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Transcript of 1 Semantic Web Jayasree, Jenny, Raj, Sunil. 2 Agenda What is Semantic Web vs Semantic History of...
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Semantic Web
Jayasree, Jenny, Raj, Sunil
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Agenda
What is Semantic Web vs Semantic History of Semantic Web Current Research Areas (eg OWL, RDF,
reasoning engines, query languages) Applications (eg. Medical Domain) Architecture/System components Challenges Future directions Reference
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What is Semantics vs. Semantic Web?
Semantics: focuses on the relation between signifiers, such as words, phrases, signs and symbols, and what
they stand for.
“Basically, what the sentence really means”
Semantic Web: The Semantic Web is data that enables machines to understand the meaning of information on
the Internet. It extends the network human-readable web pages by inserting machine-readable metadata and how they are related to each other. This enables automated agents to access the Web more intelligently and perform tasks on behalf of the user
“Using metadata to add (and extract) meaning”
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Understanding the Semantics
Computers don’t understand the website they are showing to us
They may understand the syntax, the semantics is not. If computers can understand what we are looking for,
they can help us search better From passively helping us, to actively helping us Understanding the meaning behind the Webpage
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What will Semantic Web Understand?
The computer will learn them and understand how they interact with each other.
Person Place Event Things
Currently it is dependant on Keywords
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History of Semantic Web
1989 by Sir Tim Berners-Lee
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Current Research Areas Representation Reasoning Engines Query Languages
Web Ontology Language - RDFS
RDF Schema RDF is a data model for objects and relations between
them RDF Schema (RDFS) is a vocabulary description
language based on XML and logic programming. Describes properties and classes of RDF resources Provides semantics for generalization hierarchies of
properties and classes
Web Ontology Language - OWL
OWL A richer ontology language based on description logic.
More expressive languages than RDF Schema. Is the current Web standard Relations between classes
e.g., disjointness Cardinality restrictions
e.g. “exactly one” Boolean expressions Richer typing of properties characteristics of properties (e.g., symmetry)
Web Ontology Language - DAML+OIL DAML was funded by US government. US Defense
Advanced Research Project Agency launched the DARPA Agent Markup Language to make web content more accessible.
Ontology Inference Layer (OIL), a description logic. DAML+OIL
developed by a joint committee from the US and the European Union (IST) in the context of DAML, a DARPA project for allowing semantic interoperability in XML.
DAML+OIL is built on RDF(S), extends with arbitrary data types from XML Schema type system.
More Ontology Languages
SHOE – Simple HTML Ontology Extension (University of Maryland)
OML – Ontology Markup Language (University of Washington) is partially based on SHOE, provides serialization of SHOE
XOL – Ontology Exchange Language (US bioinformatics community) for exchange of ontology definitions among different software systems.
Expressiveness of languages
XOL RDFS SHOE OML OIL DAML+OIL
Heavyweight
Querying Language – SPARQL
SPARQL is an RDF query language.
JENA Semantic Web Toolkit
Create and populate RDF data models in Java applications.
Persist them to database Query the models programmatically Interface with Ontology engine Interface with SPARQL
Reasoning Engine – Pellet OWL Reasoner for Java An open-source Java based OWL DL reasoner. Dual licensing model
Open source applications - can be used under the terms of AGPL version 3 license
Closed source/commercial applications – can be used under alternative license terms.
Execute SPARQL http://clarkparsia.com/pellet/
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Applications
Medical Domain Product recommendations in eCommerce?
A Medical Information Management System (MIMS) Uses Semantic Web Technologies Diagnosis method of “dementia” Data items are changed as research
progress.
Motivation for MIMSChallenges faced by Medical researchers in developing a method to diagnose dementia
Spends lot of time analyzing medical data in: questionnaire survey, metadata, MMSE (Mini-Mental State Examination) data, MRI (Magnetic Resonance Imaging) data, MEG (Magnetoencephalography) data, and physical checkup data. Some of them are saved as JPEG format,
DICOM (Digital Imaging and Communications in Medicine) format [1], Microsoft Excel format, Microsoft PowerPoint format and Adobe PDF format.
Stored in network file folders; not easy to retrieve
Complicated retrieval needs MMSE> 20, how to retrieve this info?
Semantic Web – The solution
A major hurdle of the Semantic Web is the creation of metadata.
The metadata creation is a tedious and laborious process for the researchers.
However, it is vital for articulating the medical data.
MIMS – Essential components
(1) A pluggable metadata extractor Metadata extraction mechanism that given as a plug-in which
automatically generates a set of metadata from medical data files.
(2) An RDFView A semantic Web retrieval mechanism which provides Web
application programming interfaces created from SPARQL templates dynamically.
(3) A representation mechanismA method to display a result of the semantic Web retrieval service.
MIMS (Contd…)
An overview of RDFViewAn RDF model for DICOM image files
An overview of the pluggable metadata extractor
The basic metadata schema using RDF graph
Overview of MIMS
Many other medical applications
SemMedApplying Semantic Web to Medical Recommendation Systems
Patient-Oriented Systems
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Architecture/System components XML layer
Syntactic basis RDF layer
RDF basic data model for facts RDF Schema
Ontology layer More expressive languages Current Web standard: OWL
Logic layer enhance ontology languages
further application-specific declarative
knowledge
Proof layer Proof generation, exchange,
validation Trust layer
Digital signatures recommendations, rating agencies.
Semantic Web Architecture
A complete database architecture, not only an application program. Semantic web architecture combines a two-step process.
First, a Semantic Web database is created from unstructured text documents.
And, then Semantic Web applications run on the Semantic Web database; not the original source documents.
The Semantic Web architecture is created by first converting text files to XML
Then analysis performed around these with a semantic processor. This process understands the meaning of the words and grammar of the sentence, and also the semantic relationships of the context.
These meanings and relationships are then stored in a Semantic web database. [13]
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Semantic Web Challenges
Classification of semantic web challenges:
Implementation Usage General
Semantic Web Challenges
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General Challenges
Multilingualism – to implement the same page in several languages with or without caching.
Social Hurdles – acceptability at management level. Treated as another mumbo jumbo.
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Execution Challenges
Visualization Scalability Services Trust
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Implementation challenges
Ontological Modeling Ontologies are key elements of semantic web. Since these are extensions into a different domain of
knowledge, creation and development becomes extremely hard.
Manual construction of Ontologies is a time consuming operation.
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Implementation Challenges
Ontology Engineering
ConstructionAlignmentMerging
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Implementation Challenges
Annotations and Metadata
Annotation of web contents is difficult.Though it exists, it is not consistent.
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Construction Challenges
Identifying and categorizing hierarchies of concepts.
Categorizing documents. Classifying concepts. Finding non-taxonomic relationship
between documents. Finding interrelated terms.
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Mapping Challenges
Finding proper metrics for mapping.
Creating new classes.
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Merging Challenges
Finding proper candidates for merging.
Finding proper metrics for merging once the candidates are identified.
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Future Directions
“Semantic Web” Used in Facebook for establishing relationships and
connections. Used in Learning Management Systems to classify
documents uniformly across all LMS. Use in Medical record management systems. More usages envisaged.
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References1. John Davies, “Semantic Web” presentation http://www.keapro.net/sekt/SemWebTutorialGeneralJD.ppt2. N. Henze and D. Krause, “Semantic Web Introduction”,
http://www.kbs.uni-hannover.de/Lehre/semweb06/all_in_one.pdf3. Charla Woodbury, David Embley, “Family History Research on the Semantic Web: Building a Semantic
Prototype for Danish Research”, http://fht.byu.edu/prev_workshops/workshop05/FHTCD/session1/s1-CharlaWoodbury_SemanticWeb.pdf
4. Deborah L. McGinnes et al, “DAML+OIL: An Ontology Language for the Semantic Web”, IEEE Intelligent Systems Journal, 2002
5. Asuncion Gomez-Perez and oscar Corcho, “Ontology Languages for the Semantic Web”, IEEE Intelligent Systems Journal, 2002
6. Mahmoud Barhamgi et al, “A framework for Data and Web Services semantic mediation in Peer-to-Peer based Medical Information System”, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS’06), 2006
7. Masaharu Hayashi et al, “A Medical Information Management System Using the Semantic Web Technology”, Fourth International Conference on Networked Computing and Advanced Information Management, 2008
8. MohammadReza Keyvanpour et al, “Comparative Classification of Semantic Web Challenges and Data Mining Techniques”, 2009 International Conference on Web Information Systems and Mining
9. Kees van der Sluijs, “Semantic Web Applications” presentation, http://wwwis.win.tue.nl/~ksluijs/material/wis-semwebapplications-class.ppt
10. Grigoris Antonious, Frank van Harmelen, “A Semantic Web Primer”, 2008, http://kianian.com/userfiles/semantic-web-primer.pdf
11. Philip McCarthy, “Introduction to Jena”, https://www.ibm.com/developerworks/java/library/j-jena/12. SPARQL Query Language for RDF, http://www.w3.org/TR/rdf-sparql-query/13. Semantic Web Architecture and Applications, http://www.oss.net