xml on semantic web. outline the semantic web ontology xml probabilistic dtd references
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XML on Semantic Web
Outline
The Semantic Web Ontology XML Probabilistic DTD References
The Semantic Web (1/4)
The first generation Web The second generation Web: current Web The third generation Web: Semantic Web The conceptual structuring of the Web in an explicit
machine-readable way Requirements: Universal expressive
power、 Support for syntactic Interoperability、 Support for Semantic Interoperability
The Semantic Web (2/4)
Syntactic interoperability talks about parsing the data, and semantic interoperability means to define mappings between unknown terms and known terms in the data
Semantic interoperability: requires standards syntactic form of document and semantic content
A further representation and inference layer is needed on top of the currently available layers of the WWW: Ontology
The Semantic Web (3/4)
The Semantic Web (4/4)
Ontology (1/5)
An explicit machine-readable specification of a shared conceptualization
Crucial role: representation of a shared conceptualization of a particular domain
reusable find pages that contain syntactically different but
semantically similar words Construct: concepts (which are usually organized
by taxonomies), relations, functions, axioms, instances
Ontology (2/5)
Ontology (3/5)
Concepts:– Be anything about which something is said– Also known as classes (XOL, RDF(s), OIL, DAML
+OIL), objects (OML), categories (SHOE) Taxonomies:
– used to organize ontological knowledge using generalization and specialization relationships through which simple and multiple inheritance could be applied
Ontology (4/5)
Relations and functions:– An interaction between concepts of the domain an
d attributes– Be called relations in SHOE、 OML, roles in OIL– Functions are a special kind of relation
Axioms:– Constraining information, verifying correctness, d
educting new information– Also known as assertions (OML), rule, logic
Ontology (5/5)
Instances:– Represent elements in th
e domain attached to a specific concept
Measurement of the expressiveness:
– XOL, RDF(s), SHOE, OML, OIL, DAML+OIL
XML (1/7)
As a serialization syntax for other markup language, ex: SMIL、 XOL、 SHOE
As semantic markup of Web-pages As a uniform data-exchange format
XML (2/7)
Universal expressive power: anything can be encoded in XML if a grammar can be defined for it
Syntactic interoperability: XML parser can parse any XML data and is usually a reusable component
Semantic interoperability: there is no way of recognizing a semantic unit from a particular domain of interest (not yet widely recognized)
XML (3/7)
XML (4/7)
Data exchange:– Build a model of the domain of interest– From the domain model a DTD or an XMLs is constructed
Advantage: reusability of the parsing software components
There exists multiple possibilities to encode a given domain model into a DTD, so the direct connection from the DTD to the domain model is lost and it cannot be easily reconstructed
XML (5/7)
XML (6/7)
A direct mapping based on the different DTDs is not possible
So we have to define the mappings between the different domain models, then between the different DTDs:
– Reengineering of the original Domain Model from the DTD or XML Schema
– Establishing mappings between the entities in the domain model
– Defining translation procedures for XML Documents Using a more suitable formalism than pure XML can
save much of the additional effort
XML (7/7)
Probabilistic DTD(1/11)
Describes the most likely orderings of XML tags and that contains statistical properties for each tag
Utilize association rule discovery algorithm and sequence mining techniques
Probabilistic DTD (2/11)
Objectives: tagging all text documents and deriving an appropriate preliminary flat XML DTD– A knowledge discovery in textual databases
(KDT) process to build clusters of semantically similar text units and then new documents can be converted into XML documents
Probabilistic DTD (3/11)
UML schema: are initially conceived by experts serves as a reference for the DTD, but there is no guarantee that the final DTD will be contained in or contain this schema
KDT process:– Tagging initial text documents– Domain knowledge constitutes such as thesaurus、 preliminary
UML schema, input to process– Pre-processing– Iterative clustering– Post-processing– Establishing a probabilistic DTD
Probabilistic DTD (4/11)
Probabilistic DTD (5/11)
Pre-processing:– Setting the level of granularity– NLP processing such as
tokenization、 normalization、 word stemming– Building text unit descriptors—a reduced feature
space(now are chosen by engineer)– Mapping all text units into Boolean vectors of this
feature space– Extract named entity
Probabilistic DTD (6/11)
Clustering:– Performed in multiple iterations, each iteration
outputs a set of clusters– All text unit vectors are clustered– Partition clusters into “acceptable” and
“unacceptable” according to quality criteria– Members of “unacceptable” are input data to the
next iteration
Probabilistic DTD (7/11)
Post-processing:– “acceptable” clusters are semi-automatically
assigned a label– Ultimately, cluster labels are determined by the
engineer– All default cluster labels are derived from text unit
descriptors– Automatically derived XML DTD from XML tags
Probabilistic DTD (8/11)
Probabilistic DTD (9/11)
Establishing a probabilistic DTD:– Deriving the most likely ordering of the tags– Computing the statistically properties of each tag
inside the document type definition
Deriving the ordering of the tags– Backward Construction of DTD Sequences:
builds “maximal” sequences– Forward sequence construction
Probabilistic DTD (10/11)
Backward Construction of DTD Sequences– Starts with an arbitrary tag ح and then identifies the tag most lik
ely to appear before it– If no such tag exists, then shifts to the next sequence. If there is
one, then the next iteration starts. If there are k tags, then duplicates k incomplete sequences.
– Each tag Xi leading to ح with a confidence Ci
– If there is a Ci larger than the others, then Xi is the predecessor of ح in the sequence
– If C0 where is the confidence where ح has no predecessor is largest, then ح is the first element
– Confidence is the tag’s TagSupport multiplied by the accuracy
Probabilistic DTD (11/11)
References
The Semantic Web—on the respective Roles of XML and RDF
– Stefan Decker, Frank van Harmelen, Jeen Broekstra, Michael Erdmann, Dieter Fensel, Ian Horrocks, Michel Klein, Sergey Melnik
Intelligent Information Agent with Ontology on the Semantic Web
– Weihua Li
Ontology Languages for the Semantic Web– Asuncion Gomez-Perez, Oscar Corcho
Extraction of Semantic XML DTDs from Texts Using Data Mining Techniques
– Karsten Winkler, Myra Spiliopoulou