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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 and attributes Be called relations in SHOE、OML, roles in OIL Functions are a special kind of relation Axioms: – – Constraining information, verifying correctness, deducting new information Also known as assertions (OML), rule, logic Ontology (5/5) Instances: – Represent elements in the 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 ﺡand then identifies the tag most likely 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 ﺡwith a confidence Ci If there is a Ci larger than the others, then Xi is the predecessor of ﺡin the sequence If C0 where is the confidence where ﺡhas no predecessor is largest, then ﺡis 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 – Intelligent Information Agent with Ontology on the Semantic Web – Weihua Li Ontology Languages for the Semantic Web – Stefan Decker, Frank van Harmelen, Jeen Broekstra, Michael Erdmann, Dieter Fensel, Ian Horrocks, Michel Klein, Sergey Melnik Asuncion Gomez-Perez, Oscar Corcho Extraction of Semantic XML DTDs from Texts Using Data Mining Techniques – Karsten Winkler, Myra Spiliopoulou