IT Copywrite

Simple steps to add RDFa to web content

2008-05-01T10:42:36Z

In many web content publishing tools it is not possible to modify the contents of and element. Therefore it may not be possible to mention correct Document Type (DOCTYPE) and Content Type (@http-equiv) values. However it is still possible to add RDF normative metadata to your web content. Web content annotated with RDFa is scanned for RDF triples by major Search Engines such as Google and Yahoo!.

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"Adding semantics to the web involves two things: allowing documents which have information in machine-readable forms, and allowing links to be created with relationship values" (TimBL, WWW1994).

Vocabulary Namespace -: Add namespace declaration xmlns: to
,
, etc. elements that contain the annotated content.
Subject -: Add about="" to the
,
, etc. elements that contain the annotated content. The @about value is the subject in the RDF triple and the value "" of @about suggests that the subject for enclosed annotated content is 'this' document and hence the value of subject IRI shall be IRI of 'this' document as displayed in the browser address bar.
Predicate -: The value of @rel and @property is the predicate in a RDF triple. Add RDF properties i.e. normative metadata from vocabularies such as FOAF, vCard, Dublin Core etc. in the following manner:

If element is present then add @rel to element with value as property name from the vocabulary.

If element is not present then enclose the data to be annotated in element and include @property to element with value as property name from the vocabulary.

Object -

Example: Semantic Annotation of web content
Plain Content	Semantic Annotation with RDFa
Title of this post OR Title of this post Content of this post Ila Nivas OR Ila Nivas Date: April 15, 2008	Title of this post OR Title of this post OR Title of this post Content of this post Ila Nivas OR Ila Nivas Date: April 15, 2008
No RDF triples are generated without annotation.	RDF triples: dc:title "Title of this post" . dc:creator "Ila Nivas" . dc:date "20080415T1600-0500"^^xsd:datetime .

View RDFa on this web page

Drag the 'RDFa Highlight' bookmarklet from here to the bookmarks bar.
Open this blog post in the browser.
Now click the 'RDFa Highlight' bookmark, the embedded RDFa is highlighted with red border rectangle around the object.
The RDF triple can be viewed if you hover mouse over the rectangle.

Validate RDFa

You can test the compliance of your web page RDFa here. If it is valid RDFa you can display the W3C XHTML+RDFa seal on your web page. For compliance with W3C standards the following are correct values:

DocType -
Content-Type -: application/xhtml+xml is the correct value to allow processing of RDFa attributes by XHTML user applications.

Conclusion: XML contortion is not required to add semantic annotation to web content. RDFa has well-defined syntax, only correct syntax must be used to structure the data with RDFa. In this example it is illustrated how RDFa can be used to annotate credit card data. Similarly RDFa can be used to add semantic annotation to other product descriptions. Many time-saving tools can be built with semantic annotation of web content.

Search strategies to explore the invisible web

2008-04-13T08:22:52Z

Google has announced a new search strategy to explore the invisible web. Invisible web comprises web documents that have not been indexed yet. Google's new search strategy is to retrieve invisible information from the web sites that use HTML forms by providing input data to HTML input controls. As mentioned in the post that announces this search strategy they shall honor the privacy policy of a web site by processing robots.txt file and META tag 'ROBOTS' and avoid HTTP requests that require user information processing.

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Another Search Engine that explores the invisible web is RSSMicro Search Engine, this Search Engine crawls the XML feeds and collects syndication data.

"In other words, a new web is evolving which has its roots in XML based feeds rather than HTML pages. Some may refer to Web 2.0 or 3.0 but nevertheless the data is mainly comprised of user generated XML feeds. Unknowingly, we might be building a new web which is a duplicate of the original in terms of the content but is being transformed into XML" RSSMicro.

XML feeds provide good amount of semantically annotated data, the normative metadata used is specified in syndication specifications such as RSS 1.0, RSS 2.0 and Atom. Due to this normative metadata it is possible to collect keywords for intelligent search, e.g. 'category' element in RSS 2.0 and Atom specification provides the keywords used in the post. As mentioned on RSSMicro web site they collect "millions of keywords" and apply clustering algorithms to answer user queries. This normative metadata that is included in the RSS feed is not present in the web page of the news item published in the RSS feed. The difference between the two can be observed by viewing the source code of the RSS feed file and the source code of any post published on this feed. The 'category' element present in the XML feed is not present in the blog web page, instead the keywords are included as anchor text for HTML 'a' element with @rel=tag.

XML is the basis of all semantic technology programming languages such as RDF, RDFa and OWL. With RDFa and microformats it is possible to annotate the web pages so that normative metadata can be found on the HTML web pages also. It is possible to avoid the duplication of information by using same normative metadata in web syndication and web content publishing tools. This issue is addressed in RSS 1.0 syndication specifications to some extent, by inclusion of Dublin Core metadata in RSS 1.0 RDF description of items. The keywords can be annotated with 'dc:subject' property in both RSS 1.0 XML feed and the HTML web page. Since this duplication is being performed by an application that is generating the feed there is no loss of effort or information. Most syndication include only first few lines of the blog post and more data with semantic annotation may be embedded in the content. Example: use of vocabularies vCard, iCal, FOAF, etc. inside the content.

Most blog tools also include links to related, popular or previous posts, categories, archives and tag clouds on the blog web page. No HTML form is to be filled to access this data. A Search Engine such as RSSMicro that reads XML files to collect data can collect some amount of data from the one time syndication. More data shall be collected by the Search Engine that crawls all the @href and @src links on the web page, this shall cover the tag cloud, archives and categories. At this moment in web 2.0 the XML file crawlers have advantage because of normative metadata made available by syndication protocols such as RSS 1.0, RSS 2.0 and Atom.

Conclusion: Indexing of semantic data by Yahoo!, HTML forms by Google and XML data by RSSMicro are steps into invisible web. Invisible web now encompasses not only documents that have not been indexed but also information (i.e. data+context) that has not been found. Information present in the web content published with AJAX tools has data that can be explored with a combination of search strategies. RDF/XML corresponding to a web page derived with GRDDL transformation and stored on the web server with P3P controls shall also solve the data accessibility issues with AJAX applications.

Update

To view RDFa embedded in this page:

Drag the 'RDFa Highlight' bookmarklet from here to the bookmarks bar.
Open this blog post in the browser.
Now click the 'RDFa Highlight' bookmark, the embedded RDFa is highlighted with red border rectangle around the object.
The RDF triple can be viewed if you hover mouse over the rectangle.

Linked Data for Search Engine Optimization

2008-03-30T13:01:43Z

Search Engine Optimization (SEO) is a technique applied to increase the rank of a page in Search Engine Result Page (SERP). SERP is the list of URIs found for the user query and rendered in descending PageRank order on a single web page. Since most web users shall traverse the URIs on the first SERP or may be next few, most web content providers would want that their web page URI is included in the first few SERPs.

]]> The web page URI relative weightage that determines the URI position in SERP is called PageRank. One Search Engine may have a different PageRank algorithm than the other. Most Search Engines use relevance of keywords mentioned in link element, values of @name, @title and @id in HTML elements, number of inlinks and outlinks, PageRank of the inlink & outlink web pages, etc to determine the value of PageRank.

Therefore for a high PageRank it is important to add popular and relevant outlinks to a web page. Outlinks are URIs of other web pages that have been embedded with a element and @href in this web page. Inlinks are the URIs of the web page that have included the URI of this web page in their content. Some web authors also use cite element to refer to a web resource. Both inlinks and citations are counted by the Search Engine.

Inserting outlinks is one of the primary steps in creation of Linked Data because it identifies the 'things' that are connected with this web page. However repetitive insertion of the same outlink in a web page may not enhance the PageRank, because an intelligent Search Engine may count unique URIs only. Sometimes it is difficult to read a web document with too many outlinks to comprehend every reference, in such cases inserting a 'gloss' is a better idea to enhance readability. To add a gloss add @title in HTML a element with the text to be displayed. In web 3.0 repetition of outlinks may be avoided with @about and blank node.

If page 'A' refers to page 'B' then the outlink is embedded in page 'A' as follows:

web 2.0	web 3.0
page_B_anchor_text	page_B_anchor_text

The difference in the two representations is the addition of property dcterms:references in the latter. The syntax for this semantic annotation is given in RDFa specifications. This property identifies the relationship between 'thing' 'A' and 'thing' 'B', which in this case is 'A' references 'B'. It is not necessary that all outlinks are references, the relationship may be identified by any other metadata term.

E.g. consider this RDF description of 'albert' copied from Linked Data architecture note:

These RDF triples may be derived from the following web 3.0 web content, the equivalent web 2.0 content is also given here

web 2.0	web 3.0
The name of albert's children is brian and carol.	The name of albert's children is brian and carol.
The name of albert's children is brian and carol.	The name of albert's children is brian and carol.

Here the property fam:child is the relationship between albert and brian.

Some web 2.0 Search Engines are powerful to scan the complete document for any word or phrase in the text. These Search Engines may infer the relationship between Albert and Brian by reading the text preceding/succeeding the a element. The advantage of adding RDFa annotation in web 3.0 is that it highlights relationship information that is extractable into RDF triple form.

<#albert> fam:child <#brian> .
<#albert> fam:child <#brian> .

This RDF triple is stored in a database that is usable by many other web applications.Search is not the only application on the WWW, but is the basis of many other practical uses of web data. Search for Albert's children may return only the names of Brian and Carol in web 2.0, but in web 3.0 may also give additional information about Brian and Carol such as school name, home address, etc. The additional information about Brian and Carol may or may not be present on the same web page, but with Linked Data in web 3.0 this information can be fetched from objects Brian URI and Carol URI.

RDFa provides a syntax that can be used to publish information i.e. (data + context) so that machine extraction and processing of relevant information is possible and many different inferences can be derived from this data. Niche Search Engines may use the value of @rel to render the relationship in audio. A marketing agent may find the names of Albert's children in a particular locality and may approach their guardians with special discounts on uniforms, teaching aids, new books, etc. Guardian of a new admission to school may find other students in his or her locality. The WWW is considered as a media for publishing information and RDFa/RDF+OWL are the technologies to represent this information in a structured manner so that it can be used in different contexts.

In recent past new web applications have been launched that either provide the metadata for semantic annotation or perform the annotation for specified vocabularies. Calais and TextWise are popular applications and hand annotation is another method.

Open Issues:

A common vocabulary or a vocabulary repository is required that is one single place to find relevant normative metadata for semantic annotation.
A procedure is required to allow any web user to suggest an addition of a new metadata term in a vocabulary. Attribution of new metadata term or vocabulary may not be necessary.
A procedure is required to eliminate redundant normative metadata as new vocabularies are defined and new metadata terms are introduced.

Conclusion: In web 3.0 Search Engines that focus on natural language processing and intelligent search may reconsider PageRank algorithms. Search Engines shall use SPARQL filters and solution sequence modifiers to select and arrange information. Since Linked Data enhances authenticity of data therefore semantic annotation shall be a contributing factor in PageRank determination. Hence semantic annotation is a Search Engine Optimization technique. Linked Data shall be used to generate data mash-ups for many different contexts.

References:
OWL, RDF, RDFa, SPARQL

FOAF a factor for strong authentication

2008-03-06T10:04:37Z

A FOAF file is a mechanism to publish one's social and professional profile. It is also used to publicize one's social and professional network, the people one knows besides work duties and the people one collaborates with on project or in an organization. FOAF vocabulary properties foaf:knows and foaf:person are used to document these relationships on the web.

"The foaf:knows property relates a foaf:Person to another foaf:Person that he or she knows" [1].

If one suggests in his/her FOAF file that he/she knows this person (linked with foaf:knows property), the other person is under no obligation to accept that he/she is socially or professionally associated with the FOAF file owner (value of property foaf:name in FOAF file).

"If someone foaf:knows a person, it would be usual for the relation to be reciprocated. However this doesn't mean that there is any obligation for either party to publish FOAF describing this relationship" [1].

Advantages & risks of FOAF file

"You probably know hundreds of people, yet might only list a few in your public FOAF file. That's OK. Or you might list them all. It is perfectly fine to have a FOAF file and not list anyone else in it at all" [1].

Is it okay to create a long list of acquaintances in the FOAF file by adding foaf:knows? It may be okay to do so until no other web application is developed that may use foaf:knows links for purposes other than being informed about one's social & professional standing. As soon as FOAF becomes popular amongst masses it may be used to establish credibility of a person, to establish trust and to authenticate a person. The name FOAF is expanded to "Friend-of-a-Friend", FOAF specification suggests:

"The name was chosen to reflect our concern with social networks and the Web, urban myths, trust and connections" [1].

A hyperlink to one's FOAF file may be embedded by anybody in the web content in order to add a reference to the person. The properties such as foaf:Project and foaf:Organization may be used to find a list of collaborators for a project or an organization. It is not necessary to include these people in the FOAF file with foaf:knows property, a SPARQL query on foaf:Project may be used to find people working on the same project. Many tools can be built to take advantage of this personal information in order to build and find trustworthy connections.

Strong authentication with FOAF

If two people trust each other they can project this information in their respective FOAF file by including each other's FOAF file IRI with foaf:knows property. This reciprocation is a way of establishing trust in each other and this trustworthy connection may be used for authentication. It may be used for responsibility delegation, endorsement and collaboration, etc.

A digital certificate that is used for strong authentication constitutes more than one factor. FOAF Identity (i.e. FOAF file IRI) may be used as one of the factors in the construction of a digital certificate. If 'A' and 'B' trust each other and agree to endorse each other's communications on a certain matter they may get a digital certificate with 'A' and 'B' FOAF Identify as one of the factor for strong authentication. The certification authority shall validate the authenticity of the suggested relationship between the two with the presence of reciprocated FOAF Id with foaf:knows property in 'A' and 'B' FOAF files respectively. The suggested relationship shall be verified by the certificate authority every time it receives a request to verify and decrypt the message encrypted with the issued private key. This verification shall ensure that the suggested trust relationship between 'A' and 'B' is not broken, i.e. digital certificate is valid only until this suggested trust relationship is present in both FOAF files. Example: If either 'A' or 'B' removes foaf:knows entry for the other then the 'knows' relationship is broken; if either 'A' or 'B' changes foaf:Project value then they are not working on the same project anymore.

The relationship may be suggested by specifying other FOAF properties as well, such as: foaf:Group, foaf:Project, foaf:member, etc. The relationship may also be rendered by the user agents such as browsers and e-mail clients in the same manner as VeriSign EV SSL shows certification authority in the green highlighted browser address bar. In e-mail client 'From' bar the relationships may be shown in a drop-down menu.

Conclusion: FOAF data is public information it may be used for strong authentication. If FOAF data is included in the construction of a digital certificate, other private factors must also be included for autentication of FOAF data. The other possible factors that may be used in the construction of digital certificate are: passport number, driving license number, etc. FOAF data may add authenticity to the information by virtue of relationship between the information originator and the foaf:knows people or other FOAF properties.

References:
[1] Miller, Libby. & Brickley, Dan. (2007). FOAF Vocabulary Specification 0.91.

Is Calais an alternative to RDFa annotation?

2008-02-08T08:47:36Z

Reuters have launched a web service for annotation of content on web, they call it Calais.

We want to make all the world's content more accessible, interoperable and valuable. Some call it Web 2.0, Web 3.0, the semantic web or the Giant Global Graph - we call our piece of it Calais [1].

What Calais web service does?

This web service will accept the text content and scan it to find data for semantic annotation. The service shall find the appropriate metadata for semantic annotation. There is a provision that the content provider can suggest a vocabulary to be used for semantic annotation. The service shall store the RDF triples generated from the semantic annotation in a central repository and provide a Globally Unique Identifier (GUID) to the content provider. The service shall also provide these RDF triples to the content provider.

What should Calais web service user do?

The content provider who will use Calais web service must proivde the returned Calais GUID to those who need RDF triples corresponding to the published content. Any web application that needs RDF triples for the published content will find these triples in Calais central repository by providing the GUID.

Using the Calais GUID, any downstream consumer is able to retrieve this metadata via a simple call to Calais [1].

The content provider may also use RDFa to include the metadata terms from the RDF triples returned by the Calais web service for semantic annotation of the content.

What are the advantages of Calais web service?

The Calais web service has following advantages:

It shall find appropriate metadata for semantic annotation of the input content. The content provider does not have to search for appropriate vocabularies and metadata for semantic annotation of the content.
It shall generate and store RDF triples for the content in a central repository. The content provider does not need to include GRDDL transformations for RDF generation.

Conclusion: The Calais web service provides RDF triples for annotable data in the input content. The Calais web service does not use RDFa for semantic annotation of the content. The web content provider must hand annotate the web content by using RDFa and metadata from RDF triples returned by the Calais web service. An unanswered question is whether Calais web service will use normative metadata from a standard ontology. Use of RDFa and normative metadata are key to building semantic web/Giant Global Graph. The use of GUID may not be considered as a replacement to GRDDL transformation; the former will require that every time the content is updated the RDF triples stored in Calais repository must be updated and if GUID is changed then all users of this GUID must be notified. By using GRDDL transformation instead of GUID the users of RDF triples need not be notified about any changes to the content. The web server cache may store the latest updates to RDF triples.

References:
[1] Overview. 2008. Calais.

Analysis of consumer sentiments for context data

2008-01-11T18:12:23Z

It is noticed that weblogs more commonly known as 'blogs' are not only used to advertise about a product or service features but also to share opinion, give feedback and to review a product or service. Very often blogs are also used to compare products/services or to give an opinion on an opinion. This chain of comments and reviews not only creates an online social network of web users interested in a product/service but also contains a wealth of context data. Consumer sentiments and context data are the two crucial information elements that are used for business advancement.

]]> Consumer Sentiments

New Ontology

The new properties that may be required for expressing consumer sentiments in order to collect context data are shown in sample RDF triples:

 std:affect "liked/disliked/good/bad/recommend/reject"
 std:review "short comment"

Example

XHTML+RDFa	User Agent
xmlns:std="http://www.somestandards.org/ xmlns:dc="http://purl.org/dc/elements/1.1" ... You can find an exclusive small size natural color pearl necklace from this store, this shall be a perfect match with this hand bag. ...	... You can find an exclusive small size natural color pearl necklace from this store, this shall be a perfect match with this hand bag. ...

The blogger may use other terms such as recommend, suggest, etc. The object value for property std:affect may be any combination of verb, adverb and adjective. If the blog poster uses RDFa attribute about to identify the 'thing' discussed or referred in the post and attribute property to describe the 'thing' characteristics within the blog post then the data for consumer sentiments and context information can be gleaned from the blog post. Product IRI that is the subject of the RDF tripple shall be same in all blog posts, the predicate of this RDF tripple is std:affect.

Product Context Data

Reference of other products/services by bloggers in a blog post for a particular product/service can provide intelligent context data. E.g. mention of sunshine pearls in a blog post on hand bags provides context information on what size and color pearls match with a hand bag. A business opportunity for both handbags and pearls can be found by the analysis of this context data. The two products are bound to each other with the property dc:relation. The binding may be written in a reverse order as well, it depends on how the sentence is framed. The requirement is to provide semantic annotation of data and to provide marketing tools that can use this context data to find new business opportunity. The tool must be intelligent to resolve std:affect synonym values in order to produce context histograms.

SPARQL query

PREFIX std: 
PREFIX dc: 
SELECT ?s ?o ?v
WHERE { 
	{ 
	  ?s dc:relation  .
	} UNION
	{
	   dc:relation ?o .
	}
	 ?p1 "small"@en .
	 ?p2 "natural"@en .
	FILTER(?p1 = std:size && ?p2 = std:color)
	 std:affect ?v .
      }

This SPARQL query shall select all web resources that are related to "pearl IRI" and have included the given properties for "pearl IRI". The SPARQL query solution shall also include the value of property std:affect used in the web resource (e.g. blog post). Blog category and tags also provide data that may be used to collect relevant information from this free source of online information. The properties that may be used for these are dcmitype:collection and dc:subject respectively.

Microformat vote-links and hreview may be embedded for rating. Vote-links microformats for similar purpose may be included as value of rev attribute. The hreview microformat 'rating' may also be used to provide numerical rating.

Conclusion: The advantage of subjective opinion on a product through std:affect property is that it has enhanced expressive power as compared to numerical value. The advantage of using RDFa over microformat are obviously the emerging semantic web applications. The purpose of the new properties is to enable annotation of language qualifiers within a sentence, the alternative may be to provide the value of the new properties as radio buttons. But the objective here is to capture the sentiments from within the text of the blog post. It is not possible to provide radio buttons for all products that may be referred to in blog posts. Without this semantic annotation of products and sentiments this useful context data may be lost.

Reference:
DCMI Metadata Terms, RDF, RDFa, SPARQL.
Leon, de. Ponce, Sandra. (2007) Sentiment Scoring Partnership With KD Paine. Buzzlogic.

Consumer requirement for "open market"

2008-01-05T14:52:22Z

A consumer has asked for help in finding a suitable credit card for credit score 550. How can semantic technology answer the question?

The credit report information is available to the Credit Reporting Agency (CRA), the credit card companies who want to offer pre-approved credit cards can search the protected CRA database for potential customers. The information about credit data available and accessible may be published and advertised through a Web Services Description (WSD) document. Access to the protected database may be controlled with user (the user may be an individual who has a credit record or credit company that can benefit from access to individual data) authentication and authorization. Different services may be made available to different type of users thus the database fields accessible to a user can be controlled. Federal laws for fair credit may be referred for guidelines. Credit agencies may use this data to calculate the credit score according to a proprietary formula or may use the credit score provided by CRA.

]]> Some vocabularies may be defined to enable machine processing of natural language queries such as "Find best credit card for credit score 550" for intelligent answers. The basic vocabularies required may be described as:

credit report vocabulary - http://www.somestandards.org/cr
credit vocabulary - http://www.somestandards.org/credit

Some normative metadata terms that may be used to answer this query are:
"xmlns:credit=http://www.somestandards.org/credit#"
"xmlns:std=http://www.somestandards.org/ns#"
"xmlns:dcmitype="http://purl.org/dc/dcmitype/"

normative metadata term(predicate)	possible values(object)	description
credit:csformula	beacon, empirica, fico	credit score formula
credit:csvalue	blank node label	credit score range and credit rate
credit:scoreMin	300-900	Minimum value of credit score
credit:scoreMax	300-900	Maximum value of credit score
std:rate	0-100%	interest or premium rate
dcmitype:dataset	Equifax, Experian, TransUnion, etc.	Name of credit report database

Credit Reporting Agency (CRA) may describe its service as (RDF triples):

 dcmitype:service "Credit Reporting Agency"@en
 dcmitype:dataset "Equifax/Experian/TransUnion"
 credit:csformula "Beacon/Empirica/FICO"

Credit Providing Agency (CPA) such as bank, credit card, insurance etc, may describe the service provided as (RDF triples):

 dcmitype:service "Credit Card"@en
 dcmitype:dataset "Equifax/Experian/TransUnion"
 credit:csformula "Beacon/Empirica/FICO"
 credit:csvalue   _:range1
_:range1  credit:scoreMin  "300"^^xsd:integer
_:range1  credit:scoreMax  "400"^^xsd:integer
_:range1  std:rate         "interest/premium"

A search engine may translate this user query into a SPARQL query as follows:

SELECT ?s ?minval ?maxval ?rateval
WHERE {?s  dcmitype:service "^Credit Card"@en .
	?s  credit:csvalue   ?bn .
	?bn credit:scoreMin  ?minval .
	?bn credit:scoreMax  ?maxval .
	?bn std:rate         ?rateval .
	FILTER  (?minval <= 550 &&  ?maxval >= 550)
	}
ORDER BY ?s ASC(?rateval)

However the suggested semantic technology method may succeed only if CPAs publish the guidelines for interest/premium rate and credit score relationship. There may be more factors involved other than credit score that shall be used in determining the approval of credit card and interest/premium rates. The human and business factors may overrule technology. The other suggested method to find an answer to this query is to post this question to an appropriate forum. A popular forum for credit card related issues can be found by a simple query: "popular credit card forum". If the forum has included semantic annotation (RDFa) then it shall be found by the semantic Search Engine. The forum may use the following terms for semantic annotation:

 dcmitype:service  "blog/forum"
 dcmitype:collection "Credit Card"@en
 std:popularity "IntegerValue"^^xsd:integer

Conclusion: Definition of Credit Report vocabulary and a data security mechanism that may control access to information shall provide enhanced logical solutions for such problems. The ability to compare how different credit card companies consider credit report and credit score shall be the answer to the consumer question. More on std:popularity property and access mechanism later.

References:
SPARQL Credit Report Credit Score

Semantic Annotation of Blogs in Web 3.0

2007-12-23T11:51:47Z

In order to incorporate the semantic annotation in the blog post, the standard ontology shall have to be used by blogging tools like MovableType, WordPress, etc. These tools use proprietary values for HTML element ‘div’ attributes id, class, etc. to annotate the published content stored on the web server. The limitation of the present practice of proprietary annotation is that the blogger has to register the blog in various forums by submitting the blog URI and Search Engine indexing is based on keywords defined in the META element and other HTML element attributes like id, etc. As described in the previous blog post a method is required to automate the process of blog discovery. I.e., classification of blog post is required for categorization.

]]> Common Vocabularies for semantic annotation

The Semantically-Interlinked Online Communities (SIOC) core ontology a W3C member submission by DERI Galway, Dublin Core Metadata, SKOS and FOAF are some of the vocabularies that may be used for semantic annotation of the blog posts. A simplified version of semantically annotated blog post may be achieved with Dublin Core Metadata and FOAF vocabulary.

xmlns:bg="http://purl.org/dc/elements/1.1/"
xmlns:bgterm="http://purl.org/dc/terms/"
xmlns:bgtype="http://purl.org/dc/dcmitype/"

normative metadata	description
bg:contributor	comment posters
bg:creator	author of the post
bg:date	date of publication
bg:description	content of the post
bg:identifier	identifier for the post
bg:publisher	e.g. IT Gumbo
bg:relation	URI of related blog posts
bg:subject	keywords that shall classify the post
bg:title	title of the post
bgterm:abstract	either an abstract or selected lines of the post
bgterm:bibliographicCitation	for references
bgtype:image	for picture of the poster

Some other terms from Dublin Core Metadata, FOAF and SKOS may be used. The DMOZ category can be added for post classification by adding bg:service property.

User interface for semantic annotated blog post

The blog posters may not use SIOC vocabulary; instead this vocabulary may be used in the blog tools. The blog tools may not necessarily use the SIOC RDF browser to publish blog content but instead use RDFa or microformats for semantic annotation and may include a GRDDL transformation. GRDDL transformation shall be used by semantic web applications to generate RDF/XML for the blog post. Refer table [2], column 2. The blogging tool shall use XHTML+RDFa to publish blog post, it is not necessary to visually render RDF/OWL class & properties these are embedded in the published content in order to annotate the content for semantic web applications. Refer table [1], column 2. The published semantic annotated blog post shall reside on the web server. There may be no visible change in the user interface for posting a blog or in the published post.

The blogging tool may continue to use its proprietary XForms or HTML forms for user input interface. Tool may either include Qnames for normative metadata in XForms model to generate table [2], column 1 submission or if proprietary tags are used in XForms model then a mapping to normative metadata terms shall be required in order to publish XHTML+RDFa.

The tools that use XForms or HTML forms for user input and standard ontology for semantic application must be able to publish web content with RDFa semantic annotation in order to make the content useful for semantic web applications.

Elimination of redundant metadata

If there are no new characteristics added to a Class or Property, it is better idea to use the base property rather than defining a new property as sub-property-of another property. Example: sioc:subject is defined as sub-property-of dc:subject. The issue here is that sioc prefix expands to http://rdfs.org/sioc/ns# namespace and dc prefix expands to http://purl.org/dc/elements/1.1/ namespace. Another new vocabulary http://some.org/newvocabulary/ may define a new property xyz:differentLocalPart as a sub-property-of dc:subject. In order to answer a user query “applications of SIOC" the Search Engine SPARQL agent shall have to select all web resources that use any of these normative metadata terms with value 'SIOC'. This may not be an easy task for SPARQL agent because the query must then find and include all evolving similar normative metadata terms such as sioc:subject, dc:subject, xyz:differentLocalPart and so on.

Note: Web users may use different prefix for the same XML Namespace, here in this blog post prefix ‘bg’ has been used to suggest that a standard ontology is required for blog annotation (to continue discussion with previous post on this topic). This standard ontology may be one of the existing common vocabularies (e.g. Dublin Core Metadata).

Advantages of standard ontology

The advantages of defining and using standard ontology are:

Redundant normative metadata is eliminated,
Ambiguity due to similar vocabularies is resolved,
WWW user shall not be inhibited of writing content for ‘semantic web’; some web users may not use blog tools and may hand-type semantic annotation in the published content. Single source of information (i.e. one common vocabulary) with one standard ontology to annotate a ‘thing’ shall address the first two points.
If normative metadata from same vocabulary is used to annotate blog posts and non-blog content then more data can be found for a keyword or category. Example: refer example in the table [1], predicate is bg:subject, object is SIOC and subject is the IRI of the blog post. Non-blog articles may also embed this property for keyword/tag ‘SIOC’. There shall be more number of IRIs in the IRI database for bg:subject='SIOC'.

Conclusion: It is not necessary that browser must render the ontology in XForms, HTML forms or published content. The blogging tool must be able to validate and process the XML data received from the XForms or HTML forms user interface and use RDFa to embed standard ontology in published content. Same normative metadata must be used for annotation of blog posts or other published web content in order to provide more data for semantic web applications.

Table 1: Published Blog Post
Web 2.0 annotation of blog post	Semantic annotation of blog post
Blogs in Web 3.0 Content of the post. Written by Ila Nivas Tags: SIOC RDFa Blogs	Blogs in Web 3.0 Content of the post Written by Ila Nivas SIOC RDFa Blogs Weblog Information Technology IT Gumbo

Table 2: Structure of Blog Data
XML data submitted to XML processor	RDF/XML data extracted from published content
xmlns:foaf="http://xmlns.com/foaf/0.1/"> Blogs in Web 3.0 Content of the post Ila Nivas Ila Nivas SIOC RDFa Blogs Weblog Information Technology IT Gumbo	xmlns:bg="http://purl.org/dc/elements/1.1/" xmlns:foaf="http://xmlns.com/foaf/0.1/"> Blogs in Web 3.0 Content of the post Ila Nivas Ila Nivas SIOC RDFa Blogs Weblog Information Technology IT Gumbo

Web Resource Classification for Search Engine Taxonomy

2007-11-12T06:26:14Z

As more data on WWW is made available with semantic annotation of web resources the categorization of web resources based on classification with characteristics identifiable by normative metadata shall be the key to development of semantic web applications. The inclusion of normative metadata from standard ontology as web resource descriptors in POWDER DR or embedded in the web content with RDFa shall provide data for search engine indexing to build SE taxonomy.

Characteristics are properties of a 'thing'.
Classification is the identification of characteristics for categorization.
Categorization is grouping of resources with same characteristics.
Taxonomy is the process of classification.

The value of HTML language elements and attributes such as content attribute in META element with attribute name='keywords', heading elements, alt, name, title, hreflang and media, etc. are used by search engines to classify web content. While these attributes may still be used for web resource classification, the normative metadata shall extend the vocabulary that is used for web resource classification. The difference is that HTML element and attribute tag value and not tag name are used for web resource classification, the RDF/OWL class property (predicate) as well as values (object) shall be used for classification of semantic web resources (subject). It is important to note that HTML tag names do not mean name attribute value but tag names such as META, TH, A, H1-H6, etc.

]]> Search Engine Indexing

Search engines may use the web resource classification to build taxonomy for efficient search. Since the web resources (subjects) with same classification, i.e. <predicate, object> tuple shall be categorized into one category, the hierarchical taxonomy may be built with normative metadata from standard ontology.

In semantic web all data is represented in the form of <subject, predicate, object> triples. The object is a value of the predicate, i.e. it is the value of a subject characteristic that is identified by the predicate. The object may be another web resource or a literal value. Hence all web resources with the characteristic identified by predicate shall have one of the well-known values for object. If the object value is an integer typed literal, SE may use predicate only for classification. This web resource classification is used to build SE taxonomy for web resources where IRI database is build by characteristics predicate and/or object. The figure illustrates the SE hierarchical taxonomy derived from the web resources RDF graph for <subject, predicate, object> triple.

In the figure the web resources in the IRI database include either of the following <predicate, object> tuple in the RDFa annotation of the web content or in POWDER DR.

dcmitype:service "ISP"
dcmitype:service "Internet Service Provider"
dcmitype:service "Electric Service Provider"
dcmitype:service
dcmitype:service

The web content providers have classified the web resources ('thing'); SE shall use these classification characteristics to build taxonomy for WWW IRIs by grouping all web resources with same characteristics. All web resource with dcmitype:service property are further classified by the value of this property. The natural language query "ISP" submitted to the search engine by the user is transformed into the following SPARQL query by the search engine.

SELECT ?s WHERE
{
?s dcmitype:service "ISP", "Internet Service Provider" .
?s dcmitype:service .
}

Note: Search engine may add other expansions of "ISP" abbreviation to the query. User must enter desired expansion for efficient query.

Conclusion: A natural language query submitted by the web user that is converted to SPARQL query with relevant normative metadata corresponding to the user query terms can be answered by traversing the SE hierarchical taxonomy to retrieve web resources from the IRI database.

References: POWDER, RDF, RDFa, SPARQL.

Why use POWDER for web resource description?

2007-10-11T13:08:04Z

POWDER specifications provide mechanisms for defining and identifying trust worthy web resource descriptions. The web resource description is the description of web resource characteristics described with normative metadata. The use of normative metadata is the basis of proof and trust requirements of semantic web. The metadata terms defined in the POWDER specifications provide a structure to semantic annotation of a web resource.

]]> We identified three types of metadata that can be used to describe a web resource. RDFa can be used to embed this metadata in the web content. The POWDER Description Resources can be used to separate the normative metadata that describes the web resource characteristics from the web content. The characteristic properties identified with normative metadata can be included as DR descriptors. POWDER specifications suggest that descriptors " DO NOT describe the DR, rather, they describe the resources in the Resource Set that is the scope of the DR".

Example:

RDFa	POWDER
The xyz.com is an ISP .	ISP

All web pages on xyz.com may link to the DR that includes the given descriptor, whereas if this characteristic dcmitype:Service is embedded with RDFa then all web pages shall include RDFa statement. However the link to POWDER DR does not preclude the use of RDFa.

The metadata terms defined in POWDER can be used to:

to authenticate normative metadata used (e.g. DR validity and trust properties)
structure the semantic annotation of the web resource (e.g. web resource classification and resource grouping)
avoid repetition of same normative metadata in multiple web resource content (e.g. include link to relevant Description Resource)
enable fast parsing of web content for SPARQL query solutions required for "open market" and other semantic web applications (e.g. Resource Set definitions)

Consumer personal and context data security concerns

2007-09-26T12:24:47Z

Ubiquitous internet access and globalization of human civilization has encouraged both sellers and buyers to approach each other beyond national borders. Irrespective of language, law and culture limitations people want to know, learn and practice new ways of life.

]]> This intermingling of people has encouraged web service providers to cater to people from different parts of society, honor laws and language of different nations. Web service providers collect visitor personal and context data in order to know about :

the popularity and reach ability of their service
the consumer interest to provide better service

The collected data is used internally for marketing research and much useful information is extracted. This information is used by the service provider to advance their business by providing better online service specially implemented for different parts of the world, individualized services for the consumer and may also be released to research organizations for global research. This silent and hidden data collection inflicts fear in consumers that result in either apprehension in use or prohibition of web service. The competition between service providers inhibit sharing of collected data specification. As a result different data may be collected by web service providers and the consumer is unaware about the personal data that is collected by the website. The internet user has many questions:

What user data is collected by the website?
What will web service provider do with this data?
What are the risks in sharing data?
What are the advantages of sharing data?
How can provider inform consumer about the data that is collected?
How can consumer control collection of data?
How does provider secure collected data?

What user data is collected by the website?

Some samples of data that may be collected by the website are listed here, this is not an exhaustive list of data collected by the website.

Personal data: Website may silently collect data for traffic statistics, this data is also called log data and include visitor IP address, type of browser used, IRI of web pages visited, time spent per page etc. Website may ask user to input data in online forms, this may be contact information, credit card information or individual interest information.
Context data: Website may also collect context data based on user behavior such as inlink IRI to know the point of reference to the website, outlink IRI to know how and when the user exits, whether he/she returns back to the website after visiting the external reference. What search terms are entered by the user in the "Google Search" box provided on the website, whether the search is conducted on the WWW or on the website? What external search terms directed the user to this website?

What will web service provider do with this data?

Many business and marketing decisions are driven based on the personal and context data collected by the websites. This data is used in a similar manner as free/paid surveys. If some 'n' number of visits from a region show an interest in a product this reflects the popularity of the product in that area. This may lead to marketing research by the organization to find business opportunity.

Personal data: Website shall use personal data to provide secure access to information and allow users to save private context data on web servers. Most e-business will provide login/password in order to protect their online data, to collect personal/context data, to allow user to store e-business context information such as current orders, order status, order history etc. Service provider can send new sale intimation to the user e-mail address or home/office address, etc.
Context data: Website shall use context data to advance their business and provide better service to the consumer. An interest of multiple users in a product that is not available on e-business website may encourage the provider to explore the business opportunity. If a user is not able to find the relevant data on the website or if there is associated information to the user search terms, personalized e-mail may be sent to the user e-mail address.

What are the risks in sharing data?

Spam, spoofing and phishing are some of the risks of online data transactions; consumer apprehension and prohibition are the dangers of data collection. Denial of service by the provider to the consumer due to latter's refusal to share data can be former's policy. Stolen identity may disturb finances and peace.

User to provider: Log data is the visitor foot print on the internet. In addition to the website data collection, at home or in office the web user activity can be watched. An individual may be targeted by international terrorists or internet culprits by tracking log data. In case of solicited data the consumer consciously provides data by agreeing to the published privacy policy.
Service provider to 3^rd party: The contract between the two parties governs the data ownership and security policies. The web user who provided the data may not be aware about the identity of the 3^rd party who has access to data. The security breach due to 3^rd party negligence may result in consumer annoyance. The service provider is at the risk of loosing the consumer unless it is ensured that 3^rd party will also honor the consumer interest.

What are the advantages of sharing data?

Consumers can receive personalized service. Data shared is used for research, business advancement and new inventions. If you like thing X with color Y and you search for it on the internet it is very likely that the search term is logged. This search may lead to a new product X with color Y in your vicinity soon. The log data is also used to catch international terrorists and internet culprits such as hackers and spammers.

How can provider inform consumer about the data that is collected?

Most websites will include a "Privacy Policy" web page to inform the user about the data collected. This web page will include human readable text description of the policy and is written in the regional language. The human readable text is to be trusted by the consumer, the service provider may display the regulatory compliance and audit seals and certificates on the website. Web technologies have defined P3P specifications for machine-readable policies. Provider can publish and configure different P3P policies for sections of website or have a single policy for the complete website. If website uses cookies to collect data, the P3P policy must include the policy for the data collected by the cookie. The compact P3P policy is published to control the cookie data collection and is carried in the HTTP response header, the browser can decode this policy and prompt the user according to browser privacy settings. The advantage of providing website P3P policy is that it provides better control to the visitor on the data that is collected by the website. A website that has deployed P3P policy will not collect personal identifiable data with cookies unless the P3P policy has been fetched by the browser and the fetched policy complies with browser privacy setting configured by the user.

P3P specifications provide standard structures for the commonly collected log data and a mechanism by means of EXTENSION element to include proprietary log data structure. The log data structure in P3P policy along with the text description in human readable policy will eliminate the user fear of unknown data collection policy and also educate the web user about the significance of the collected data.

How can consumer control collection of data?

Consumer can control the collection of personal/context data by not visiting the websites that have published data collection policy not amiable to the consumer. Web technologies provide the following mechanisms for consumers to control data collection:

Set policy preference in browser: The privacy option in internet settings has options to configure the 1^st party and 3^rd party cookie acceptance. This setting will allow/block cookies irrespective of web page P3P policy.
Access mechanism in P3P policy: The access mechanism in P3P has three options: "always", "opt-in", "opt-out". The data that is flagged as "always" does not provide any option to the website visitor and is always collected. The website visitor can choose to provide or reject request for data that is flagged "opt-in" or "opt-out" respectively, the mechanism to "opt-in" or "opt-out" is specified either in the human readable privacy policy or may be provided dynamically as forms or pop-ups on the website.
Interactive user interface on website: Websites that do not publish P3P policy support forms & pop-ups to seek user consent for data collection.

How does provider secure collected data?

The collected data is secured by the web service provider:

During data collection transaction: Data is collected over Secure Socket Layer (SSL) connections. SSL connections encrypt the data and provide checksum, digital certificate protection against data corruption and spoofing.
In storage during retention period: Data is stored on servers behind firewall to protect against unauthorized access. Strict information security policies are practiced within the organization to avoid security breach due to computer/network system failure or employee mishandling.

After the published data retention period is over the web service provider must destroy the collected data and ensure that destroyed data is not accessible. The compliance of the organization with the necessary security standards and regulatory laws is displayed on the websites in the form of seals and certificates.

Conclusion: Build a website that can infuse confidence in visitors by informing what data is collected, how this data is secured and how this data is used. Unless necessary provide an open access to information on the website and collect minimal data. Many free content websites require user login/e-mail and password to collect user behavior context data, this may inhibit many interested readers. The syndication methods like RSS/ATOM feeds may be used to communicate with visitors. Add machine-readable P3P and human readable text policies to the website to provide better consumer control on data. Web technologies must be used to build simple and secure website. The integration of P3P policies stored on web server with user interface i.e. browser and data storage systems behind the firewall will automate the data collection process and security audits thus infuse trust in the web user. Data rich applications such as "open market" for semantic web and smart stores will provide better data collection policy transparency with integrated P3P policies.

Data Security in Semantic Web

2007-08-31T15:07:18Z

Data is the building block of the semantic web; therefore security of all data in the semantic web is the essential requirement for the success of semantic web. The web service provider and web service requester data must be secured. Web services specifications and semantic technologies provide mechanisms to define and execute security policies for different types of data. Data that must be secured is:

Published data - Data that has been published by the web service provider as HTML web content or is accessible according to Message Exchange Pattern (MEP) described in Web Service Definition (WSD) document.
Concealed data - Data that is stored in the internal storage media of the web service provider and is not available for public access. Data that is stored in the personal computer storage media of the web user who connects to the semantic web.
Personal Identifiable Information - Data that is provided by the web service requester and is collected by the web service provider.

]]> In addition to data security it is important to ensure web service requester and web service provider integrity is secure. To ensure integrity security the providers must ensure that information provided does not break the information exchange contract between the provider and the requester, i.e. the wire messages are constructed in accordance with WSD and are processed according to the published policies. In semantic web where the automated web service integration is promoted the WSD documents and policy documents must be carefully elaborated to ensure that no unwarranted access to information is allowed. The web service requester & provider must provide the message IOPE with ontology references (modelReference and schemaMapping) and policy definitions to find the best match web service from the semantic web service registry.

Example:

There is a video library that maintains an online database of all disks. Assume that this library wants to provide a special service for non-adult members. The non-adult members can host access to the library database on their web site in order to post reviews, blog and create discussion forums. The views of one community created on a web site can be shared with another community through library database and thus cultural information in different localities is shared. For the benefit and interest of its non-adult members the library hosts the community profile and views on the library web server. The library web service provides policy alternatives to control access to the database for different rating (TV-14, PG, R, etc.) movies. This suggests that the member web sites must select the movie rating for membership and include rating in all messages.

Title	Rating
Heroes	TV-14
Death Sentence	R

If the metadata terms that describe the audio/video record in the library database are proprietary tags of this library then the web requesters (non-adult members) who want to integrate with several libraries around the globe will have to manually read the MEP and policies of library web services from XML files to find appropriate security for content that will be published on their web sites. Use of normative metadata (title, rating) from a standard ontology (library movie record) that describes library records will enable automated discovery and integration with library web services. The web requester can find library web services with a particular rating movie database from the semantic web registry.

Library WSD	Library member WSD

In this example, libMovieRec, listMovieRating are objects from a standard ontology. The object listMovieRatingNA is the list of movie rating values for non-adults that is supported by this library. The library member supports access to TY-14 rating movies only on the member web site. Therefore the library member WSD document has precondition for message output parameter element to have value TY-14 for rating. This member web service will integrate with those library web services that provide access to TY-14 movie database. The library has provided access to all non-adult movies on this interface and adult movies may be accessed on another interface . The precondition child element in the operation element of an interface IOPE is used to suggest the rating value accepted on this interface. The rating value is an input parameter element for the library web service. Another member who wants access to R movies will not be able to integrate with this service of library.

Conclusion: The semantic annotation (reference of standard ontology) in web service WSD document will provide secure interface for automated integration of web services.

Encoding normative metadata context

2007-07-09T14:20:29Z

Here it is suggested that if normative metadata terms can be represented by alphanumeric codes defined by respective standards organization associated with the product, then this will simplify the web services development and make search more efficient. The Web Service Definition (WSD) and Functional Definition (FD) documents will then use this normative metadata code only. The translation and conversion of the natural language query to normative metadata code will be done by the thesaurus based context mediator.

Example:
Normative metadata term code - xxxyyyzzz
Standards organization identification - xxx
Product vocabulary identification - yyy
Metadata term identification – zzz

]]> By assigning alphanumeric codes to the metadata terms the ambiguity due to use of synonyms in the vocabulary can be eliminated. Synonyms can be assigned the same code. The identification of a metadata term in the user query and translation of this term to code may be done by the context mediator. This alphanumeric code will encode the metadata context within the code, thus connecting the language with the data. This method is analogous to the EPCglobal tag that is assigned to physical "things". Since all web resources subjects, predicate and objects are considered as "things", this alphanumeric code will assign a unique identity to every "thing" on the World Wide Web. Within a web document the metadata term code will still be used as text term, i.e. by including vocabulary namespace and reference with Qname.

The vocabulary that defines an alphanumeric code for a metadata term can be identified from the code. Introduction of alphanumeric codes for normative metadata terms does not add any processing overhead for context mediators. The context mediators will still process the natural language query to understand the query context and find corresponding vocabulary. The use of alphanumeric codes for metadata terms will enable introduction of new features in database applications; intelligent context search algorithms can be built based on these codes. The textual terms require an exact match of vocabulary and metadata, whereas alphanumeric code will enable search on sub-code such as "xxxyyy". Thus a two-pass search where first a web document that uses a given vocabulary is found and then the metadata term is searched within the document can be replaced with single-pass search algorithms based on metadata term code.

Consider RDF triples:

DSLBroadbandService	BitRate	_:2Mbps
_:2Mbps	rateValue	"2"
_:2Mbps	unitValue	"Mbps"

Service_code	BitRate_code
	rateValue_code	unitValue_code
DSLBroadbandService_code	2	Mbps
DSLBroadbandService_code	10	Mbps
SatelliteBroadbandService_code	200	Mbps

The table has BitRate column with two sub-properties rateValue and unitValue. These sub-properties are assigned unique code "aaabbb001" and "aaabbb002" respectively, where "aaa" is the standards organization (e.g. IEEE) that defines the vocabulary for broadband service, "bbb" is the code that uniquely identifies broadband vocabulary amongst other vocabularies defined by this organization and the values "001" and "002" identify the property rateValue and unitValue respectively. The table stores the actual values of the elements of BitRate class for all instances of broadband service record. A search for "Mbps broadband service" can be limited to search for records with BitRate sub-property "aaabbb002" value "Mbps". A search for vocabulary "aaabbb" is eliminated and direct search for metadata term "aaabbb002" is executed.

The BitRate class may be written as (rateValue_code = aaabbb001):

The disadvantage of these alphanumeric codes for normative metadata terms is that these codes will have to be embedded in the HTML schema of a web page to enable assimilation of information in the semantic web framework. Whereas textual metadata terms would have enabled assimilation of information in the semantic web framework even when metadata terms are not added to HTML schema, this text may be found by pattern matching.

Conclusion: The advantages of alphanumeric code for normative metadata terms are:

context is encoded within metadata term code,

context search features can be added to database applications,

more efficient search applications can be built.

Normative metadata for services

2007-07-07T07:46:49Z

Web users have diverse requirements and seek different information. In order to do this they form different phrases and try to get the best result from a search application. The search query that is composed with the same meaning as the question generally seeks the best result. To support these diverse requirements most text search engines have been built with the basic algorithm as: best match for the query pattern in the web content. Semantic technologies aim to provide accurate and fast objective results that will not only enhance customer satisfaction but also provide better business opportunities. A customer has to find an appropriate search application that can lead him/her to the best business solution available for his/her requirement. Once the business solution has been found then the online customer interface of the business must be able to answer the customer queries.

]]> What are customer requirements?

The normative metadata is the solution to standardize these requirements so that comparative search can be conducted in a competitive "open market". While normative metadata definition does not stop web service providers from introducing proprietary metadata, the former is a method to participate and play in the larger framework supported by semantic technologies. Normative metadata for different businesses may be formed based on the product/service characteristics and customer requirements. This normative metadata may be used directly or mapped to internal database structure of the business to maintain service records and business information.

Example:

A traveler has many questions that must be answered before a travel agency is chosen. The apprehension is about spending a day reading articles to find the most appropriate information source and then the waiting period to get answers from the customer representatives. There is a hidden wealth of metadata terms related to travel business that can be found in this article, such as: family vacation, adventure, cheap hotel, discount ticket, couple only resort, travel directions, hotel check-in and check-out, etc.

Conclusion: Normative metadata terms for service businesses can be collected from the customer requirements and online customer interface. Service records, customer feedback and popular search terms are some of the possible information sources for normative metdata definition.

Extensibility of normative metadata ontology

2007-07-04T16:56:02Z

Consider the following OWL ontology with normative metadata (assumed) provided by a standards organization, this ontology describes the characteristics of a product "Laptop":

]]> This ontology is extended by web 3.0 search application provider as follows:

Different vendors of laptop may define the product laptop as given here and use the normative metadata terms associated with this class in WSD document or web content to assimilate the information into semantic web framework. The vendors may associate names with product range for a given processor.

The OWL Inverse Functional Property implies that if property P is applied to a <subject, object> tuple such that P and P then y=z. Here in this example a user query such as: "laptop with 2.0 GHz speed" may be converted to a search for the Laptop vendors with support for processor speed or corresponding processor number. The search for normative metadata terms LaptopProcessorNumber and LaptopProcessorSpeed will be converted to application of these Inverse Functional Properties on tuples and . Thus all web pages and web services found on the WWW for these properties (normative metadata terms) and the given values can be considered to describe "Laptop" product and may be returned in the query result. Example: all vendors found for LaptopProcessorNumber = T2500 will be considered as laptop vendors.

The product vendors may use different subset of normative metadata terms to describe the products. The web content provider can find the necessary normative metadata terms from the basic ontology provided by the standards organization. These ontologies may also be used by other agencies to define standards for products to be used within an organization. The web 3.0 search applications may have different search algorithms and hence define different ontologies for the normative metadata terms. The challenge for web 3.0 search application developers will be to define the most optimized and efficient algorithm and ontologies.

Conclusion: The provision of normative metadata terms by standards organization will provide the flexibility to define different ontologies with these normative metadata terms. In order to be able to do so the ontologies provided by the standards organization must provide the basic ontology for normative metadata that is extensible by the users of the ontology. The ontology can be extended by using "rdf:about attribute".

Note: Speed class may have properties as value and clock speed.

IT Copywrite

Simple steps to add RDFa to web content

Title of this post

Title of this post

Title of this post

Title of this post

Title of this post

View RDFa on this web page

Validate RDFa

Search strategies to explore the invisible web

Linked Data for Search Engine Optimization

Open Issues:

FOAF a factor for strong authentication

Advantages & risks of FOAF file

Strong authentication with FOAF

Is Calais an alternative to RDFa annotation?

What Calais web service does?

What should Calais web service user do?

What are the advantages of Calais web service?

Analysis of consumer sentiments for context data

New Ontology

Example

Product Context Data

SPARQL query

Consumer requirement for "open market"

Semantic Annotation of Blogs in Web 3.0

User interface for semantic annotated blog post

Elimination of redundant metadata

Advantages of standard ontology

Blogs in Web 3.0

Blogs in Web 3.0

Web Resource Classification for Search Engine Taxonomy

Why use POWDER for web resource description?

Consumer personal and context data security concerns

What user data is collected by the website?

What will web service provider do with this data?

What are the risks in sharing data?

What are the advantages of sharing data?

How can provider inform consumer about the data that is collected?

How can consumer control collection of data?

How does provider secure collected data?

Data Security in Semantic Web

Encoding normative metadata context

Normative metadata for services

Extensibility of normative metadata ontology