Community & Business Groups

Argumentation Community Group

The Argumentation Community Group will facilitate and promote the use of the Web for all forms of argumentation. The group will discuss and design both argumentation representation formats and systems.

Note: Community Groups are proposed and run by the community. Although W3C hosts these conversations, the groups do not necessarily represent the views of the W3C Membership or staff.

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Commerce Technology, Economics and Fuel Taxation

Executive Summary

Presented are topics pertaining to economics policies, to the granular taxation of fuel purchases, policies possible with modern computing technology.

Related Standards Activities

Web Payments Interest Group, Web Payments Community Group


New policy opportunities include that the United States, states and municipalities could enact tax discounts or tax exemptions for fuel purchases pertaining to or based upon the categories of shipments or the categories of cargos; for example, fuel tax exemptions are possible for the transportation of foods or of construction materials.

Federal, state and municipal laws could facilitate discounting or exempting the taxes upon fuel purchases categorized by the cargos aboard the vehicles at the times of fuel purchases; implementations of such policy ideas are recently possible by means of interoperating information technologies across sectors.

Educational Technology, Educational Data Mining and Student Data Privacy

Executive Summary

Educational technology can be delivered, utilizing peer-to-peer (P2P) database computing, P2P statistical computing, providing educational data mining (EDM) features while providing students storage of their data on their mobile computers, facilitating data privacy.

Educational technology can interoperate with software at schools’ libraries, public schools or universities, providing transparent configurability to local school districts or local schoolboards. Public school libraries’ software can transparently, configurably, provide semantic content services from a large number of materials, including digital textbooks, services utilized by dialogue systems, spoken dialogue systems or digital characters (e.g. Digital Ira) while interacting with students in scenarios including uses of featureful, interoperable, digital textbooks selected by state or local schoolboards.

Related Standards Activities

IDPF (EPUB), IMS Global (EDUPUB), Digital Publishing Activity, Math Working Group, Multimodal Interaction Activity, Provenance Working Group (Provenance Page at the Semantic Web Wiki), Semantic Web Activity, Speech API Community Group, Voice Browser Working Group


Topical to peer-to-peer (P2P) educational technology systems is that each student’s data, each student’s models, can be stored on their mobile computer while the providing features of educational data mining (EDM). The local storage of student data, of student models, is possible while simultaneously providing EDM features, technological advantages, to students, teachers, administrators and school systems. Numerous architectural models are possible with the premises of: (1) student data privacy, students’ data, students’ models, on students’ mobile computers, (2) interoperability with school library technologies. An architectural concept is described with students’ mobile computers as natural language user interfaces, dialogue systems, spoken dialogue systems or digital characters, to semantic content services provided by schools’ libraries.

With familiarity of users’ contexts, users’ tasks, users’ documents, users’ books, users’ textbooks, software can better perform natural language understanding, semantically interpreting the instantaneous utterances of users. Similarly, software can better perform natural language generation, articulating language to users more featurefully utilizing such data. Similarly, software can better perform handwriting recognition, including of diagrams as well as of scientific or mathematical notations. Systems processing sensor data, e.g. students’ prosody, nonverbal cues or affect, can process such sensor data or derived data locally on students’ mobile computers. Data from multiple software applications, users’ contexts, users’ tasks, users’ documents, users’ books, users’ textbooks, as well as event streams from multiple applications can be processed on users’ machines, complex event processing, data stream mining, derived data stored on users’ machines to provide modeling-related features to multiple software applications on users’ machines. The view presented is that users’ data is reasonably stored on users’ computers.

Interestingly, components on students’ mobile computers, in addition to teachers’ software, can utilize real-time data, including EDM data, from distributed P2P processing. Machine learning heuristics, for example, can be enhanced by harnessing classroom P2P computing, obtaining course-specific contextual data. Students’ mobile computers can, as per P2P computing, process EDM data for students, teachers, administrators or school systems, while students’ data, including increasingly sophisticated student models, are stored on students’ devices. Pertinent to student data privacy topics, both the present as well as the futures of student modeling are important to consider; increasingly advanced modeling components accumulating data, through at least K-12, from uses of software applications, Web browsing, digital textbook uses, per course, through years of students’ learning.

We can envision library system technologies which stream semantic content for dialogue systems, spoken dialogue systems or digital characters, natural language generation, components on mobile computers. Library system technologies can also predict, prepare, content that students’ might request. Students would tend to be simultaneously exploring topics pertaining to or related to topics in digital textbooks or curriculum. School library systems can utilize data from materials including the textbooks, syllabi and websites of students’ courses.

We can envision content on the sides of students’ screens, hyperlinks to materials or into digital textbooks, concurrent to digital tutors’ utterances while digital tutors, dialogue systems, spoken dialogue systems or digital characters, present topics to students or answer students’ questions. Such content could include the provenance of the semantic content processed into utterances by dialogue systems, spoken dialogue systems or digital characters. Semantic data, streamed from school libraries, articulated into natural language utilizing students’ data on students’ mobile computers, could have provenances including from the processed content of numerous resources and materials.

In addition to the provenances of semantic content, from resources and materials at school libraries or websites processed by schools’ library systems, the content on the sides of students’ screens, concurrent to the utterances of dialogue systems, spoken dialogues or digital characters, could include hyperlinks to recommended content which relates to the topics uttered.

To estimate when students’ mobile computing hardware will be ready to compute advanced dialogue systems, concurrent to students’ tasks, scientists can utilize data including graphics card manufacturers’ roadmaps, where such projections include advancements including stacked DRAM. Technical topics include mobile computing batteries, graphics chips’ processing throughputs, graphics chips’ electrical efficiencies as well as the number of such chips in mobile computers. At CES 2014, Digital Ira was presented on a mobile device.

Advantages of EDM are possible for students, teachers, administrators and school systems without any storage, collection, harvesting, processing, productization or utilization of students’ data by third parties. Advantages of EDM are possible for students, teachers, administrators and school systems while students’ data is stored on students’ mobile computers. Advancements to databases ( and to digital textbooks ( were indicated.

Distributed computation, decentralized computation, P2P computation, utilizing students’ mobile computers, including in public school settings, increases the number of EDM features which can be provided to students, teachers, administrators or school systems, while students’ data, including increasingly sophisticated student models, can be stored locally on their mobile computers, protecting students’ data privacy. The storage of students’ various data, including student models, other than upon their mobile computers is not necessary to provide students, teachers, administrators or schools systems with EDM features.

Library technologies can provide semantic content services, knowledgebase services, transparently configurable by school districts, interoperating with dialogue systems, spoken dialogue systems or digital characters, digital tutoring technologies, on students’ mobile computers.


[1] Adomavicius, Gediminas, and Alexander Tuzhilin. “Context-aware recommender systems.” In Recommender systems handbook, pp. 217-253. Springer US, 2011.

[2] Cugola, Gianpaolo, and Alessandro Margara. “Processing flows of information: From data stream to complex event processing.” ACM Computing Surveys (CSUR) 44, no. 3 (2012): 15.

[3] Cugola, Gianpaolo, and Alessandro Margara. “Low latency complex event processing on parallel hardware.” Journal of Parallel and Distributed Computing 72, no. 2 (2012): 205-218.

[4] Koychev, Ivan, and Ingo Schwab. “Adaptation to drifting user’s interests.” InProceedings of ECML2000 Workshop: Machine Learning in New Information Age, pp. 39-46. 2000.

[5] Kuflik, Vania Dimitrova Tsvi, David Chin Francesco Ricci, and Peter Dolog Geert-Jan Houben. “User Modeling, Adaptation, and Personalization.”

[6] Luckham, David C., and Brian Frasca. “Complex event processing in distributed systems.” Computer Systems Laboratory Technical Report CSL-TR-98-754. Stanford University, Stanford 28 (1998).

[7] Özsu, M. Tamer, and Patrick Valduriez. Principles of distributed database systems. Springer, 2011.

[8] Patel Kuldip, L., M. Savalia Jay, and Sreevalsan-Nair Jaya. “Parallelization of Complex Event Processing on GPU.” HiPC in (2011).

[9] Resnick, Paul, and Hal R. Varian. “Recommender systems.” Communications of the ACM 40, no. 3 (1997): 56-58.

[10] Schmidberger, Markus, Martin Morgan, Dirk Eddelbuettel, Hao Yu, Luke Tierney, and Ulrich Mansmann. “State-of-the-art in Parallel Computing with R.” Journal of Statistical Software 47, no. 1 (2009).

[11] Suchard, Marc A., Quanli Wang, Cliburn Chan, Jacob Frelinger, Andrew Cron, and Mike West. “Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures.” Journal of Computational and Graphical Statistics 19, no. 2 (2010): 419-438.

[12] Tanenbaum, Andrew, and Maarten Van Steen. Distributed systems. Pearson Prentice Hall, 2007.

[13] Turaga, Pavan, Rama Chellappa, Venkatramana S. Subrahmanian, and Octavian Udrea. “Machine recognition of human activities: A survey.” Circuits and Systems for Video Technology, IEEE Transactions on 18, no. 11 (2008): 1473-1488.

[14] VanLehn, Kurt. “Student modeling.” Foundations of intelligent tutoring systems 55 (1988): 78.

[15] Watt, Michael G. “Research on the Textbook Selection Process in the United States of America.” Online Submission (2009).

[16] Webb, Geoffrey I., Michael J. Pazzani, and Daniel Billsus. “Machine learning for user modeling.” User modeling and user-adapted interaction 11, no. 1-2 (2001): 19-29.

[17] Wegener, Dennis, Thierry Sengstag, Stelios Sfakianakis, Stefan Rüping, and Anthony Assi. “GridR: An R-based tool for scientific data analysis in grid environments.” Future Generation Computer Systems 25, no. 4 (2009): 481-488.

[18] Wu, Eugene, Yanlei Diao, and Shariq Rizvi. “High-performance complex event processing over streams.” In Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pp. 407-418. ACM, 2006.

[19] Yoginath, Srikanth B., Nagiza F. Samatova, David Bauer, Guruprasad Kora, George Fann, and Al Geist. “RScaLAPACK: High-Performance Parallel Statistical Computing with R and ScaLAPACK.” In ISCA PDCS, pp. 61-67. 2005.

Document Personalization and User Data Privacy: Client-Side Document Processing Utilizing Locally-Stored User Data and User Models

An idea is described for client-side document processing, utilizing locally-stored user data or user models, to provide document personalization while protecting users’ data privacy.

The highly-customizable, user-configurable, processing of the XML macros, resembling relational algebra on XML trees, ideas resembling those of XSLT (1.0, 2.0, 3.0), utilizing XPath (1.0, 2.0, 3.0), utilizes interprocess communication, securely across application domains, while additionally describing a default behavior of markup processing per feature.  The document or page metadata pertinent to the processing of such macros is provided across application domains.  The document context of each XML macro pertinent to the processing of such macros is provided across application domains.

Documents can route events, events which occur in document contexts, along with document context information, across application domains to components which improve users’ data or users’ models with the information from the events including information about the usage of the documents, such as digital textbooks.

Streams of such events can be referred to as multisource streams of categorized events or event streams.  In addition to such streams of events, sensor data as well as the data derived from sensor data (which can also be described as event streams) can be processed by components on users’ machines. The data from the processing of such streams, including user models, can be stored upon users’ computers.

Applications can provide data to components, securely, configurably, across application domains.  Applications can securely, configurably, utilize data from components, across application domains, to provide utility, features, to users.  The locally-stored user data or user models, then, accumulate information, processing events from multiple applications, data including from the browsing or use of multiple documents, multiple digital textbooks, across quarters or semesters.

The following examples of XML macros, expressing concepts, are towards a syntax indicating the personalization of documents, digital textbooks, while also facilitating user data privacy.

Example 1: Resembling EPUB switch/case, XML macros can select content or varieties of the same content, a form of personalization, specific to user data or user models.  As per switch/case, a default option can be indicated, the means to specify a default option, per sequence, include referencing an element in an attribute value of an attribute upon the ext:choose element or by means of an attribute upon one element per sequence.

Example 2.1: Sorting content based on a specific function of a specified per-element key. The document context facilitates enhanced sorting of content, e.g. by how interesting topics are to students, in a document context, components utilizing locally-stored user data, user models, possibly utilizing syllabi or other materials.  Interest-based sorting can sort content with topics indicated at multiple levels of granularity.  There are a variety of interest-based sortings of topics, including percolating, sorting while also recommending, topics, combining interest-based sorting with recommendation heuristics.  The default sorting is the sequence of elements that occurs in the markup content.

Example 2.2: In scenarios such as digital books or textbooks, modular XML files can be utilized securely in macro processing; combinations of interest-based sorting macros, utilizing components securely, configurably, across application domains, with XML include macros, utilizing XML files in digital books or textbooks, could facilitate recommendation system features from the entirety the of contents of digital books, textbooks or reference materials.  Such features could be interoperable with the tables of contents or indices of digital books, textbooks or reference materials.

Example 2.3:

Example 3: Combining the ideas of the previous examples, selecting the first or best elements of a described sort, e.g. the most interesting content to a user.

Example 4.1: The technologies are interoperable with Web Components.

Example 4.2: The technologies are interoperable with Web Components.

Example 5: The technologies, in particular using document context while expanding macros, scale to natural language generation from semantic data.


Multimedia Tracks, Data and Metadata

Metadata standards, extensible ontology, vocabulary and API for multimedia track metadata, such as XMPMPEGMatroska and WebM, can provide enhanced viewing experiences and features based upon multimedia metadata.  Describing multimedia tracks with metadata enhances the uses of tracks, of track-based data, and the portability of multimedia files.  Web browsers and multimedia software can provide enhanced viewing experiences and features based upon multimedia metadata and including for multi-device scenarios as broached in: Argumentation Scenarios and Use Cases: Web and Television, Speeches, Presentations, Discussions and Debates.

A use case is that of multimedia presentations.  Presentation scenarios are numerous, including digital education.  Presentation videos might have a number of video tracks: (1) video of presenter, (2) video of a presentation surface, (3) video with cinematography between presenter and presentation surface. HTML5 supports synchronizing multiple media elements for simultaneous rendering such as video tracks (1) and (2). A fourth track, however, could be (4) side-by-side of tracks (1) and (2). Some of the combinations of presenters and presentation surfaces, as aforementioned, are referred to as enhanced video.

A 2011 article, HTML5 multi-track audio or video, about media resources with multiple media tracks, indicates the use of mediagroups. For example:

The example multimedia object, video.mp4 has four referenced tracks: v1, v2, a1 and a2. Track metadata can be useful for programmatic uses of multimedia tracks, where track identifiers, such as v1 and v2, are not indicative of the semantics of the track content, such as presenter and presentation. URI-based track metadata, from an extensible ontology and vocabulary, could indicate audio, video and data track contents for scenarios including enhanced video, multiple camera angle video, multiview video, free viewpoint video and 3D video and so as to increase the portability of the multimedia across web pages.

With URI-based track metadata, multimedia software could recognize multimedia track structure (see also: AudioTrackList and VideoTrackList), such as tracks of presenters and presentations, and from URL’s such as to video.mp4, to provide features, ergonomics and intuitive navigation.

Beyond features possible from XHTML transcripts, multimedia tracks can include: XML, RDF, temporal XML and RDF data, and other data pertaining to 3D geometry and animations (see also: as well as data pertaining to multiple camera angles, multiview video, free viewpoint video and 3D video. Utilizing track metadata, selections of multimedia can provide data in multiple clipboarding formats.

Extensible semantic metadata ontology, vocabularies, including the expressiveness of XMP, MPEG, Matroska and WebM, and JavaScript API can facilitate enhanced features, uses of tracks, of track-based data, and the portability of multimedia objects.


Semantics and Selectors

Semantics enhances the selection and styling of content; varieties of semantic selection include: (1) selecting upon URI items in white space separated lists of TERMorCURIEorAbsIRI values, (2) selecting upon parallel markup structure and reference combinators, and (3) graph-based selections with SPARQL expressiveness.

Selecting upon URI items in white space separated lists of TERMorCURIEorAbsIRI values, such as @xhtml:role, @rdf:type, @rdfa:typeof or @epub:type, could be expressed with a syntax resembling:

An example of selecting upon parallel markup structure, e.g. MathML content markup and parallel markup, and reference combinators:

Ontologydescription logic and semantic reasoning can enhance the functionality of selection based upon URI items in TERMorCURIEorAbsIRI attribute values, selection based upon the parallel markup structure and reference combinators and of graph-based selection, with an expressiveness resembling that of SPARQL, as broached in Document and Package Semantics and Metadata.

Document and Package Semantics and Metadata

Linguistic and semantic annotations, rhetorical structure and argumentation formats are some of the numerous scenarios where data or metadata are desired in addition to document trees, e.g. SSML and XHTML documents.  In SSML contexts, such data can facilitate prosodic speech synthesis and, in XHTML contexts, many new features are possible.

A solution for document and modular document component semantics is a document object model interface, e.g. document.semantics, a graph-based interface.  The contents of such a graph could be:

  1. From content regions in a document as per: <script type="application/rdf+xml">...</script> or <semantics type="application/rdf+xml">...</semantics>.
  2. Linked to from a document as per: <script type="application/rdf+xml" src="..." /><semantics type="application/rdf+xml" src="..." /> or <link rel="semantics" type="application/rdf+xml" href="..." />.
    1. A @rel attribute could vary processing or map graphs to resultant graphs; <semantics rel="annotation" type="application/rdf+xml" src="..." /> or <link rel="semantics annotation" type="application/rdf+xml" href="..." /> could map graph data to or from an annotation ontology.
  3. Inferred from or processed from other document content including: document markup semantics, structural semantics, attributes such as @xhtml:role, @rdf:type, @rdfa:typeof or @epub:type, microformats and RDFa.

Documents can interface as both trees and graphs.  A graph dataset could be derived from a document object model tree dataset, programmatic changes through a tree-based document object model could be reflected in graph-based data; a tree dataset could be derived from a graph dataset, changes through a graph-based API could be reflected in tree-based, document object model, data.

For modularity, object elements could have a semantics component and so too could custom elements. Web components could include a means of specifying such semantics in addition to styling and scripting. XML preprocessing can output semantic graphs including utilizing parallel markup.

In addition to a document semantics and metadata interface, an interface could reference package semantics metadata, as described in OpenDocument 1.2, Part 3: PackagesChapter 6: Metadata Manifest.

Enhanced features include semantic reasoning upon graph-based data and the Web-based and desktop-based indexing, search and retrieval of such data and metadata, the data and metadata of document packages, documents, document components and multimedia.  Furthermore, by expanding document object models to include document semantics, implementations of semantic selectors can be facilitated.

XML Preprocessing and XSLT Processing Models

There has been interest in dynamic or parameterizable XSLT imports and includes.  XML preprocessing, XML macros and XSLT-enhanced XML includes, facilitates such expressiveness.

For example:

such that:

describes and expands into a structure as per iterative processing and the iterative processing of XML preprocessing facilitates dynamic or parameterizable XML, XSLT-enhanced XML, and XSLT includes.

XSLT processing models are topical to XML preprocessing and, in addition to heuristics from other preprocessing models, advanced functionalities are possible from parallel processing, where each processing context is as a concurrent thread and can access a document object model, including traversal between macros and includes and macro expansions and included content, and where concurrent processing contexts can exchange messages.  Such concurrency facilitates advanced scenarios, e.g. layout or rendering engine logic and grammatical processing scenarios such as the grammatical framework.

For those interested, the topics pertain to: preprocessing, rewriting systems, string rewriting systems, term rewriting systems, graph rewriting systemsLindenmayer systems, parallel rewriting systems, process calculi and trace theory.

Also topical to macro expansion is outputting multiple subtrees and such that concurrent processing contexts can output @xref attributes referencing elements between subtrees:


Argumentation Scenarios and Use Cases: Computation

A use case category for models of argumentation, formats and ontology, is that of general-purpose computation. Pertinent topics include serializing and deserializing data structures to and from argumentation formats and the utility of such data structures for general-purpose computing. Accordingly, the expressiveness of claims should include that of lambda calculus and of abstract syntax trees.

function(…, IArgument** argument)

Topical are the Curry-Howard correspondence, program semantics, axiomatic, denotational and operational semantics, mappings from the structures of programs, subroutines in programs, to argumentation for outputs from inputs, such mappings to programs and subroutines which generate, in addition to outputs from inputs, argumentation for outputs from inputs, metaknowledge, metareasoningmetalogic programming and metaprogramming.  Some programming languages, such as logic programming and functional programming languages, include such expressiveness and other programming languages’ compilers could, for instance via program transformation, generate both function(…) and function(…, IArgument** argument).  Some scripting environments and runtime environments with suitable runtime reflection could, additionally, provide such functionalities.

In addition to mechanically generating argument structure based upon programming language structures or annotated structures, there is the programmatic, or manual, construction of resultant argument structure. The semantics of function calls could be topical to approaches as pertinent to the construction of resultant argument structure from the argument structures returned by subroutines.