The document discusses the Semantic Web and declarative knowledge representation in information technology. It provides an introduction to key concepts including semantics, ontologies, rules, and logic-based knowledge representation. It also outlines technologies that make up the Semantic Web such as RDF, RDF Schema, OWL, and SPARQL. The goal of these technologies is to represent information on the web in a structured, machine-readable format in order to enable automated processing of data.
Ontop: Answering SPARQL Queries over Relational DatabasesGuohui Xiao
We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL 2 QL and RDFS ontologies), and its support for all major relational databases.
A tutorial on how to create mappings using ontop, how inference (OWL 2 QL and RDFS) plays a role answering SPARQL queries in ontop, and how ontop's support for on-the-fly SQL query translation enables scenarios of semantic data access and data integration.
Ontop: Answering SPARQL Queries over Relational DatabasesGuohui Xiao
We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL 2 QL and RDFS ontologies), and its support for all major relational databases.
A tutorial on how to create mappings using ontop, how inference (OWL 2 QL and RDFS) plays a role answering SPARQL queries in ontop, and how ontop's support for on-the-fly SQL query translation enables scenarios of semantic data access and data integration.
AI - Artificial Intelligence - Implications for LibrariesBrian Pichman
What does the world of AI (artificial intelligence) mean for libraries? Can AI replace library services or how can libraries leverage the technology for more streamlined services. From Smart Houses, to Robots, to technology yet to be mainstreamed, this session will cover it all to help you better prepare and plan for the future.
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
There are various Information Literacy Standards & Models.
The Aim of these S&M are to enable persons to acquire the necessary competencies and become Information Literate citizens.
The Standards provide a means to provide key milestones for students and assess their skill level.
Presentation about AI and Libraries. Why should libraries follow technology and be the main information provider and how innovating libraries can reach the AI audience and the increased need for data and information.
presentation on "CATALOGUING" during Training workshop in library science for staff of muktangan school libraries organised by muktangan school teacher reference library, mumbai on 15th November 2010
16 грудня 2021 року в Тернопільській ОУНБ відбувся семінар директорів та керівників центральних бібліотек територіальних громад «Сучасна публічна бібліотека: завдання, роль та місце в громаді». До семінару, який відбувався у форматі онлайн, доєдналося понад 60 учасників.
З проєктом «Зведений каталог публічних бібліотек Тернопільської області» ознайомила усіх присутніх Любов Кульчицька — завідувачка відділу обробки літератури та організації каталогів Тернопільської обласної універсальної наукової бібліотеки.
Library education was initially a technical education that was acquired on the job. Practical work in a library, based on a good education in schools, was the only way to train librarians.
It took quite a long time to introduce library education as a subject and has been taught at different levels in the universities of the world.
Information Extraction and Linked Data CloudDhaval Thakker
In the media industry there is a great emphasis on providing descriptive metadata as part of the media assets to the consumers. Information extraction (IE) is considered an important tool for metadata generation process and its performance largely depend on the knowledge base it utilizes. The advances in the “Linked Data Cloud” research provide a great opportunity for generating such knowledge base that benefit from the participation of wider community. In this talk, I will discuss our experiences of utilizing Linked Data Cloud in conjunction with a GATE-based IE system.
AI - Artificial Intelligence - Implications for LibrariesBrian Pichman
What does the world of AI (artificial intelligence) mean for libraries? Can AI replace library services or how can libraries leverage the technology for more streamlined services. From Smart Houses, to Robots, to technology yet to be mainstreamed, this session will cover it all to help you better prepare and plan for the future.
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
There are various Information Literacy Standards & Models.
The Aim of these S&M are to enable persons to acquire the necessary competencies and become Information Literate citizens.
The Standards provide a means to provide key milestones for students and assess their skill level.
Presentation about AI and Libraries. Why should libraries follow technology and be the main information provider and how innovating libraries can reach the AI audience and the increased need for data and information.
presentation on "CATALOGUING" during Training workshop in library science for staff of muktangan school libraries organised by muktangan school teacher reference library, mumbai on 15th November 2010
16 грудня 2021 року в Тернопільській ОУНБ відбувся семінар директорів та керівників центральних бібліотек територіальних громад «Сучасна публічна бібліотека: завдання, роль та місце в громаді». До семінару, який відбувався у форматі онлайн, доєдналося понад 60 учасників.
З проєктом «Зведений каталог публічних бібліотек Тернопільської області» ознайомила усіх присутніх Любов Кульчицька — завідувачка відділу обробки літератури та організації каталогів Тернопільської обласної універсальної наукової бібліотеки.
Library education was initially a technical education that was acquired on the job. Practical work in a library, based on a good education in schools, was the only way to train librarians.
It took quite a long time to introduce library education as a subject and has been taught at different levels in the universities of the world.
Information Extraction and Linked Data CloudDhaval Thakker
In the media industry there is a great emphasis on providing descriptive metadata as part of the media assets to the consumers. Information extraction (IE) is considered an important tool for metadata generation process and its performance largely depend on the knowledge base it utilizes. The advances in the “Linked Data Cloud” research provide a great opportunity for generating such knowledge base that benefit from the participation of wider community. In this talk, I will discuss our experiences of utilizing Linked Data Cloud in conjunction with a GATE-based IE system.
Toward Semantic Representation of Science in Electronic Laboratory Notebooks ...Stuart Chalk
An electronic laboratory Notebook (ELN) can be characterized as a system that allows scientists to capture the data and resources used in performing scientific experiments. This allows users to easily organize and find their data however, little information about the scientific process is recorded.
In this paper we highlight the current status of progress toward semantic representation of science in ELNs.
Resource Description Framework Approach to Data Publication and FederationPistoia Alliance
Bob Stanley, CEO, IO Informatics, explains the utility to RDF as a standard way of defining and redefining data that could have utility in managing life science information.
Semantics in Financial Services -David NewmanPeter Berger
David Newman serves as a Senior Architect in the Enterprise Architecture group at Wells Fargo Bank. He has been following semantic technology for the last 3 years; and has developed several business ontologies. He has been instrumental in thought leadership at Wells Fargo on the application of Semantic Technology and is a representative of the Financial Services Technology Consortium (FSTC)on the W3C SPARQL Working Group.
Semantic Web: Technolgies and Applications for Real-WorldAmit Sheth
Amit Sheth and Susie Stephens, "Semantic Web: Technolgies and Applications for Real-World," Tutorial at 2007 World Wide Web Conference, Banff, Canada.
Tutorial discusses technologies and deployed real-world applications through 2007.
Tutorial description at: http://www2007.org/tutorial-T11.php
These slides were presented as part of a W3C tutorial at the CSHALS 2010 conference (http://www.iscb.org/cshals2010). The slides are adapted from a longer introduction to the Semantic Web available at http://www.slideshare.net/LeeFeigenbaum/semantic-web-landscape-2009 .
A PDF version of the slides is available at http://thefigtrees.net/lee/sw/cshals/cshals-w3c-semantic-web-tutorial.pdf .
Keynote presentation delivered at ELAG 2013 in Gent, Belgium, on May 29 2013. Discusses Research Objects and the relationship to work my team has been involved in during the past couple of years: OAI-ORE, Open Annotation, Memento.
Explanations in Dialogue Systems through Uncertain RDF Knowledge BasesDaniel Sonntag
We implemented a generic dialogue shell that can be configured for and applied to domain-specific dialogue applications. The dialogue system works robustly for a new domain when the application backend can automatically infer previously unknown knowledge (facts) and provide explanations for the inference steps involved. For this purpose, we employ URDF, a query engine for uncertain and potentially inconsistent RDF knowledge bases. URDF supports rule-based, first-order predicate logic as used in OWL-Lite and OWL-DL, with simple and effective top-down reasoning capabilities. This mechanism also generates explanation graphs. These graphs can then be displayed in the GUI of the dialogue shell and help the user understand the underlying reasoning processes. We believe that proper explanations are a main factor for increasing the level of user trust in end-to-end human-computer interaction systems.
Tutorial - Introduction to Rule Technologies and SystemsAdrian Paschke
Tutorial at Semantic Web Applications and Tools for the Life Sciences (SWAT4LS 2014), 9-11 Dec., Berlin, Germany
http://www.swat4ls.org/workshops/berlin2014/
Semantic CEP with Reaction RuleML, Keynote at 8th International Web Rule Symposium (RuleML 2014) @ ECAI 2014, Prague, Czech Republic, August 18-22, 2014
Loomp - Web 3.0 Collaborative Semantic Content AnnotatorAdrian Paschke
User study about semantic content annotation presented at Xinnovations 2012, September 2012 and at ISWC 2012 http://iswc2012.semanticweb.org/sites/default/files/76490161.pdf
The RuleML Perspective on Reaction Rule StandardsAdrian Paschke
Presentation about Reaction RuleML at the Ontology, Rules, and Logic Programming for Reasoning and Applications (RulesReasoningLP) Session at the Ontolog Forum, 9 January 2014
PragmaticWeb 4.0 - Towards an active and interactive Semantic Media WebAdrian Paschke
Keynote at W3C Regional Event - Aspects of Semantic Technologies; Fachtagung Semantische Technologien26.-27. September 2013 | HU Berlin
http://semantic-media-web.de/referenten/?detail=33
Semantic Complex Event Processing with Reaction RuleML 1.0 and Prova 3.0Adrian Paschke
Seminar presented by Visiting Professor Adrian Paschke from Freie Universitaet Berlin as part of the BPM EduNet (http://bpmedu.net) staff exchange at University of Toronto, McGill University, Ryerson University, University of Ontario Institute of Technology
Seminar about Semantic Complex Event Processing and Reaction RuleML presented at the School of Computer Science at McGill University on Sept. 9th, 2013 as part of the Transatlantic Business Process Management Education Network (http://bpmedu.net/) and presented at the DemAAL 2013 - Dem@Care Summer School on Ambient Assisted Living, 16-20 September 2013, Chania, Crete, Greece.
Tutorial: Deliberation RuleML, Reaction RuleML, and LegalRuleML: Specification and Application - Part 2: Adrian Paschke - Reaction RuleML; in Adrian Paschke, Harold Boley, Zhili Zhao, Kia Teymourian and Tara Athan. Reaction RuleML 1.0: Standardized Semantic Reaction Rules, 6th International Conference on Rules (RuleML 2012) ECAI 2012, Montpellier, France, August 27-31, 2012.
Semantic Complex Event Processing at Sem Tech 2010Adrian Paschke
Semantic Complex Event Processing - The Future of Dynamic IT
Presentation by Paul Vincent, Adrian Paschke, Harold Boley
at the RuleML Semantic Rules Track of the Semantic Technologies Conference 2010 (SemTech 2010), San Francisco, CA, USA
http://semtech2010.semanticuniverse.com/rules
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
1. Pragmatic Web 4.0
Kommission „Die Natur der Information“
Göttinger Akademie, 8.11.2013
Prof. Dr. Adrian Paschke
Corporate Semantic Web (AG-CSW)
Institut für Informatik, Freie Universität Berlin
paschke@inf.fu-berlin
http://www.inf.fu-berlin/groups/ag-csw/
and
Department of Information Systems
Poznan University of Economics
paschke@inf.fu-berlin
2. Agenda
What is Semantics?
Declarative Knowledge
Representation in IT
The Semantic Web – An
Introduction
Semantic Web and it’s Relations
What comes next?
3. What is Semantics?
The Problem of Machine Meaning
Interpretation and Machine
Understanding
4. Data vs. Information
Data
A “given,” or fact; text, a number, a statement, or a
picture, …
The raw materials in the production of information
Information
Data that has been put into a meaningful and useful
context.
5. Example Data vs. Information
data
95
information My score on the final exam is
95%
knowledge I have passed the exam with
excellent mark bdb
data
representation,
e.g. relational
DB
data + context
+ information
representation
data /
information +
meaning
interpretation
6. Search Results from Publication
Database
Title
Lorenz P,
Transcriptional repression
mediated by the KRAB domain of the human
Author
C2H2 zinc finger protein Kox1/ZNF10 does not
require histone deacetylation.
Biol Chem. 2001 Apr;382(4):637-44.
Fredericks WJ. An engineered PAX3-KRAB
transcriptional repressor inhibits the malignant
Journal
Year
phenotype of alveolar rhabdomyosarcoma
cells harboring the endogenous PAX3-FKHR
oncogene.
However, for a machine things look different!
Mol Cell Biol. 2000 Jul;20(14):5019-31.
7. Results from Publication Database
Lorenz P, Transcriptional repression
mediated by the KRAB domain of the
human C2H2 zinc finger protein
Kox1/ZNF10 does not require histone
deacetylation.
Biol Chem. 2001 Apr;382(4):637-44.
Fredericks WJ. An engineered PAX3KRAB transcriptional repressor inhibits
the malignant phenotype of alveolar
rhabdomyosarcoma cells harboring the
endogenous PAX3-FKHR oncogene.
Mol Cell Biol. 2000
Solution:
Tags (XML)?
Jul;20(14):5019-31.
8. Results from Publication Database
<author>Lorenz P</author><title>Transcriptional repression
mediated by the KRAB domain of the human C2H2 zinc finger
protein Kox1/ZNF10 does not require histone deacetylation.
</title>
<journal>Biol Chem </journal><year>2001<year>
<author>Lorenz P</author><title>Transcriptional repression
mediated by the KRAB domain of the human C2H2 zinc finger
protein Kox1/ZNF10 does not require histone deacetylation.
</title>
<journal>Biol Chem </journal><year>2001<year>
However, for a machine things look different!
...
9. Results from Publication Database
<author>Lorenz
P</author><title>Transcriptional
repression mediated by the KRAB
domain of the human C2H2 zinc finger
protein Kox1/ZNF10 does not require
histone deacetylation. </title>
<journal>Biol Chem
</journal><year>2001<year>
<author>Lorenz
P</author><title>Transcriptional
repression mediated by the KRAB
domain of the human C2H2 zinc finger
protein Kox1/ZNF10 does not require
histone deacetylation. </title>
<journal>Biol Chem
</journal><year>2001<year>
Solution: Use Semantic
Knowledge
Representation
10. Example: Traffic Light
Syntax – Semantics - Pragmatics
Syntax
green (bottom); yellow; red
Semantics
green = go; …; red = stop
Pragmatics
If red and no traffic
then allowed to go
11. Example: Question-Answer Interaction
Syntax – Semantics - Pragmatics
Syntax
“What time is it?” (English)
Semantics
Question about current time (Meaning)
Pragmatics
An answer to the question is obligatory
(even if time is unknown) (Understanding
and Commitment)
12. Example - XML Syntax vs. Semantics
Adrian Paschke is a lecturer of Logic Programming
<course name=“Logic Programming">
<lecturer>Adrian Paschke</lecturer>
</course>
<lecturer name=“Adrian Paschke">
<teaches>Logic Programming</teaches>
</lecturer>
Opposite nesting (syntax), same meaning (semantics)!
13. Syntax – Semantics - Pragmatics
Syntax
about form
Semantics
about meaning
Pragmatics
about use.
16. Semantic Technologies for
Declarative Knowledge Representation
1. Rules
Describe derived conclusions if premium(Customer)
and reactions from given
then discount(10%)
information (inference)
2. Ontologies
equal
with
Customer
Ontologies represent the conceptual
knowledge of a domain (concept
semantics)
is a
Partner
Client
17. What is an Ontology? (in IT)
An Ontology is a
formal specification
Executable, Discussable
of a shared
Group of persons
conceptualization
About concepts; abstract class
of a domain of interest
e.g. an application, a specific
area, the “world model”
[Gruber 1993] - T.R. Gruber, Toward Principles for the Design of Ontologies Used for
Knowledge Sharing, Formal Analysis in Conceptual Analysis and Knowledge
Representation, Kluwer, 1993.
18. What is a Rule? (in IT)
1. Rules
•
•
Derivation rules (deduction rules): establish / derive new information
Reaction rules that establish when certain actions or effects should
take place :
• Condition-Action rules (production rules)
• Event-Condition-Action (ECA) rules + variants (e.g. ECAP).
• Messaging Reaction Rules (event message reaction rules)
2. Constraints
•
•
•
Structural constraints (e.g. deontic assignments).
Integrity constraints and state constraints
Process and flow constraints
[Paschke, A., Boley, H.]: Rule Markup Languages and Semantic Web Rule Languages, in Handbook of Research on Emerging Rule-Based
Languages and Technologies: Open Solutions and Approaches, IGI Publishing, ISBN:1-60566-402-2, 2009
19. Example: Ontology and Rules
Ontology
Object
is_a-1
Person
is_a-1
is_a-1
knows
has
Topic
described_in
Prior Art
Document
related_to
related_to
is_a-1
Patent
Application Priority
Skill
Patentee
Technique described_in
Teaching
writes
is_a-1
Patent
date
becomes
granted
RULES:
Topic
Patentee
writes
described_in
Patent
Application
Document
is_about
Topic
Topic
Patentee
Patentee
is_about
knows
has
Document
Topic
Skill
20. Ontologies and their relatives
informal
formal semantics
expressiveness
Based on AAAI’99 Ontologies Panel – McGuiness, Welty,
Ushold, Gruninger, Lehmann
21. Many Ontology Languages
No special ontolgy languages,
Entity Relationship Modell
but might be used to describe
ontologies
UML with OCL
Frames, F-Logic
Predicate Logic
Common Logic
formal logic
Description Logic
specialized
SHOE, XOL, OML, SKOS, OBO, SBVR, …
Web Ontology Languages
RDFS, DAML+OIL -> OWL
ODM
Ontology Transformation
…
22. Logic and Knowledge Representation
in IT
Declarative Knowledge Representation
express what is valid, the responsibility to interpret
this and to decide on how to do it is delegated to an
automated interpreter / reasoner
Formal logic-based languages for the
representation of knowledge with a clear
semantics
23. Main Requirements of a Logic-based
Ontology / Rule Language in IT
a well-defined syntax
a formal semantics
sufficient expressive power
efficient reasoning support
convenience/adequacy of
expression syntax
24. Logic-based Knowledge Representation
First Order Logic
Expressive syntax: constants, functions, predicates, equality,
quantifiers, variables
Objects and relations are semantic primitives represented as
predicate formula
But: reasoning not efficient and undecidable
Solution: Restriction to Subsets of FOL
Horn Logic (Logic Programming / Rules)
Descripition Logics (Ontologies)
But: convenience of expression: only formal syntax +
semantics, but not a Web representation format
=> Semantic Web Syntax and Semantic Web Data Model needed
26. Semantic Web – An Introduction
"The Semantic Web is an
extension of the current web in
which information is given welldefined meaning, better
enabling computers and people
to work in cooperation."
Tim Berners-Lee, James Hendler,
Ora Lassila, The Semantic Web
„Make the Web understandable
for machines“
W3C Stack 2007
27. Main Building Blocks of the
Semantic Web
1.
2.
3.
4.
Explicit Metadata on the WWW
Ontologies
Rule Logic and Inference
Semantic Tools ,Semantic Web Services,
Software Agents
28. The (current) W3C Semantic Web Stack
Ontologies
RDF Query
Language
Rules
Semantic Web
Information
Model
Standard
Internet
Technologies
W3C Semantic Web Stack since 2007
29. Overview on the Semantic Web
Technologies
URI/IRI: Web Resource Identifiers
Note: Representational State Transfer (REST) – Resources are
abstraction from an information/knowledge (e.g. „weather in Göttingen“)
XML: Syntactic basis
RDF: Resource Description Framework
RDF as Web data model for facts and metadata
RDF schema (RDFS) as simple ontology language
(mainly taxonomies)
SPARQL as a RDF query language
Linked Data – data publishing method
30. Overview on the Semantic Web
Technologies (2)
Ontology
RDF Schema (RDFS) and Web Ontology Language (OWL)
Rules / Logic
Rule Interchange Format (RIF, RuleML)
Proof
Generation of proofs-, interchange of proofs, validation
Trust
Digital signatures
recommendations, ratings
Semantic Web Applications & Interfaces
e.g. Semantic Search, Semantic Agents, …
40. Metadata Problem Domains
Syntax:
Which representation and interchange format for
metadata? (Microformats, RDF, RDFa, Microdata)
Semantics:
Which metadata are allowed for Web resources
(expressiveness, metadata vocabulary, schema)
Association problem:
How to connect metadata with resources? (who
defines the metadata, are metadata separated
from the content (RDF vs. RDFa), etc.)
41. The W3C Semantic Web
Architecture
W3C Semantic Web Stack since 2007
42. RDF Triple Stores
A specialized database for RDF triples
Supports a query language
SPARQL is the W3C recommendation
Triple stores might be in memory or provide a
persistent backend
Presistence provided by an underlying relational DBMS
(e.g., mySQL) or a custom DB for efficiency.
43. Example: SPARQL SELECT
SELECT:
SELECT Variables
FROM Dataset
WHERE Pattern
Examples:
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name
WHERE ( ?x foaf:name ?name )
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT *
WHERE ( ?x foaf:name ?name )
44. The (current) W3C Semantic Web
Architecture
W3C Semantic Web Stack since 2007
47. OWL vs. RDFS
More Expressiveness
Definition of relations between classes
Definition of constraints and cardinalities
Constraints on properties: exists, forall, cardinality
Definition of equivalences between classes (e.g.
different ontologies)
Properties of properties
Boolean combinations of classes and constraints
…
48. Example: OWL Ontology
peopleAtUni
range
id
Student
involves
domain
domain
Course
OWL
Staff Member
range
domain
subClassOf
equivalentClass
subPropertyOf
isTaughtBy
phone
domain
unionOf
T-Box
Model
range
Literal
1
Faculty
Academic Staff Member
subClassOf
range
subClassOf
subClassOf
disjointWith
Professor
Assistant
Professor
Associate
Professor
rdf:type
rdf:type
RDF
isTaughtBy
Semantic Web
Adrian Paschke
A-Box
Model
49. Reasoning with OWL
Semantics of OWL is defined by Description Logics (DL)
Satisfiability: whether the assertions in an TBox and ABox has
a model (i.e. non-contradicting)
Subsumption: whether one description is more general than
another one
Equivalence: whether two classes denote same set
Consistence: whether its set of assertions is consistent
Instantiation: check if an individual is an instance of class C
Retrieval: retrieve a set of individuals that instantiate C
50. The (current) W3C Semantic Web
Architecture
W3C Semantic Web Stack since 2007
52. Example: Rule Markup Language
Standards (RuleML)
RuleML 1.0 (Deliberation, Reaction, Defeasible, Modal, …)
Semantic Web Rule Language (SWRL)
Uses RuleML Version 0.89
Semantic Web Services Language (SWSL)
Uses RuleML Version 0.89
W3C Rule Interchange Format (RIF)
Uses RuleML Version 0.91 with frames and slots
OASIS LegalRuleML
Uses RuleML Version 1.0
OMG Production Rules Representation (PRR)
Input from RuleML
OMG Application Programming Interfaces four KBs (API4KB)
Input from Reaction RuleML 1.0
53. Unifying Logic
• Not standardized in W3C Semantic Web Stack yet
• Which semantics? (e.g., Description Logics, F-Logic, Horn Logic, …)
• Which assumptions? (e.g., Closed World, Open World, Unique Name)
• …
W3C Semantic Web Stack since 2007
54. Example Decision Scenario
You need to wait if the
traffic light is not green.
I know that the traffic light
is green, so I’m allowed to
cross the street
I’m not sure if the traffic
light is green, so I’m
allowed to cross the street
????
55. Open World vs. Closed Word
Assumption
Open World Assumption (typical for ontologies)
explicitly prove the truth of negation
Closed World Assumption (typical for rules / logic programs)
if we do not know (from our closed knowledge base) we assume
falsity
This difference has practical implications
Traffic light example:
Under open world assumption we need to explicitly
prove that the light is not red => cross street
Under closed world assumption it is enough if we
prove that there is no information if the light is red
=> cross street
Who is responsible if an accident happens?
56. Unique-Names Assumption
isTaughtBy
domain
Course
range
1
Academic Staff Member
A course is taught by at most one staff member.
The course „Ontologies in IT“ is taught by
„Prof. Paschke“ and „Prof. Wecel“
OWL does not adopt the unique-names assumption
of database systems/logic programs (rules)
If two instances have a different name or keys/IDs does not
imply that they are different individuals
An OWL reasoner does not flag an error
Instead it infers that the two resources are equal
A rule reasoner / deductive database does flag an error
57. Proof and Trust
• Proof Markup Languages, Justifications and Argumentations
• Claims can be verified, if there are evidences from other (trusted) Internet
sources
• Semantic Reputation Models
58. Example Scenario – eCommerce
E-Shop
Review
Relying Party
Reseller Bob
Delivery
Service
Buyer
Monitored
Delivery
Performance
Business
Owner/Seller/Factory
used for service
management
used for buying
decisions
Semantic
Reputation Object
Semantic Web Reputation and Trust Management
http://www.corporate-semantic-web.de/rule-responder.html
Other Buyers
59. Use Cases / Applications / Tools
Semantic-enriched Search
Content management
Knowledge management
Business intelligence
Collaborative user interfaces
Sensor-based services
Linking virtual communities
Grid infrastructure
Multimedia data management
Semantic Web Services
Etc. see e.g.SWEO’s use case collection
http://www.w3.org/2001/sw/sweo/public/UseCases/
63. Example:
Semantic Desktop Systems
Combine desktop systems with Semantic Web
Technologies
Extract, manage, visualize and use semantic and
contextual associations respectively metadata for
Personal Information Management (PIM)
e.g. Gnowsis, Nepomuk, Beagle++, Social Semantic Desktop, Haystack
64. Example: Job Portal
Semantic Recommendation
d (Java, C++) = d (Java, Object Oriented) + d (C++, Object Oriented)
= (0.25-0.0.0625) + (0.25-0.0625)
= 0.375
sim(Java, C++) = 1 – 0.375 = 0.625 (Semantic Similarity is 0,625)
Example:
Query „Job offers for Java Programmer“ + expanded with Personal Skill Profile (Java +
C++ Knowledge)
=> also recommend job offers for C++ programmer
(see Semantic Matchmaking Framework: http://www.corporate-semantic-web.de/technologies.html
69. Example: OMG Ontology Definition Metamodel (ODM)
Ontology Definition Metamodel
MOF
MOF XMI
Of UML
MOF XMI
Of ODM
UML
ODM
User
UML Model
UML XMI
Of User Model
User
Ontology
Ontology XMI
Of User Model
ISO
Topic Maps
M2
M1
User
Instances
UML 2
(+OCL)
M3
M0
ISO
CL
W3C
RDFS
W3C
OWL
ODM brings together the communities (SE+KR) by providing:
Broad interoperation within Model Driven Architecture
MDA tool access to ontology based reasoning capability
UML notation for ontologies and ontological interpretation of UML
70. Example: Rule Markup Language
Standards (RuleML)
RuleML 1.0 (Deliberation, Reaction, Defeasible, Modal, …)
Semantic Web Rule Language (SWRL)
Uses RuleML Version 0.89
Semantic Web Services Language (SWSL)
Uses RuleML Version 0.89
W3C Rule Interchange Format (RIF)
Uses RuleML Version 0.91 with frames and slots
OASIS LegalRuleML
Uses RuleML Version 1.0
OMG Production Rules Representation (PRR)
Input from RuleML
OMG Application Programming Interfaces four KBs (API4KB)
Input from Reaction RuleML 1.0
71. Social Semantic Web
The concept of the Social Semantic Web
subsumes developments in which social
interactions on the Web lead to the creation
of explicit and semantically rich knowledge
representations. (Wikipedia)
72. Corporate Semantic Web
Corporate Semantic Web (CSW) address
the applications of Semantic Web
technologies and Knowledge Management
methodologies in corporate environments
(semantic enterprises).
(www.corporate-semantic-web.de)
73. Corporate Semantic Web
Public Semantic Web
Corporate Semantic Web
Business Context
Corporate
Semantic
Engineering
Corporate
Semantic
Search
Corporate
Semantic
Collaboration
Corporate Business Information Systems
74. Pragmatic Web
The Pragmatic Web consists of the tools,
practices and theories describing why and how
people use information. In contrast to the
Syntactic Web and Semantic Web the Pragmatic
Web is not only about form or meaning of
information, but about interaction which brings
about e.g. understanding or commitments.
(www.pragmaticweb.info)
75. Pragmatic Agent Web
The Pragmatic Agent Web utilize the Semantic Web with
multiple interacting intelligent agents which collaborate on
the Web and put independent meta data, ontologies and
local data into a pragmatic context such as communicative
situations, organizational norms, purposes or individual
goals and values.
Duration & Connectedness
Intelligence
Knowledge
Pragmatic
Semantic
Information
Syntax
Data
(Machine) Understanding
76. Pragmatic Agent Web (2)
Utilize the heterogenous Semantic Web resources, meta data and
meaning representations with intelligent agents and web-based services
with the ability to understand the others intended meaning (pragmatic
competence)
Formal Logic Representation vs. (Controlled) Natural Language Representation
Collaborate in a communicative conversation-based process where
content and context is interchanged in terms of messages (relation of
signs) between senders and receivers (interpreters/users).
Loosley-coupled vs. de-coupled interactions
Fixed negotiation and coordination protocols vs. free conversations
Pragmatic layer/wrapper around semantic/content e.g. by KQML / ACL
like speech-act primitives (e.g. assert(content), retract(content), query(kb))
Model, negotiate and control shared and invividual meanings
requires learning and knowledge adaption / updates
78. Challenges for the Semantic Web
Connectedness
Intelligence / Wisdom
understanding
principles
Pragmatics
Knowledge
Understanding
patterns
Sematics
Information / Content
Understanding relations
Data
Ontologies
(Logic)
Rules
(Logic)
Syntax
???
(Human Logic +
Machine Logic)
Understanding
79. Ubiquitous Pragmatic Web 4.0
Pragmatic Agent
Ecosystems
Machine
Understanding
Situation Aware Real-time Semantic
Complex Event Processing
Ubiquitous Pragmatic Web 4.0
Pragmatic Web
Connects Intelligent Agents and Smart Things
Massive
Multi-player Web Gaming
Ubiquitous autonomic
Smart Services and
Things
Smart Web TV
Social Semantic Web 3.0,
Web of Services & Things,
Corporate Semantic Web Connects
Semantic Web
Smart Content
People, Services and Things
Semantic Web 2.0
Connects Knowledge
Syntactic Web
World Wide Web 1.0
Smart Content
Passive
Active
Desktop Computing
Syntactic
Web
Semantic
Web
Consumer
Smart
Agents
XML
RDF
Monolithic
Systems Era
HTML
Desktop
Content
Producer
Connects Information
Pragmatic
Web
Ubiquitous Next Generation Agents and Social Connections