, with a choice of ranking methods. Can also be used in a Firefox plug-in (not texted with 3.5+)
• SemanticHacker (from Textwise) is an API that does a number of different things, including categorization, search, etc. By using ‘concept tags’, the API can be leveraged to generate metadata or tags for content
• TagFinder is a Web service that automatically extracts tags from a piece of text. The tags are chosen based on both statistical and linguistic analysis of the original text
• Tagthe.net has a demo and an API for automatic tagging of web documents and texts. Tags can be single words only. The tool also recognizes named entities such as people names and locations
• TermExtractor extracts terminology consensually referred in a specific application domain. The software takes as input a corpus of domain documents, parses the documents, and extracts a list of “syntactically plausible” terms (e.g. compounds, adjective-nouns, etc.)
• TermFinder uses Poisson statistics, the Maximum Likelihood Estimation and Inverse Document Frequency between the frequency of words in a given document and a generic corpus of 100 million words per language; available for English, French and Italian
• TerMine is an online and batch term extractor that emphasizes part of speech (POS) and n-gram (phrase extraction). TerMine is the terminological management system with the C-Value term extraction and AcroMine acronym recognition integrated
• Topia term extractor is a part-of-speech and frequency based term extraction tool implemented in python. Here is a term extraction demo based on this tool
• Topicalizer is a service which automatically analyses a document specified by a URL or a plain text regarding its word, phrase and text structure. It provides a variety of useful information on a given text including the following: Word, sentence and paragraph count, collocations, syllable structure, lexical density, keywords, readability and a short abstract on what the given text is about

• TrMExtractor does glossary extraction on pure text files for either English or Hungarian

• Wikify! is a system to automatically “wikify” a text by adding Wikipedia-like tags throughout the document. The system extracts keywords and then disambiguates and matches them to their corresponding Wikipedia definition
• Yahoo! Placemaker is a freely available geoparsing Web service. It helps developers make their applications location-aware by identifying places in unstructured and atomic content – feeds, web pages, news, status updates – and returning geographic metadata for geographic indexing and markup
• Yahoo! Term Extraction Service is an API to Yahoo’s term extraction service, as well as many other APIs and services in a variety of languages and for a variety of tasks; good general resource. The service has been reported to be shut down numerous times, but apparently is kept alive due to popular demand.

Initial Ontology Development

• COE COE (CmapTools Ontology Editor) is a specialized version of the CmapTools from IMHC. COE — and its CmapTools parent — is based on the idea of concept maps. A concept map is a graph diagram that shows the relationships among concepts. Concepts are connected with labeled arrows, with the relations manifesting in a downward-branching hierarchical structure. COE is an integrated suite of software tools for constructing, sharing and viewing OWL encoded ontologies based on these constructs
• Conzilla2 is a second generation concept browser and knowledge management tool with many purposes. It can be used as a visual designer and manager of RDF classes and ontologies, since its native storage is in RDF. It also has an online collaboration server
• http://diagramic.com/ has an online Flex network graph demo, which also has a neat facility for quick entry and visualization of relationships; mostly small scale; pretty cool. Does not appear to be code available anywhere
• DogmaModeler is a free and open source, ontology modeling tool based on ORM. The philosophy of DogmaModeler is to enable non-IT experts to model ontologies with a little or no involvement of an ontology engineer; project is quite old, but the software is still available and it may provide some insight into naive ontology development
• Erca is a framework that eases the use of Formal and Relational Concept Analysis, a neat clustering technique. Though not strictly an ontology tool, Erca could be implemented in a work flow that allows easy import of formal contexts from CSV files, then algorithms that computes the concept lattice of the formal contexts that can be exported as dot graphs (or in JPG, PNG, EPS and SVG formats). Erca is provided as an Eclipse plug-in
• GraphMind is a mindmap editor for Drupal. It has the basic mindmap features and some Drupal specific enhancements. There is a quick screencast about how GraphMind looks like and what is does. The Flex source is also available from Github
• GrOWL is the software framework to provide graphical, intuitive browsing and editing of knowledge maps. GrOWL is open source and is used in several projects worldwide. None of the online demos apparently work, but the screenshots look interesting and the code is still available
• irON using spreadsheets, via its notation and specification. Spreadsheets can be used for initial authoring, esp if the irON guidelines are followed. See further this case study of Sweet Tools in a spreadsheet using irON (commON)
• ITM T3 stands for Terminology, Thesaurus, Taxonomy, Metadata dictionary. ITM T3 includes a range of functions for managing enterprise shareable multilingual domain-specific taxonomies, thesaurus, terminologies in a unified way. It uses XML, SKOS and RDF standards. Commercial; from Mondeca
• MindRaider is Semantic Web outliner. It aims to connect the tradition of outline editors with emerging technologies. MindRaider mission is to organize not only the content of your hard drive but also your cognitive base and social relationships in a way that enables quick navigation, concise representation and inferencing
• Topincs is a Topic Map authoring software that allows groups to share their knowledge over the web. It makes use of a variety of modern technologies. The most important are Topic Maps, REST and Ajax. It consists of three components: the Wiki, the Editor, and the Server. The servier requires AMP; the Editor and Wiki are based on browser plug-ins.

Ontology Editing

• First, see all of the Comprehensive Tools listing above
• Anzo for Excel includes an (RDFS and OWL-based) ontology editor that can be used directly within Excel. In addition to that, Anzo for Excel includes the capability to automatically generate an ontology from existing spreadsheet data, which is very useful for quick bootstrapping of an ontology.
• Hozo is an ontology visualization and development tool that brings version control constructs to group ontology development; limited to a prototype, with no online demo
• Lexaurus Editor is for off-line creation and editing of vocabularies, taxonomies and thesauri. It supports import and export in Zthes and SKOS XML formats, and allows hierarchical / poly-hierarchical structures to be loaded for editing, or even multiple vocabularies to be loaded simultaneously, so that terms from one taxonomy can be re-used in another, using drag and drop. Not available in open source
• Model Futures OWL Editor combines simple OWL tools, featuring UML (XMI), ErWin, thesaurus and imports. The editor is tree-based and has a “navigator” tool for traversing property and class-instance relationships. It can import XMI (the interchange format for UML) and Thesaurus Descriptor (BT-NT XML), and EXPRESS XML files. It can export to MS Word.
• OntoTrack is a browsing and editing ontology authoring tool for OWL Lite. It combines a sophisticated graphical layout with mouse enabled editing features optimized for efficient navigation and manipulation of large ontologies
• OWLViz is an attractive visual editor for OWL and is available as a Protégé plug-in
• PoolParty is a triple store-based thesaurus management environment which uses SKOS and text extraction for tag recommendations. See further this manual, which describes more fully the system’s functionality. Also, there is a PoolParty Web service that enables a Zthes thesaurus in XML format to be uploaded and converted to SKOS (via skos:Concepts)
• SKOSEd is a plugin for Protege 4 that allows you to create and edit thesauri (or similar artefacts) represented in the Simple Knowledge Organisation System (SKOS).
• TemaTres is a Web application to manage controlled vocabularies, taxonomies and thesaurus. The vocabularies may be exported in Zthes, Skos, TopicMap, etc.
• ThManager is a tool for creating and visualizing SKOS RDF vocabularies. ThManager facilitates the management of thesauri and other types of controlled vocabularies, such as taxonomies or classification schemes
• Vitro is a general-purpose web-based ontology and instance editor with customizable public browsing. Vitro is a Java web application that runs in a Tomcat servlet container. With Vitro, you can: 1) create or load ontologies in OWL format; 2) edit instances and relationships; 3) build a public web site to display your data; and 4) search your data with Lucene. Still in somewhat early phases, with no online demos and with minimal interfaces.

Not Apparently in Active Use

• Omnigator The Omnigator is a form-based manipulaton tool centered on Topic Maps, though it enables the loading and navigation of any conforming topic map in XTM, HyTM, LTM or RDF formats. There is a free evaluation version.
• OntoGen is a semi-automatic and data-driven ontology editor focusing on editing of topic ontologies (a set of topics connected with different types of relations). The system combines text-mining techniques with an efficient user interface. It requires .Net.
• OWL-S-editor is an editor for the development of services in OWL-S, with graphical, WSDL and import/export support
• ReTAX+ is an aide to help a taxonomist create a consistent taxonomy and in particular provides suggestions as to where a new entity could be placed in the taxonomy whilst retaining the integrity of the revised taxonomy (c.f., problems in ontology modelling)
• SWOOP is a lightweight ontology editor. (Swoop is no longer under active development at mindswap. Continuing development can be found on SWOOP’s Google Code homepage at http://code.google.com/p/swoop/)
• WebOnto supports the browsing, creation and editing of ontologies through coarse grained and fine grained visualizations and direct manipulation.

Ontology Mapping

• COMA++ is a schema and ontology matching tool with a comprehensive infrastructure. Its graphical interface supports a variety of interaction
• ConcepTool is a system to model, analyse, verify, validate, share, combine, and reuse domain knowledge bases and ontologies, reasoning about their implication
• MatchIT automates and facilitates schema matching and semantic mapping between different Web vocabularies. MatchIT runs as a stand-alone or plug-in Eclipse application and can be integrated with popular third party applications. MatchIT’s uses Adaptive Lexicon™ as an ontology-driven dictionary and thesaurus of English language terminology to quantify and ank the semantic similarity of concepts. It apparently is not available in open source
• myOntology is used to produce the theoretical foundations, and deployable technology for the Wiki-based, collaborative and community-driven development and maintenance of ontologies instance data and mappings
• OLA/OLA2 (OWL-Lite Alignment) matches ontologies written in OWL. It relies on a similarity combining all the knowledge used in entity descriptions. It also deal with one-to-many relationships and circularity in entity descriptions through a fixpoint algorithm
• Potluck is a Web-based user interface that lets casual users—those without programming skills and data modeling expertise—mash up data themselves. Potluck is novel in its use of drag and drop for merging fields, its integration and extension of the faceted browsing paradigm for focusing on subsets of data to align, and its application of simultaneous editing for cleaning up data syntactically. Potluck also lets the user construct rich visualizations of data in-place as the user aligns and cleans up the data.
• PRIOR+ is a generic and automatic ontology mapping tool, based on propagation theory, information retrieval technique and artificial intelligence model. The approach utilizes both linguistic and structural information of ontologies, and measures the profile similarity and structure similarity of different elements of ontologies in a vector space model (VSM).
• Vine is a tool that allows users to perform fast mappings of terms across ontologies. It performs smart searches, can search using regular expressions, requires a minimum number of clicks to perform mappings, can be plugged into arbitrary mapping framework, is non-intrusive with mappings stored in an external file, has export to text files, and adds metadata to any mapping. See also http://sourceforge.net/projects/vine/.

Not Apparently in Active Use

• ASMOV (Automated Semantic Mapping of Ontologies with Validation) is an automatic ontology matching tool which has been designed in order to facilitate the integration of heterogeneous systems, using their data source ontologies
• Chimaera is a software system that supports users in creating and maintaining distributed ontologies on the web. Two major functions it supports are merging multiple ontologies together and diagnosing individual or multiple ontologies
• CMS (CROSI Mapping System) is a structure matching system that capitalizes on the rich semantics of the OWL constructs found in source ontologies and on its modular architecture that allows the system to consult external linguistic resources
• ConRef is a service discovery system which uses ontology mapping techniques to support different user vocabularies
• DRAGO reasons across multiple distributed ontologies interrelated by pairwise semantic mappings, with a vision of peer-to-peer mapping of many distributed ontologies on the Web. It is implemented as an extension to an open source Pellet OWL Reasoner
• Falcon-AO (Finding, aligning and learning ontologies) is an automatic ontology matching tool that includes the three elementary matchers of String, V-Doc and GMO. In addition, it integrates a partitioner PBM to cope with large-scale ontologies
• FOAM is the Framework for ontology alignment and mapping. It is based on heuristics (similarity) of the individual entities (concepts, relations, and instances)
• hMAFRA (Harmonize Mapping Framework) is a set of tools supporting semantic mapping definition and data reconciliation between ontologies. The targeted formats are XSD, RDFS and KAON
• IF-Map is an Information Flow based ontology mapping method. It is based on the theoretical grounds of logic of distributed systems and provides an automated streamlined process for generating mappings between ontologies of the same domain
• LILY is a system matching heterogeneous ontologies. LILY extracts a semantic subgraph for each entity, then it uses both linguistic and structural information in semantic subgraphs to generate initial alignments. The system is presently in a demo version only
• MAFRA Toolkit – the Ontology MApping FRAmework Toolkit allows users to create semantic relations between two (source and target) ontologies, and apply such relations in translating source ontology instances into target ontology instances
• OntoEngine is a step toward allowing agents to communicate even though they use different formal languages (i.e., different ontologies). It translates data from a “source” ontology to a “target”
• OWLS-MX is a hybrid semantic Web service matchmaker. OWLS-MX 1.0 utilizes both description logic reasoning, and token based IR similarity measures. It applies different filters to retrieve OWL-S services that are most relevant to a given query
• RiMOM (Risk Minimization based Ontology Mapping) integrates different alignment strategies: edit-distance based strategy, vector-similarity based strategy, path-similarity based strategy, background-knowledge based strategy, and three similarity-propagation based strategies
• semMF is a flexible framework for calculating semantic similarity between objects that are represented as arbitrary RDF graphs. The framework allows taxonomic and non-taxonomic concept matching techniques to be applied to selected object properties
• Snoggle is a graphical, SWRL-based ontology mapper. Snoggle attempts to solve the ontology mapping problem by providing a graphical user interface (similar to which of the Microsoft Visio) to guide the process of ontology vocabulary alignment. In Snoggle, user-defined mappings can be serialized into rules, which is expressed using SWRL
• Terminator is a tool for creating term to ontology resource mappings (documentation in Finnish).

Ontology Visualization/Analysis

Though all are not relevant, see my post from a couple of years back on large-scale RDF graph software.

• Social network graphing tools (many covered elsewhere)
• Cytoscape is a bioinformatics software platform for visualizing molecular interaction networks and integrating these interactions with gene expression profiles and other state data; I have also written specifically about Cytoscape’s use in UMBEL
  • RDFScape is a project that brings Semantic Web “features” to the popular Systems Biology software Cytoscape
  • NetworkAnalyzer performs analysis of biological networks and calculates network topology parameters including the diameter of a network, the average number of neighbors, and the number of connected pairs of nodes. It also computes the distributions of more complex network parameters such as node degrees, average clustering coefficients, topological coefficients, and shortest path lengths. It displays the results in diagrams, which can be saved as images or text files; used by SD
• Graphl is a tool for collaborative editing and visualisation of graphs, representing relationships between resources or concepts of the real world. Graphl may be thought of as a visual wiki, a place where everybody can contribute to a shared repository of knowledge
• igraph is a free software package for creating and manipulating undirected and directed graphs
• Network Workbench is a very complex, comprehensive; Swiss Army Knife
• NetworkX – Python; very clean
• Stanford Network Analysis Package (SNAP) is a general purpose network analysis and graph mining library. It is written in C++ and easily scales to massive networks with hundreds of millions of nodes
• Social Networks Visualizer (SocNetV) is a flexible and user-friendly tool for the analysis and visualization of Social Networks. It lets you construct networks (mathematical graphs) with a few clicks on a virtual canvas or load networks of various formats (GraphViz, GraphML, Adjacency, Pajek, UCINET, etc) and modify them to suit your needs. SocNetV also offers a built-in web crawler, allowing you to automatically create networks from all links found in a given initial URL
• Tulip may be incredibly strong
  • quite active (but not much online stuff): http://sourceforge.net/projects/auber/files/
• Springgraph component for Flex
• VizierFX is a Flex library for drawing network graphs. The graphs are laid out using GraphViz on the server side, then passed to VizierFX to perform the rendering. The library also provides the ability to run ActionScript code in response to events on the graph, such as mousing over a node or clicking on it.

Miscellaneous Ontology Tools

• Apolda (Automated Processing of Ontologies with Lexical Denotations for Annotation) is a plugin (processing resource) for GATE (http://gate.ac.uk/). The Apolda processing resource (PR) annotates a document like a gazetteer, but takes the terms from an (OWL) ontology rather than from a list
• DL-Learner is a tool for learning complex classes from examples and background knowledge. It extends Inductive Logic Programming to Description Logics and the Semantic Web. DL-Learner now has a flexible component based design, which allows to extend it easily with new learning algorithms, learning problems, reasoners, and supported background knowledge sources. A new type of supported knowledge sources are SPARQL endpoints, where DL-Learner can extract knowledge fragments, which enables learning classes even on large knowledge sources like DBpedia, and includes an OWL API reasoner interface and Web service interface.
• LexiLink is a tool for building, curating and managing multiple lexicons and ontologies in one enterprise-wide Web-based application. The core of the technology is based on RDF and OWL
• mopy is the Music Ontology Python library, designed to provide easy to use python bindings for ontology terms for the creation and manipulation of music ontology data. mopy can handle information from several ontologies, including the Music Ontology, full FOAF vocab, and the timeline and chord ontologies.
• OBDA (Ontology Based Data Access) is a plugin for Protégé aimed to be a full-fledged OBDA ontology and component editor. It provides data source and mapping editors, as well as querying facilities that, in sum, allow you to design and test every aspect of an OBDA system. It supports relational data sources (RDBMS) and GLAV-like mappings. In its current beta form, it requires Protege 3.3.1, a reasoner implementing the OBDA extensions to DIG 1.1 (e.g., the DIG server for QuOnto) and Jena 2.5.5
• OntoComP is a Protégé 4 plugin for completing OWL ontologies. It enables the user to check whether an OWL ontology contains “all relevant information” about the application domain, and extend the ontology appropriately if this is not the case
• Ontology Browser is a browser created as part of the CO-ODE (http://www.co-ode.org/) project; rather simple interface and use
• Ontology Metrics is a web-based tool that displays statistics about a given ontology, including the expressivity of the language it is written in
• OntoSpec is a SWI-Prolog module, aiming at automatically generating XHTML specification from RDF-Schema or OWL ontologies
• OWL API is a Java interface and implementation for the W3C Web Ontology Language (OWL), used to represent Semantic Web ontologies. The API is focused towards OWL Lite and OWL DL and offers an interface to inference engines and validation functionality
• OWL Module Extractor is a Web service that extracts a module for a given set of terms from an ontology. It is based on an implementation of locality-based modules that is part of the OWL API.
• OWL Syntax Converter is an online tool for converting ontologies between different formats, including several OWL syntaxes, RDF/XML, KRSS
• OWL Verbalizer is an on-line tool that verbalizes OWL ontologies in (controlled) English
• OwlSight is an OWL ontology browser that runs in any modern web browser; it’s developed with Google Web Toolkit and uses Gwt-Ext, as well as OWL-API. OwlSight is the client component and uses Pellet as its OWL reasoner
• Pellint is an open source lint tool for Pellet which flags and (optionally) repairs modeling constructs that are known to cause performance problems. Pellint recognizes several patterns at both the axiom and ontology level.
• PROMPT is a tab plug-in for Protégé is for managing multiple ontologies by comparing versions of the same ontology, moving frames between included and including project, merging two ontologies into one, or extracting a part of an ontology.
• SegmentationApp is a Java application that segments a given ontology according to the approach described in “Web Ontology Segmentation: Analysis, Classification and Use” (http://www.co-ode.org/resources/papers/seidenberg-www2006.pdf)
• SETH is a software effort to deeply integrate Python with Web Ontology Language (OWL-DL dialect). The idea is to import ontologies directly into the programming context so that its classes are usable alongside standard Python classes
• SKOS2GenTax is an online tool that converts hierarchical classifications available in the W3C SKOS (Simple Knowledge Organization Systems) format into RDF-S or OWL ontologies
• SpecGen (v5) is an ontology specification generator tool. It’s written in Python using Redland RDF library and licensed under the MIT license
• Text2Onto is a framework for ontology learning from textual resources that extends and re-engineers an earlier framework developed by the same group (TextToOnto). Text2Onto offers three main features: it represents the learned knowledge at a metalevel by instantiating the modelling primitives of a Probabilistic Ontology Model (POM), thus remaining independent from a specific target language while allowing the translation of the instantiated primitives
• Thea is a Prolog library for generating and manipulating OWL (Web Ontology Language) content. Thea OWL parser uses SWI-Prolog’s Semantic Web library for parsing RDF/XML serialisations of OWL documents into RDF triples and then it builds a representation of the OWL ontology
• TONES Ontology Repository is primarily designed to be a central location for ontologies that might be of use to tools developers for testing purposes; it is part of the TONES project
• Visual Ontology Manager (VOM) is a family of tools enables UML-based visual construction of component-based ontologies for use in collaborative applications and interoperability solutions.
• Web Ontology Manager is a lightweight, Web-based tool using J2EE for managing ontologies expressed in Web Ontology Language (OWL). It enables developers to browse or search the ontologies registered with the system by class or property names. In addition, they can submit a new ontology file
• RDF evoc (external vocabulary importer) is an RDF external vocabulary importer module (evoc) for Drupal caches any external RDF vocabulary and provides properties to be mapped to CCK fields, node title and body. This module requires the RDF and the SPARQL modules.

Not Apparently in Active Use

• Almo is an ontology-based workflow engine in Java supporting the ARTEMIS project; part of the OntoWare initiative
• ClassAKT is a text classification web service for classifying documents according to the ACM Computing Classification System
• Elmo provides a simple API to access ontology oriented data inside a Sesame RDF repository. The domain model is simplified into independent concerns that are composed together for multi-dimensional, inter-operating, or integrated applications
• ExtrAKT is a tool for extracting ontologies from Prolog knowledge bases.
• F-Life is a tool for analysing and maintaining life-cycle patterns in ontology development.
• Foxtrot is a recommender system which represents user profiles in ontological terms, allowing inference, bootstrapping and profile visualization.
• HyperDAML creates an HTML representation of OWL content to enable hyperlinking to specific objects, properties, etc.
• LinKFactory is an ontology management tool, it provides an effective and user-friendly way to create, maintain and extend extensive multilingual terminology systems and ontologies (English, Spanish, French, etc.). It is designed to build, manage and maintain large, complex, language independent ontologies.
• LSW – the Lisp semantic Web toolkit enables OWL ontologies to be visualized. It was written by Alan Ruttenberg
• Ontodella is a Prolog HTTP server for category projection and semantic linking
• OntoWeaver is an ontology-based approach to Web sites, which provides high level support for web site design and development
• OWLLib is a PHP library for accessing OWL files. OWL is w3.org standard for storing semantic information
• pOWL is a Semantic Web development platform for ontologies in PHP. pOWL consists of a number of components, including RAP
• ROWL is the Rule Extension of OWL; it is from the Mobile Commerce Lab in the School of Computer Science at Carnegie Mellon University
• Semantic Net Generator is a utlity for generating Topic Maps automatically from different data sources by using rules definitions specified with Jelly XML syntax. This Java library provides Jelly tags to access and modify data sources (also RDF) to create a semantic network
• SMORE is OWL markup for HTML pages. SMORE integrates the SWOOP ontology browser, providing a clear and consistent way to find and view Classes and Properties, complete with search functionality
• SOBOLEO is a system for Web-based collaboration to create SKOS taxonomies and ontologies and to annotate various Web resources using them
• SOFA is a Java API for modeling ontologies and Knowledge Bases in ontology and Semantic Web applications. It provides a simple, abstract and language neutral ontology object model, inferencing mechanism and representation of the model with OWL, DAML+OIL and RDFS languages; from java.dev
• WebScripter is a tool that enables ordinary users to easily and quickly assemble reports extracting and fusing information from multiple, heterogeneous DAMLized Web sources.

Minor Updates Provided to these Standard AI3 Datasets

If you are like me, you like to clear the decks before the start of major new projects. In Structured Dynamics‘ case, we actually have multiple new initiatives getting underway, so the deck clearing has been especially focused this time.

As a result, we have updated Sweet Tools, AI3’s listing of semantic Web and -related tools, with the addition of some 30 new tools, updates to others, and deletions of five expired entries. The dataset now lists 835 tools. And, as before, there is also now a new structured data view via conStruct (pick the Sweet Tools dataset).

We have also updated SWEETpedia, a listing of 246 research articles that use Wikipedia in one way or another to do semantic-Web related research. Some 20 new papers were added to this update.

Please use the comments section on this post to suggest new tools or new research articles for inclusion in future updates.

SD Selected to Proceed with Formal Proposal

Structured Dynamics and its Citizen DAN project has been selected as one of the finalists to proceed with a formal proposal for the 2010 $5 million Knight News Challenge. The proposal extends SD’s basic structWSF and conStruct Drupal frameworks to provide a data appliance and network (DAN) to support citizen journalists with data and analysis at the local, community level.

Thanks to all of you who submitted votes in support of the earlier draft proposal. The News Challenge received 2,489 proposals for the 2010 contest, according to Jose Zamora, journalism program associate at the Knight Foundation. According to the Nieman Journalism Lab, Zamora said 65 percent of proposals came through the closed category and 35 percent were open.

The next-round full proposals are due by January 31. Eventual winners are slated to be announced around mid-June 2010.

Sweet Tools Expands by 13% to 810 Tools; Gets Major Structured Data Update

Sweet Tools, AI3’s listing of semantic Web and -related tools, now has a total of 810 tools listed, a significant expansion from the last update. With the retirement of 19 prior tools, this new listing represents an increase of 93 tools, or 13%, from the previous version that listed 736.

The Sweet Tools dataset is also now showing the way to a couple of exciting innovations:  new generic ontology-driven applications for structured data; and, tools for authoring structured data via spreadsheets.

Summary of Major Changes

So, here is the summary of major changes in this new listing:

• A completely new structured data view of the listing, courtesy of Structured Dynamics‘ structWSF and conStruct open source frameworks. This version can be viewed on the conStruct SCS Web site (pick the Sweet Tools dataset). You can compare this server-side presentation and version to the client-side JavaScript version using Exhibit that has been part of this blog for some time
• A new structural organization of the tools into an ontology that relates portions of the ACM classification and  UMBEL to the tools categories. This provides richer retrievals and inspections on the conStruct version (the Exhibit version remains fairly “flat” in structure)
• In light of the above, refined tools classifications, and, of course,
• The increase in coverage to 810 tools.

To see the major Sweet Tools page for this updated listing in its existing format, filter on ‘New’ under New or Existing? to see the recent additions. Alternatively, you can also see this same filtering using the conStruct structured data view by searching on the Status attribute using the value ‘New’; see example here.

Structured Data via conStruct

Though still formative, the most exciting change with the Sweet Tools listing is this new presentation via conStruct. It is a structured data Web services framework with a UI, all offered as a set of modules to Drupal. To kick the tires with this new system, you may want to look at:

• Browsing the ontology tree (then, Browse by Kind)
• Viewing an instance record
• Viewing a Class Type Report
• Viewing an Attribute Report
• Searching by facet (check the tabs)
• Doing a multi-value filtering (make selections from the various tabs)
• Exporting stuff in a variety of formats.

BTW, there are some helpful documentation pages that show how all of these various tools work and more, such as, for example, Browse. (Also, BTW, as a demo user, you also are not seeing all of the write and update tools, either; again, see the documentation.)

The essential underlying basis to conStruct is the structWSF Web services framework. There are still some aspects to this system that we feel are incomplete and we are working on.  Some of these things include dropdown selections (controlled vocabulary selects); easier template creation; and intuitive template re-use. Nonetheless, these additions will come quickly, and what is here is already a great demonstration of how structured data can drive generic tools and interfaces.

The case study of how this system was constructed from a spreadsheet input using the irON vocabulary is described in an earlier post.

Updated Statistics

The updated Sweet Tools listing now includes nearly 50 different tools categories. The most prevalent categories are browser tools (RDF, OWL), information extraction, parsers or converters, composite application frameworks and general ontology tools. Each accounts for more than 8% — or more than 50 tools — of the total. This breakdown is as follows (click to expand):

There are no real discernable trends in application tool categories over the past couple of years.

As for the languages these applications are written in, that has stayed pretty steady, too. Java is still the leading language at about 46%, which has been very slightly trending downward over the past three years or so. PHP has increased a bit as well. The current splits are (click to expand):

Prior Updates

Background on prior listings and earlier statistics may be found on these previous posts:

• Sweet Tools Updated to 736 Tools (February 2, 2009)
• Sweet Tools Listing Now Exceeds 700 Tools (July 5, 2008)
• Sweet Tools Updated, Opened for Collaboration (Mar. 31, 2008)
• Sweet Tools Updated to 650 Tools (Nov. 18, 2007)
• New Release: 578 Semantic Web and -related Tools (Sept. 16, 2007)
• 542 Semantic Web and -related Tools (Jun. 19, 2007)
• Listing of 500 Semantic Web and Related Tools (Mar. 11, 2007)
• Sweet Tools Updated to 420 Tools (Feb. 7, 2007)
• Converting ‘Sweet Tools’ to an Exhibit (Jan. 22, 2007)
• Permanent Sweet Tools Listing — 400+ Tools and Counting! (Jan. 5, 2007)
• Comprehensive Listing of 250 Semantic Web Tools (updated) (Oct. 4, 2006)
• Comprehensive Listing of 175 Semantic Web Tools (Sep. 22, 2006)
• Current Listing of 70 Semantic Web Tools (Aug. 12, 2006)

With interim updates periodically over that period.

We Invite You to Comment on this Knight News Challenge Submission

Structured Dynamics is one of the 320+ plus submitters (and counting!) to the 2010 $5 million Knight News Challenge. Our proposal is to extend our basic structWSF and conStruct Drupal frameworks to provide a data appliance and network (DAN) to support citizen journalists with data and analysis at the local, community level.

We invite you to look at our application and to provide comments or your rating of the application. The deadline for comments is tomorrow, and we will incorporate any appropriate last-minute suggestions. You can find our submission at:

Citizen DAN Proposal (or, search on ‘citizen dan’)

Please note: you must be signed in via a short submission to vote or comment on the application (or others shown in the listings).

The Citizen DAN Idea

Citizen DAN is meant to be a complete, open source framework for promoting citizen journalism. It is a:

• Appliance for filtering and analyzing data specific to local community indicators
• Meeting place for the public to upload and share local data and information
• Web data portal that can be individually tailored by any local community, and
• Node in a global network of communities across which to compare measures of community well-being.

Good decisions and good journalism require good starting information. Citizen DAN is a framework to provide access for any citizen to learn and compare local statistics and data with other similar communities. This helps to promote the grist for citizen journalism, as well as to provide a vehicle for discovery and learning across the community.

Citizen DAN will come pre-packaged with all necessary deployment components and documentation, including local data from government sources. It will include facilities for direct upload of additional local data in formats from spreadsheets to standard databases; many standard converters are included with the basic package.

Citizen DAN may be implemented either by local governments or by community advocacy groups. When deployed, using its clear documentation, sponsors may choose whether or what portions of local data are exposed to the broader Citizen DAN network. Data exposed on the network is automatically available to any other network community for comparison and analysis purposes.

(You may want to see our separate description — structWSF: A Framework for Collaboration Networks — of how this framework can lead to collaboration through widely distributed community nodes.)

The complete data appliance and network (DAN) is multi-lingual. If funded, this project will be tested and deployed in at least two prominent cities; one in Canada (French and English), and one in the United States (English and Spanish).

We think Citizen DAN is an exciting new prospect for local communities to share and use local data. Your support can help make this app available to any community for free.

And, in any case, do check out the other fine submissions to the challenge.

A Case Study of Turning Spreadsheets into Structured Data Powerhouses

In a former life, I had the nickname of ‘Spreadsheet King’ (perhaps among others that I did not care to hear). I had gotten the nick because of my aggressive use of spreadsheets for financial models, competitors tracking, time series analyses, and the like. However, in all honesty, I have encountered many others in my career much more knowledgeable and capable with spreadsheets than I’ll ever be. So, maybe I was really more like a minor duke or a court jester than true nobility.

Yet, pro or amateur, there are perhaps 1 billion spreadsheet users worldwide [1], making spreadsheets undoubtedly the most prevalent data authoring environment in existence. And, despite moans and wails about how spreadsheets can lead to chaos, spaghetti code, or violations of internal standards, they are here to stay.

Spreadsheets often begin as simple notetaking environments. With the addition of new findings and more analysis, some of these worksheets may evolve to become full-blown datasets. Alternatively, some spreadsheets start from Day One as intended datasets or modeling environments. Whatever the case, clearly there is much accumulated information and data value “locked up” in existing spreadsheets.

How to “unlock” this value for sharing and collaboration was a major stimulus to development of the commON serialization of irON (instance record and Object Notation) [2]. I recently published a case study [3] that describes the reasons and benefits of dataset authoring in a spreadsheet, and provides working examples and code based on Sweet Tools [4] to aid users in understanding and using the commON notation. I summarize portions of that study herein.

Background on Sweet Tools and irON

The dataset that is the focus of this use case, Sweet Tools, began as an informal tracking spreadsheet about four years ago. I began it as a way to learn about available tools in the semantic Web and -related spaces. I began publishing it and others found it of value so I continued to develop it.

As it grew over time, however, it gained in structure and size. Eventually, it became a reference dataset, with which many other people desired to use and interact. The current version has well over 800 tools listed, characterized by many structured data attributes such as type, programming language, description and so forth. As it has grown, a formal controlled vocabulary has also evolved to bring consistency to the characterization of many of these attributes.

It was natural for me to maintain this listing as a spreadsheet, which was also reinforced when I was one of the first to adopt an Exhibit presentation of the data based on a Google spreadsheet about three years back. Here is a partial view of this spreadsheet as I maintain it locally:

(click to expand)

When we began to develop irON in earnest as a simple (”naïve”) dataset authoring framework, it was clear that a comma-separated value, or CSV [5], option should join the other two serializations under consideration, XML and JSON. CSV, though less expressive and capable as a data format than the other serializations, still has an attribute-value pair (also known as key-value pairs and many other variants [6]) orientation. And, via spreadsheets, datasets can be easily authored and inspected, while also providing a rich functional environment including sorting, formatting, data validation, calculations, macros, etc.

As a dataset very familiar to us as irON’s editors, and directly relevant to the semantic Web, Sweet Tools provided a perfect prototype case study for helping to guide the development of irON, and specifically what came to be known as the commON serialization for irON. The Sweet Tools dataset is relatively large for a speciality source, has many different types and attributes, and is characterized by text, images, URLs and similar.

The premise was that if Sweet Tools could be specified and represented in commON sufficiently to be parsed and converted to interoperable RDF, then many similar instance-oriented datasets could likely be so as well. Thus, as we tried and refined notation and vocabulary, we tested applicability against the CSV representation of Sweet Tools in addition to other CSV, JSON and XML datasets.

Dataset Authoring in a Spreadsheet

A large portion of the case study describes the many advantages of authoring small datasets within spreadsheets. The useful thing about the CSV format is that these full functional capabilities of the spreadsheet are available during authoring or later updates and modifications, but, when exported, the CSV provides a relatively clean format for processing and parsing.

So, some of the reasons for small dataset authoring in a spreadsheet include:

• Formatting and on-sheet management -  the first usefulness of a spreadsheet comes from being able to format and organize the records. Records can be given background colors to highlight distinctions (new entries, for example); live URL links can be embedded; contents can be wrapped and styled within cells; and the column and row heads can be “frozen”, useful when scrolling large workspaces
• Named blocks and sorting – named blocks are a powerful feature of modern spreadsheets, useful for data manipulation, printing and internal referencing by formulas and the like.  Sorting with named blocks is especially important as an aid to check consistency of terminology, records completeness, duplicates checks, missing value checks, and the like. Named blocks can also be used as references in calculations. All of these features are real time savers, especially when datasets grow large and consistency of treatment and terminology is important
• Multiple sheets and consolidated access – commON modules can be specified on a single worksheet or multiple worksheets and saved as individual CSV files; because of its size and relative complexity, the Sweet Tools dataset is maintained on multiple sheets. Multi-worksheet environments help keep related data and notes consolidated and more easily managed on local hard drives
• Completeness and counts - the spreadsheet counta function is useful to sum counts for cell entries by both column and row, a useful aid to indicate if an attribute or type value is missing or if a record is incomplete.  Of course, similar helps and uses can be found for many of the hundreds of embedded functions within a spreadsheet
• Controlled vocabularies and data entry validation – quality datasets often hinge on consistency and uniform values and terminology; the data validation utilities within spreadsheets can be applied to Boolean, ranges and mins and maxes, and to controlled vocabulary lists. Here is an example for Sweet Tools, enforcing proper tool category assignments from a 50-item pick list:

• Specialized functions and macros – all functionality of spreadsheets may be employed in the development of commON datasets. Then, once employed, only the values embedded within the sheets are then exported as CSV.

Staging Sweet Tools for commON

The next major section of the case study deals with the minor conventions that must be followed in order to stage spreadsheets for commON. Not much of the specific commON vocabulary or notation is discussed below; for details, see [7].

Because you can create multiple worksheets within a spreadsheet, it is not necessary to modifiy existing worksheets or tabs. Rather, if you are reluctant or can not change existing information, merely create parallel duplicate sheets of the source information. These duplicate sheets have as their sole purpose export to commON CSV. You can maintain your spreadsheet as is while staging for commON.

To do so, use the simple = formula to create cross-references between the existing source spreadsheet tab and the target commON CSV export tab. (You can also do this for complete, highlighted blocks from source to target sheet.) Then, by adding the few minor conventions of commON, you have now created a staged export tab without modifying your source information in the slightest.

In standard form and for Excel and Open Office, single quotes, double quotes and commas when entered into a spreadsheet cell are automatically ‘escaped‘ when issued as CSV. commON allows you to specify your own delimiter for lists (the standard is the pipe ‘|’ character) and what the parser recognizes as the ‘escape’ character (’\’ is the standard). However, you probably should not change for most conditions.

The standard commON parsers and converters are UTF-8 compatible. If your source content has unusual encodings, try to target UTF-8 as your canonical spreadsheet output.

In the irON specification there are a small number of defined modules or processing sections. In commON, these modules are denoted by the double-ampersand character sequence (’&&‘), and apply to lists of instance records (&&recordList), dataset specifications and associated metadata describing the dataset (&&dataset), and mappings of attributes and types to existing schema (&&linkage). Similarly, attributes and types are denoted by a single ampersand prefix (&attributeName).

In commON, any or all of the modules can occur within a single CSV file or in multiple files. In any case, the start of one of these processing modules is signaled by the module keyword and &&keyword convention.

The RecordList Module

The first spreadsheet figure above shows a Sweet Tools example for the &&recordList module. The module begins with that keyword, indicating one of more instance records will follow. Note that the first line after the &&recordList keyword is devoted to the listing of attributes and types for the instance records (designated by the &attributeName convention in the columns for the first row after the &&recordList keyword is encountered).

The &&recordList format can also include the stacked style (see similar Dataset example below) in addition to the single row style shown above.

At any rate, once a worksheet is ready with its instance records following the straightforward irON and commON conventions, it can then be saved as a CSV file and appropriately named. Here is an example of what this “vanilla” CSV file now looks like when shown again in a spreadsheet:

(click to expand)

Alternatively, you could open this same file in a text editor. Here is how this exact same instance record view looks in an editor:

(click to expand)

Note that the CSV format separates each column by the comma separator, with escapes shown for the &description attribute when it includes a comma-separated clause. Without word wrap, each record in this format occupies a single row (though, again, for the stacked style, multiple entries are allowed on individual rows so long as a new instance record &id is not encountered in the first column).

The Dataset Module

The &&dataset module defines the dataset parameters and provides very flexible metadata attributes to describe the dataset [8]. Note the dataset specification is exactly equivalent in form to the instance record (&&recordList) format, and also allows the single row or stacked styles (see these instance record examples), with this one being the stacked style:

(click to expand)

The Linkage Module

The &&linkage module is used to map the structure of the instance records to some structural schema, which can also include external ontologies. The module has a simple, but specific structure.

Either attributes (presented as the &attributeList) or types (presented as the &typeList) are listed sequentially by row until the listing is exhausted [8]. By convention, the second column in the listing is the targeted &mapTo value. Absent a prior &prefixList value, the &mapTo value needs to be a full URL to the corresponding attribute or type in some external schema:

Notice in the case of Sweet Tools that most values are from the actual COSMO mini-ontology underlying the listing. These need to be listed as well, since absent the specifications in commON the system has NO knowledge of linkages and mappings.

The Schema (structure) Module

In its current state of development, commON does not support a spreadsheet-based means for specifying the schema structure (lightweight ontology) governing the datasets [2]. Another irON serialization, irJSON, does. Either via this irJSON specification or via an offline ontology, a link reference is presently used by commON (and, therefore, Sweet Tools for this case study) to establish the governing structure of the input instance record datasets.

A spreadsheet-based schema structure for commON has been designed and tested in prototype form. commON should be enhanced with this capability in the near future [8].

Saving and Importing

If the modules are spread across more than one worksheet, then each worksheet must be saved as its own CSV file. In the case of Sweet Tools, as exhibited by its reference current spreadsheet, sweet_tools_20091110.xls, three individual CSV files get saved. These files can be named whatever you would like. However, it is essential that the names be remembered for later referencing.

My own naming convention is to use a format of appname_date_modulename.csv because it sorts well in a file manager accommodating multiple versions (dates) and keeps related files clustered. The appname in the case of Sweet Tools is generally swt. The modulename is generally the dataset, records, or linkage convention. I tend to use the date specification in the YYYYMMDD format. Thus, in the case of the records listings for Sweet Tools, its filename could be something like:  swt_20091110_records.csv.

Once saved, these files are now ready to be imported into a structWSF [9] instance, which is where the CSV parsing and conversion to interoperable RDF occurs [8]. In this case study, we used the Drupal-based conStruct SCS system [10]. conStruct exposes the structWSF Web services via a user interface and a user permission and access system. The actual case study write-up offers more details about the import process.

Using the Dataset

We are now ready to interact with the Sweet Tools structured dataset using conStruct (assuming you have a Drupal installation with the conStruct modules) [10].

Introduction to the App

The screen capture below shows a couple of aspects of the system:

• First, the left hand panel (according to how this specific Drupal install was themed) shows the various tools available to conStruct.  These include (with links to their documentation) Search, Browse, View Record, Import, Export, Datasets, Create Record, Update Record, Delete Record and Settings [11];
• The Browse tree in the main part of the screen shows the full mini-ontology that classifies Sweet Tools. Via simple inferencing, clicking on any parent link displays all children projects for that category as well (click to expand):

(click to expand)

One of the absolutely cool things about this framework is that all tools, inferencing, user interfaces and data structure are a direct result of the ontology(ies) underlying the system (plus the irON instance ontology, as well). This means that switching datasets or adding datasets causes the entire system structure to now reflect those changes — without lifting a finger!!

Some Sample Uses

Here are a few sample things you can do with these generic tools driven by the Sweet Tools dataset:

• Browsing the ontology tree (then, Browse by Kind)
• Viewing an instance record
• Viewing a Class Type Report
• Viewing an Attribute Report
• Searching by facet (check the tabs)
• Doing a multi-value filtering (make selections from the various tabs),
• Exporting stuff in a variety of formats.

Note, if you access this conStruct instance you will do so as a demo user. Unfortunately, as such, you may not be able to see all of the write and update tools, which in this case are reserved for curators or admins. Recall that structWSF has a comprehensive user access and permissions layer.

Exporting in Alternative Formats

Of course, one of the real advantages of the irON and structWSF designs is to enable different formats to be interchanged and to interoperate. Upon submission, the commON format and its datasets can then be exported in these alternate formats and serializations [8]:

• commON
• irJSON
• irXML
• N-Triples/CSV
• N-Triples/TSV
• RDF+N3
• RDF+XML

As should be obvious, one of the real benefits of the irON notation — in addition to easy dataset authoring — is the ability to more-or-less treat RDF, CSV, XML and JSON as interoperable data formats.

The Formal Case Study

The formal Sweet Tools case study based on commON, with sample download files and PDF, is available from Annex: A commON Case Study using Sweet Tools, Supplementary Documentation [3].

[3] Michael Bergman, 2009. Annex: A commON Case Study using Sweet Tools, Supplementary Documentation, prepared by Structured Dynamics LLC, November 10, 2009. See http://openstructs.org/iron/common-swt-annex. It may also be downloaded in PDF .

New Release Expands to 810 Tools; Gets Major Structured Data Update

It has been eight months since the last major update to Sweet Tools, AI3’s listing of semantic Web and -related tools. With today’s release, there are now a total of 810 tools listed, crashing through the sound barrier of 761 tools. With the retirement of 19 prior tools, this new listing represents an increase of 93 tools, or 13%, from the previous version that listed 736.

But simply adding to the tools listing is not the cause of this longer than normal period between updates.

This little Sweet Tools dataset is now showing the way to a couple of exciting innovations:  new generic ontology-driven applications for structured data; and, tools for authoring structured data via spreadsheets.

We deal with the former in this post. I will deal with the spreadsheet business in a subsequent post.

Summary of Major Changes

So, here is the summary of major changes in this new listing:

• A completely new structured data view of the listing, courtesy of Structured Dynamics‘ structWSF and conStruct open source frameworks. This version can be viewed on the conStruct SCS Web site (pick the Sweet Tools dataset). You can compare this server-side presentation and version to the client-side JavaScript version using Exhibit that has been part of this blog for some time
• A new structural organization of the tools into an ontology that relates portions of the ACM classification and  UMBEL to the tools categories. This provides richer retrievals and inspections on the conStruct version (the Exhibit version remains fairly “flat” in structure)
• In light of the above, refined tools classifications, and, of course,
• The increase in coverage to 810 tools.

To see the major Sweet Tools page for this updated listing in its existing format, filter on ‘New’ under New or Existing? to see the recent additions. Alternatively, you can also see this same filtering using the conStruct structured data view by searching on the Status attribute using the value ‘New’; see example here.

Structured Data via conStruct

Though still formative, the most exciting change with the Sweet Tools listing is this new presentation via conStruct. It is a structured data Web services framework with a UI, all offered as a set of modules to Drupal. To kick the tires with this new system, you may want to look at:

• Browsing the ontology tree (then, Browse by Kind)
• Viewing an instance record
• Viewing a Class Type Report
• Viewing an Attribute Report
• Searching by facet (check the tabs)
• Doing a multi-value filtering (make selections from the various tabs)
• Exporting stuff in a variety of formats.

BTW, there are some helpful documentation pages that show how all of these various tools work and more, such as, for example, Browse. (Also, BTW, as a demo user, you also are not seeing all of the write and update tools, either; again, see the documentation.)

The essential underlying basis to conStruct is the structWSF Web services framework. There are still some aspects to this system that we feel are incomplete and we are working on.  Some of these things include dropdown selections (controlled vocabulary selects); easier template creation; and intuitive template re-use. Nonetheless, these additions will come quickly, and what is here is already a great demonstration of how structured data can drive generic tools and interfaces.

As I said:  More on this in a later post.

Updated Statistics

The updated Sweet Tools listing now includes nearly 50 different tools categories. The most prevalent categories are browser tools (RDF, OWL), information extraction, parsers or converters, composite application frameworks and general ontology tools. Each accounts for more than 8% — or more than 50 tools — of the total. This breakdown is as follows (click to expand):

There are no real discernable trends in application tool categories over the past couple of years.

As for the languages these applications are written in, that has stayed pretty steady, too. Java is still the leading language at about 46%, which has been very slightly trending downward over the past three years or so. PHP has increased a bit as well. The current splits are (click to expand):

Prior Updates

Background on prior listings and earlier statistics may be found on these previous posts:

• Sweet Tools Updated to 736 Tools (February 2, 2009)
• Sweet Tools Listing Now Exceeds 700 Tools (July 5, 2008)
• Sweet Tools Updated, Opened for Collaboration (Mar. 31, 2008)
• Sweet Tools Updated to 650 Tools (Nov. 18, 2007)
• New Release: 578 Semantic Web and -related Tools (Sept. 16, 2007)
• 542 Semantic Web and -related Tools (Jun. 19, 2007)
• Listing of 500 Semantic Web and Related Tools (Mar. 11, 2007)
• Sweet Tools Updated to 420 Tools (Feb. 7, 2007)
• Converting ‘Sweet Tools’ to an Exhibit (Jan. 22, 2007)
• Permanent Sweet Tools Listing — 400+ Tools and Counting! (Jan. 5, 2007)
• Comprehensive Listing of 250 Semantic Web Tools (updated) (Oct. 4, 2006)
• Comprehensive Listing of 175 Semantic Web Tools (Sep. 22, 2006)
• Current Listing of 70 Semantic Web Tools (Aug. 12, 2006)

With interim updates periodically over that period.

Note: Because of comments expirations on prior posts, this entry is now the new location for adding a suggested new tool. Simply provide your information in the comments section, and your tool will be included in the next update.