This document discusses ontology engineering tools. It begins by introducing Protégé as a free, open-source platform for constructing domain models and knowledge-based applications with ontologies. It provides a graphical user interface for developing RDF and OWL statements. The document then provides instructions for downloading and getting started with Protégé, focusing on using its OWL editor. It notes that Protégé has both frame-based and OWL modeling interfaces to support different ontology languages.
Gardner’s multiple intelligences planning grid with activity ideas and starte...Jacqui Sharp
This document provides a planning grid with activity ideas for each of Gardner's Multiple Intelligences. The grid lists verbs and starter words associated with each intelligence and provides examples of activities and tools that can be used to design lessons targeting each intelligence. It includes ideas for remembering, understanding, applying, analyzing, evaluating, and creating for the eight intelligences: verbal/linguistic, logical/mathematical, visual/spatial, bodily/kinaesthetic, musical, interpersonal, intrapersonal, and naturalist.
Formal Ontologies and Nyaya-Vaiseshika SystemNagaraju Pappu
This document discusses formal ontology and its role in specifying meanings for computational agents like robots. It covers:
1) The goal of formal ontology is to provide an entitative account of meanings that can recur in cognition and be expressed in language, in a way that can be automated.
2) Specifying meanings computationally involves representing concepts as classes that inherit from higher classes, and objects as having properties that allow for multiple inheritances.
3) Formal ontology seeks to develop foundational ontologies that can underpin domain ontologies and be expressed using formal logic, rather than natural language, to specify meanings for artificial agents.
4) The development of formal ontology involves applying formal methods from fields like topology
The document discusses sentence processing and comprehension. It covers topics like sentence and clause as processing units, case-role assignment and subject-verb processing, structurally related sentences like negatives and passives, factors in comprehension like pragmatics and semantics, parsing strategies, and models of comprehension like autonomous vs interactive processing. The conclusion summarizes that a sentence is a major processing unit, an interpreter relates subject and verb, and comprehension involves factors like structure, meaning, and context.
FOAF (Friend of a Friend) is a vocabulary for describing people, their activities and their relationships. It allows personal profile pages to be interlinked to form a social web of machine-readable data. The FOAF ontology defines terms like Person, Agent and their properties like name, email and knows. FOAF documents must be written in RDF and can link to each other to form a semantic web of relationships between people.
Penny Ur
This session will begin with a summary of some interesting insights from the research and their implications for teaching. We shall then look at some practical ways in which we can help students acquire, consolidate and widen their vocabulary in order to communicate and read texts successfully in English.
The document discusses word vectors for natural language processing. It explains that word vectors represent words as dense numeric vectors which encode the words' semantic meanings based on their contexts in a large text corpus. These vectors are learned using neural networks which predict words from their contexts. This allows determining relationships between words like synonyms which have similar contexts, and performing operations like finding analogies. Examples of using word vectors include determining word similarity, analogies, and translation.
This document provides an overview of word embeddings and the Word2Vec algorithm. It begins by establishing that measuring document similarity is an important natural language processing task, and that representing words as vectors is an effective approach. It then discusses different methods for representing words as vectors, including one-hot encoding and distributed representations. Word2Vec is introduced as a model that learns word embeddings by predicting words in a sentence based on context. The document demonstrates how Word2Vec can be used to find word similarities and analogies. It also references the theoretical justification that words with similar contexts have similar meanings.
Gardner’s multiple intelligences planning grid with activity ideas and starte...Jacqui Sharp
This document provides a planning grid with activity ideas for each of Gardner's Multiple Intelligences. The grid lists verbs and starter words associated with each intelligence and provides examples of activities and tools that can be used to design lessons targeting each intelligence. It includes ideas for remembering, understanding, applying, analyzing, evaluating, and creating for the eight intelligences: verbal/linguistic, logical/mathematical, visual/spatial, bodily/kinaesthetic, musical, interpersonal, intrapersonal, and naturalist.
Formal Ontologies and Nyaya-Vaiseshika SystemNagaraju Pappu
This document discusses formal ontology and its role in specifying meanings for computational agents like robots. It covers:
1) The goal of formal ontology is to provide an entitative account of meanings that can recur in cognition and be expressed in language, in a way that can be automated.
2) Specifying meanings computationally involves representing concepts as classes that inherit from higher classes, and objects as having properties that allow for multiple inheritances.
3) Formal ontology seeks to develop foundational ontologies that can underpin domain ontologies and be expressed using formal logic, rather than natural language, to specify meanings for artificial agents.
4) The development of formal ontology involves applying formal methods from fields like topology
The document discusses sentence processing and comprehension. It covers topics like sentence and clause as processing units, case-role assignment and subject-verb processing, structurally related sentences like negatives and passives, factors in comprehension like pragmatics and semantics, parsing strategies, and models of comprehension like autonomous vs interactive processing. The conclusion summarizes that a sentence is a major processing unit, an interpreter relates subject and verb, and comprehension involves factors like structure, meaning, and context.
FOAF (Friend of a Friend) is a vocabulary for describing people, their activities and their relationships. It allows personal profile pages to be interlinked to form a social web of machine-readable data. The FOAF ontology defines terms like Person, Agent and their properties like name, email and knows. FOAF documents must be written in RDF and can link to each other to form a semantic web of relationships between people.
Penny Ur
This session will begin with a summary of some interesting insights from the research and their implications for teaching. We shall then look at some practical ways in which we can help students acquire, consolidate and widen their vocabulary in order to communicate and read texts successfully in English.
The document discusses word vectors for natural language processing. It explains that word vectors represent words as dense numeric vectors which encode the words' semantic meanings based on their contexts in a large text corpus. These vectors are learned using neural networks which predict words from their contexts. This allows determining relationships between words like synonyms which have similar contexts, and performing operations like finding analogies. Examples of using word vectors include determining word similarity, analogies, and translation.
This document provides an overview of word embeddings and the Word2Vec algorithm. It begins by establishing that measuring document similarity is an important natural language processing task, and that representing words as vectors is an effective approach. It then discusses different methods for representing words as vectors, including one-hot encoding and distributed representations. Word2Vec is introduced as a model that learns word embeddings by predicting words in a sentence based on context. The document demonstrates how Word2Vec can be used to find word similarities and analogies. It also references the theoretical justification that words with similar contexts have similar meanings.
Reading is an interactive process between an author and reader where meaning is constructed. It involves filling in gaps left in a text based on the reader's own knowledge and experiences. The reading experience is dialogical in nature with the reader assuming a critical position. True reading moves beyond just decoding words to involve understanding how one can act to change the world based on what is read. Reading plays a crucial role in developing critical thinking and an awareness of one's role in society.
Greene county etymology and morphology january 15branzburg
The workshop included the following activities:
- Discussing the origins of ancient Egyptian words and their meanings.
- Brainstorming possibilities for creating new "-ologies" and "-ologists" based on areas of expertise.
- Analyzing Latin and Greek word origins and their influence on the English language.
- Playing guessing games to learn the histories and meanings of words.
- Creating charts to show how words can change forms and meanings through prefixes, suffixes, and morphological transformations.
- Mapping networks of related words connected to common roots, prefixes, and suffixes.
Learning practice: the ghosts in the education machineDavid R Cole
This slide share analyses learning and practice together. The idea here is that if analysed together, learning and practice become comprehensible as a conceptual unit that does work in education as a ghost. This ghost acts as means to separate and analyse the educational machine. in
This document discusses strategies for promoting reading comprehension in content area subjects. It defines content area reading and scaffolding techniques for supporting students before, during, and after reading. A variety of strategies are presented, including preparing students with vocabulary and activating prior knowledge, using graphic organizers during reading, and having students summarize and reflect after reading.
The document discusses natural language and natural language processing (NLP). It defines natural language as languages used for everyday communication like English, Japanese, and Swahili. NLP is concerned with enabling computers to understand and interpret natural languages. The summary explains that NLP involves morphological, syntactic, semantic, and pragmatic analysis of text to extract meaning and understand context. The goal of NLP is to allow humans to communicate with computers using their own language.
The document discusses the key concepts of syntax including:
- Syntax examines how words are combined to form sentences.
- Speakers have linguistic competence which includes understanding grammaticality, word order, constituents, functions, ambiguity, and paraphrase.
- Generative grammar uses phrase structure rules to represent the hierarchical structure of sentences and generate all possible grammatical sentences.
- Tests like substitution and movement are used to determine if a string of words forms a constituent.
Interactive Teaching methods and techniquesChetan T R
The document discusses innovative and interactive techniques for teaching, including using analogies like fishing and music. It describes selecting instructional methods based on factors like learners and objectives. Teaching methods are defined as techniques, strategies as planned approaches, and aids as supplemental tools. Specific methods are listed like lectures, discussions, and field trips. The document provides templates for problem-solving approaches like the forked road model and possibilities-factors model. Finally, it discusses integrating skills through the jigsaw technique where students research subtopics and teach their expertise to others.
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
The document discusses blended learning strategies for teacher education. It defines blended learning as combining online and in-person instruction using the right technologies matched to learning styles. Blended learning blends past teaching methods with new technologies. It requires technology, supportive learning environments, effective teachers, and open-minded teacher educators with technology skills. Blended learning can help students reach their potential if implemented properly with conceptual changes and training to replace fears of new technologies with skills and confidence.
The document discusses the characteristics of an effective research assignment and provides a suggested assignment template. It emphasizes that assignments should be clear, relevant to course goals, specify required resources, and have a clear timeline. The template includes sections for the course information, assignment description and instructions, required resources, timeline, format requirements, length, and penalties. It also provides a grading rubric with criteria for different performance levels and identifies online writing help resources.
Cognitive processes such as thinking, problem solving, language, and intelligence involve complex mental activities. Thinking refers to making sense of and changing the world through attention, mental representation, reasoning, judgment, and decision making. Problem solving uses strategies like algorithms, heuristics, analogies, and overcoming biases. Language allows for complex communication and shapes thought and culture. Theories of intelligence propose that it involves multiple abilities and can be analyzed through factors, domains, and problem-solving styles.
This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
Language teaching methods are important to study for several reasons:
1) Studying methods provides teachers with an understanding of how the field has evolved over time and exposes them to practices they can adapt or implement.
2) Experience using different methods gives teachers basic skills and allows them to reflect on their own assumptions and beliefs.
3) Comparing methods offers alternatives to how teachers were previously taught, so they can choose approaches aligned with their own views rather than what was imposed on them.
4) Understanding methods is part of building teaching knowledge and joining the community of language teaching practitioners. It expands a teacher's toolbox to address diverse learner needs.
Understanding ASL Grammatical Features and Discourse MappingDoug Stringham
The document introduces discourse mapping as a tool to help sign language interpreters and users cognitively organize and retell messages in ASL texts. It discusses analyzing ASL grammatical features like nominal structures, verbs, and complex sentences. Small groups will practice incorporating grammatical features into discourse maps - visual representations of how ideas chunk and link together. The mapping process involves watching a text multiple times to observe linguistic features and spatial relationships, then representing and discussing these to confirm comprehension before retelling the text. Discourse mapping aims to replicate logical ASL organization to improve interpretation and language ability.
Thank you for the invitation, but I'm afraid I don't have the capacity to participate as a panelist. As an AI assistant, my role is to provide helpful information to users, but not take part in live discussions. Please let me know if there's any other way I can offer support.
By watching this Power Point presentation, you'll acquire the necessary tools as well as basic information that is needed whenever you want to evaluate Vocabulary.
Discourse is a set of utterances that constitute a recognizable speech event such as a conversation, joke, sermon, or interview. Discourse analysis attempts to discover linguistic regularities in discourse using grammatical, phonological, and semantic criteria to interpret what a speaker or writer intends to convey within a social context. There are various tools and devices used for discourse analysis, including cohesion, coherence, parallelism, speech events, background knowledge, and conversational interaction principles.
The document provides instructions for creating a PowerPoint presentation on child labour in a selected country. Students must research and define child labour, select a country to focus on, and include details about the economic situation, types of child labour, statistics, and a case study from the country. Presentations should be approximately 10 minutes and include references. Students will have 3 class periods to work and presentations will be graded using provided rubrics on content, writing conventions, and presentation skills.
This document discusses different approaches to defining words and determining their meaning. It begins by explaining prototypes and mental images, noting that while useful for determining common associations, they do not fully capture a word's meaning. Dictionaries are then discussed, pointing out their limitations in relying on other defined words and being influenced by those who write them. The document advocates for usage-based definitions, using examples of different meanings of the word "fine" based on context. It concludes by introducing corpus linguistics as a way to study language usage through large databases to better inform definitions.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Reading is an interactive process between an author and reader where meaning is constructed. It involves filling in gaps left in a text based on the reader's own knowledge and experiences. The reading experience is dialogical in nature with the reader assuming a critical position. True reading moves beyond just decoding words to involve understanding how one can act to change the world based on what is read. Reading plays a crucial role in developing critical thinking and an awareness of one's role in society.
Greene county etymology and morphology january 15branzburg
The workshop included the following activities:
- Discussing the origins of ancient Egyptian words and their meanings.
- Brainstorming possibilities for creating new "-ologies" and "-ologists" based on areas of expertise.
- Analyzing Latin and Greek word origins and their influence on the English language.
- Playing guessing games to learn the histories and meanings of words.
- Creating charts to show how words can change forms and meanings through prefixes, suffixes, and morphological transformations.
- Mapping networks of related words connected to common roots, prefixes, and suffixes.
Learning practice: the ghosts in the education machineDavid R Cole
This slide share analyses learning and practice together. The idea here is that if analysed together, learning and practice become comprehensible as a conceptual unit that does work in education as a ghost. This ghost acts as means to separate and analyse the educational machine. in
This document discusses strategies for promoting reading comprehension in content area subjects. It defines content area reading and scaffolding techniques for supporting students before, during, and after reading. A variety of strategies are presented, including preparing students with vocabulary and activating prior knowledge, using graphic organizers during reading, and having students summarize and reflect after reading.
The document discusses natural language and natural language processing (NLP). It defines natural language as languages used for everyday communication like English, Japanese, and Swahili. NLP is concerned with enabling computers to understand and interpret natural languages. The summary explains that NLP involves morphological, syntactic, semantic, and pragmatic analysis of text to extract meaning and understand context. The goal of NLP is to allow humans to communicate with computers using their own language.
The document discusses the key concepts of syntax including:
- Syntax examines how words are combined to form sentences.
- Speakers have linguistic competence which includes understanding grammaticality, word order, constituents, functions, ambiguity, and paraphrase.
- Generative grammar uses phrase structure rules to represent the hierarchical structure of sentences and generate all possible grammatical sentences.
- Tests like substitution and movement are used to determine if a string of words forms a constituent.
Interactive Teaching methods and techniquesChetan T R
The document discusses innovative and interactive techniques for teaching, including using analogies like fishing and music. It describes selecting instructional methods based on factors like learners and objectives. Teaching methods are defined as techniques, strategies as planned approaches, and aids as supplemental tools. Specific methods are listed like lectures, discussions, and field trips. The document provides templates for problem-solving approaches like the forked road model and possibilities-factors model. Finally, it discusses integrating skills through the jigsaw technique where students research subtopics and teach their expertise to others.
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
The document discusses blended learning strategies for teacher education. It defines blended learning as combining online and in-person instruction using the right technologies matched to learning styles. Blended learning blends past teaching methods with new technologies. It requires technology, supportive learning environments, effective teachers, and open-minded teacher educators with technology skills. Blended learning can help students reach their potential if implemented properly with conceptual changes and training to replace fears of new technologies with skills and confidence.
The document discusses the characteristics of an effective research assignment and provides a suggested assignment template. It emphasizes that assignments should be clear, relevant to course goals, specify required resources, and have a clear timeline. The template includes sections for the course information, assignment description and instructions, required resources, timeline, format requirements, length, and penalties. It also provides a grading rubric with criteria for different performance levels and identifies online writing help resources.
Cognitive processes such as thinking, problem solving, language, and intelligence involve complex mental activities. Thinking refers to making sense of and changing the world through attention, mental representation, reasoning, judgment, and decision making. Problem solving uses strategies like algorithms, heuristics, analogies, and overcoming biases. Language allows for complex communication and shapes thought and culture. Theories of intelligence propose that it involves multiple abilities and can be analyzed through factors, domains, and problem-solving styles.
This document provides an overview of natural language processing (NLP). It discusses how NLP allows computers to understand human language through techniques like speech recognition, text analysis, and language generation. The document outlines the main components of NLP including natural language understanding and natural language generation. It also describes common NLP tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Finally, the document explains how to build an NLP pipeline by applying these techniques in a sequential manner.
Language teaching methods are important to study for several reasons:
1) Studying methods provides teachers with an understanding of how the field has evolved over time and exposes them to practices they can adapt or implement.
2) Experience using different methods gives teachers basic skills and allows them to reflect on their own assumptions and beliefs.
3) Comparing methods offers alternatives to how teachers were previously taught, so they can choose approaches aligned with their own views rather than what was imposed on them.
4) Understanding methods is part of building teaching knowledge and joining the community of language teaching practitioners. It expands a teacher's toolbox to address diverse learner needs.
Understanding ASL Grammatical Features and Discourse MappingDoug Stringham
The document introduces discourse mapping as a tool to help sign language interpreters and users cognitively organize and retell messages in ASL texts. It discusses analyzing ASL grammatical features like nominal structures, verbs, and complex sentences. Small groups will practice incorporating grammatical features into discourse maps - visual representations of how ideas chunk and link together. The mapping process involves watching a text multiple times to observe linguistic features and spatial relationships, then representing and discussing these to confirm comprehension before retelling the text. Discourse mapping aims to replicate logical ASL organization to improve interpretation and language ability.
Thank you for the invitation, but I'm afraid I don't have the capacity to participate as a panelist. As an AI assistant, my role is to provide helpful information to users, but not take part in live discussions. Please let me know if there's any other way I can offer support.
By watching this Power Point presentation, you'll acquire the necessary tools as well as basic information that is needed whenever you want to evaluate Vocabulary.
Discourse is a set of utterances that constitute a recognizable speech event such as a conversation, joke, sermon, or interview. Discourse analysis attempts to discover linguistic regularities in discourse using grammatical, phonological, and semantic criteria to interpret what a speaker or writer intends to convey within a social context. There are various tools and devices used for discourse analysis, including cohesion, coherence, parallelism, speech events, background knowledge, and conversational interaction principles.
The document provides instructions for creating a PowerPoint presentation on child labour in a selected country. Students must research and define child labour, select a country to focus on, and include details about the economic situation, types of child labour, statistics, and a case study from the country. Presentations should be approximately 10 minutes and include references. Students will have 3 class periods to work and presentations will be graded using provided rubrics on content, writing conventions, and presentation skills.
This document discusses different approaches to defining words and determining their meaning. It begins by explaining prototypes and mental images, noting that while useful for determining common associations, they do not fully capture a word's meaning. Dictionaries are then discussed, pointing out their limitations in relying on other defined words and being influenced by those who write them. The document advocates for usage-based definitions, using examples of different meanings of the word "fine" based on context. It concludes by introducing corpus linguistics as a way to study language usage through large databases to better inform definitions.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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For more information about PECB:
Website: https://pecb.com/
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Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Strong semantics : Ontolgy
1.
2. • Introduction Subhashis Das
• Ontology language Sonali Kishore Kalani
• Ontology Engineering Tools Amit Kumar Shaw
• Application of Ontologies Mayukh Biswas
• Conclusion Anurodh Kumar Sinha
3. Markup consists of:
rendering information
(e.g., font size and colour)
Hyper-links to related
content
Semantic content is accessible
to humans but not (easily) to
computers…
4. • WWW2002
• The elevenvh inveqnavional soqld side seb confeqence
• Sheqavon saikiki hovel
• Honoltlt, hasaii, USA
• 7-11 may 2002
• 1 locavion 5 dayu leaqn inveqacv
• Regiuveqed paqvicipanvu coming fqom
• atuvqalia, canada, chile
denmaqk, fqance, geqmany, ghana, ho
ng
kong, india, iqeland, ivaly, japan, m
alva, nes zealand, vhe
nevheqlandu, noqsay, uingapoqe, usivzeql
and, vhe tnived kingdom, vhe tnived
uvaveu, vievnam, zaiqe
• Regiuveq vh
nos
• On vhe 7 May Honoltlt sill pqovide vhe
backdqop of vhe elevenvh inveqnavional
soqld side seb confeqence. Thiu
pqeuvigiotu evenv
• Speakequ confiqmed
• Tim beqnequ-lee
• Tim iu vhe sell knosn invenvoq of vhe
Web,
• Ian Fouveq
• Ian iu vhe pioneeq of vhe Gqid, vhe
nev geneqavion inveqnev
5. • External agreement on meaning of annotations
– E.g., Dublin Core
• Agree on the meaning of a set of annotation tags
– Problems with this approach
• Inflexible
• Limited number of things can be expressed
• Use Ontologies to specify meaning of annotations
– Ontologies provide a vocabulary of terms
– New terms can be formed by combining existing ones
– Meaning (semantics) of such terms is formally specified
– Can also specify relationships between terms in multiple
ontologies
7. -----By Thomas Robert (Tom) Gruber (1994)
‘A formal, explicit specification of a shared conceptualization’
must be machine
not private to some individual,
understandable
but accepted by a group
types of concepts and an abstract model of some
constraints must be clearly phenomenon in the world formed by
defined
identifying the relevant concepts of
that phenomenon
8. v To share common understanding of the structure of information among people or
software agents.
v To enable reuse of domain knowledge.
v To make domain assumptions explicit.
v To separate domain knowledge from the operational knowledge.
v To analyze domain knowledge.
9. r Defining terms in the domain and relations among them
v Identifying the domain.
v Defining concepts in the domain (classes). (human, animal, food, table, movies, etc..)
v Arranging the concepts in a hierarchy (superclass -Subclass hierarchy). (Ex-
Animal
-herbivorous
-omnivorous
-carnivorous)
v Defining which attributes and properties classes can have and constraints on their
values.
Attributes (data properties), i.e. human has properties of
gender, height, weight, father, mother, etc.
Properties (Relations), i.e. Indian Statistical Institute is located in Bangalore.
HERBIVORES= only eat vegetables for example elephants are herbivores
CARNIVORES= only eat meat for example tigers are carnivores
OMNIVORES= omnivores eat both meat and plants for example dogs are omnivores
v Defining individuals and filling in properties values.
10. The three major uses of Ontologies are:
v To assist in communication between humans and computer.
v To achieve interoperability and communication among software systems.
v To improve the design and quality design and the quality of software system.
11. The term ‘procedure’ used by one tool
is translated into the term ‘method ‘
used by the other via the ontology,
whose term for the same underlying
procedure concept is ‘process’.
give me the procedure for…
viewer
here is the give me the
procedure for… translator procedure = ??? process for…
procedure =
process Ontology ??? = process
give me the
METHOD = translator METHOD
process for…
here is here is the
the process for… METHOD for… method
library
21. • Ontologies generally describe:
v Classes
sets, collections, or types of objects (Ex-Person, animal, food, table, etc.)
v Individuals
the basic or “ground level” objects (Ex- Subhashis Das is an Individual of Class
Person)
v Relationships
ways that objects can be related to one another (Subhashis Das lives in Kolkata )
v Attributes
properties, features, characteristics, or parameters that objects can have and
share
( Subhashis Das has properties of gender, height, weight, hair colour, mobile no, etc)
22. From a practical view, ontology is the representation of something we know about.
“Ontologies" consist of a representation of things, that are detectable or directly
observable, and the relationships between those things.
23. • Sir Ratan Naval Tata (born 28 December 1937) is an Indian businessman who
became chairman (1991– ) of the Tata Group, a Mumbai-based conglomerate. He
is a member of a prominent family of Indian industrialists and philanthropists (Tata
family). Tata received the Padma Bhushan, one of India’s most distinguished
civilian awards, in 2000 and Padma Vibhushan in 2008. He has also been ranked as
India's most powerful CEO. Ratan Tata was adopted to famous Tata , a prominent
family belonging to the Parsi community. Ratan is the grandson of Tata group
founder Jamsedji Tata. (http://en.wikipedia.org/wiki/Ratan_Tata)
Relations: is-a, received, is-CEO-of, is_granson_of,
Ratan Tata has Properties of
Gender: male
DOB: 28 Dec, 1937
Race: Parsi
Administrative role: CEO of Tata group
25. Requirements for an ontology language
• A well defined syntax
• A well-defined semantics
• Efficient reasoning support
• Adequate expressive power
• Convenience of expression
26. RDF/RDF Schema
• Used for describing resources on web
• Written in XML
• W3C recommendation
• RDF Schema is an extension of RDF
• Provides the framework to describe application-
specific classes and properties instead of actual
application classes and properties
• Similar to classes in OOP languages
27. Basic Building Blocks of RDF Schema
• Classes and their instances
• Binary properties between classes
• Organization of classes and properties in
hierarchies
• Domain and range restrictions
28. Limitations of RDF Schema
• Local Scope of properties
• Disjointness of classes
• Boolean combinations of classes
• Cardinality restrictions
• Special characteristics of properties
29. OWL, a Web Ontology Language
• OWL stands for Web Ontology Language
• OWL is for processing information on the web
• Three sublanguages
– OWL Full
– OWL DL
– OWL Lite
• Build on top of
– XML
– RDFS
• Similar to RDF but with much stronger syntax and larger
vocabulary
• OWL is a W3C standard
30. OWL Full
• Maximum expressiveness
• Fully upward compatible with RDF
• OWL Full allows an ontology to enhance the
meaning of the pre-defined (RDF or OWL)
vocabulary
• All language constructors can be used in any
combination as long as it is legal RDF
• Reasoning software are not able to support every
feature of OWL Full
31. OWL DL
• Based on Description Logic
• Maximum expressiveness without losing
completeness
• Widely available reasoning systems
• Constraints:
– Vocabulary partitioning
– Explicit typing
– Property separation
– No transitive cardinality restrictions
– Restricted anonymous classes
32. OWL Lite
• Must be an OWL DL ontology
• The constructors
owl:oneOf, owl:disjointWith, owl:union
Of, owl:complementOF and owl:hasValue
are not allowed
• Cardinality statements can be made only on values 0
or 1.
• owl:equivalentClass cannot be made between
anonymous classes, but only between class
identifiers
33. RDF OWL OWL OWL
XML RDF
Schema Lite DL Full
Increasing Semantic Expressiveness
34. Building Blocks in OWL…[contd.]
• Ontology declaration (XML syntax)
<rdf:RDF xmlns:owl =http://www.w3.org/2002/07/owl#"
xmlns:rdf ="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns:xsd ="http://www.w3.org/2001/XMLSchema#">
• Ontology metadata (information about the ontology)
<owl:Ontology rdf:about="">
<rdfs:comment>An example OWL ontology</rdfs:comment>
<owl:priorVersion
rdf:resource="http://www.mydomain.org/uni-ns-old"/>
<owl:imports
rdf:resource="http://www.mydomain.org/persons"/>
<rdfs:label>University Ontology</rdfs:label>
</owl:Ontology>
35. Building Blocks in OWL
• Classes
– Every class is a descendant of owl:Thing
– Classes are defined using owl:Class
– Equivalence is defined using owl:equivalentClass
• Subsumption
– Provided by owl:subClassOf
• Partitions
– Disjoint partition owl:disjointWith
– Exhaustive partition owl:oneOf
36. Building Blocks in OWL…[contd.]
• Attributes (properties)
– Datatype properties: Allows to describe a specific
aspect of a concept
• Based on XSD data types
• The range specifies the data type
• The domain specifies the class to which the property is
referred
– E.g.: Phone, title, age
– Object properties: Attributes that define
relationships between classes (Relations)
• E.g.: isTaughtBy(Class(course), Class(professor))
37. Building Blocks in OWL…[contd.]
• Relationships
– Directed
• From one concept to another, no vice versa
– Defined through object properties
• Domain: the class(es) from which the relation departs
• Range: the relation destination(s)
– Subsumption between relationships is possible
38. Building Blocks in OWL…[contd.]
• Instances (Individuals)
– No unique name assumption in OWL
– If two instances have a different name or ID this does
not imply that they are different individuals
• E.g.: “Queen Elizabeth”, “The Queen” and “Elizabeth
Windsor” might all refer to the same individual
– It must be explicitly stated that individuals are the
same as each other, or different to each other
– Defined by means of rdf:Description + rdf:Type
39. Building Blocks in OWL…[contd.]
• Advanced constructs
– OWL supports several advanced constructs to define
classes and relationships
– Constraints defined on attribute values (either object
or datatype properties)
44. • The are many Ontology tools are available in the present
times such as
Protégé, OntoEdit, Ontolingua, OilEd, pOWL etc.
• Protégé is a free, open-source platform to construct domain
models and knowledge-based applications with ontologies.
• It provide Graphical User Interface for development of RDF
and OWL statement.
45. • Go http://protege.stanford.edu/download/registered.htmlto
download Protégé
• Protégé OWL editor is built with the full installation of
Protégé platform. During the install process, choose the
“Basic+OWL” option.
• For more details:
http://protege.stanford.edu/doc/owl/getting-started.html
46. Protégé
• There are two main ways of modeling ontologies:
– Frame-based
– OWL
• Each has its own user interface
– Protégé Frames editor: enables users to build and populate ontologies
that are frame-based, in accordance with OKBC (Open Knowledge
Base Connectivity Protocol).
– Protégé OWL editor: enables users to build ontology for the Semantic
Web, in particular to OWL
• Classes
• Properties
• Instances
• Reasoning
47. Building an OWL Ontology
Create a new OWL project
– Start protégé
– A new empty Protégé-OWL project has been created.
– Save it in your local file as pizza.owl
48. Named Classes
• Go to OWL Classes tab
• The empty class tree contains one class called owl:Thing, which is superclass of
everything.
• Create subclasses Pizza, PizzaTopping and PizzaBase. They are subclasses of
owl:Thing.
50. OWL Properties
• OWL Properties represent relationships between
two objects.
• There are two main properties:
– Object properties: link object to object
– datatype properties: link object to XML Schema
datatype or rdf:literal
• OWL has another property – Annotation
properties, to be used to add annotation
information to classes, individuals, OntoGraf etc.
51.
52. Inverse Properties
• Each object property may have a corresponding
inverse property.
• If some property links individual a to individual
b, then its inverse property will link individual b
to individual a.
53. Functional Properties
• If a property is functional, for a given individual, there can
only be at most one individual to be related via this
property.
– For a given domain, range must be unique
• Functional properties are also known as single valued
properties.
54. Inverse Functional Properties
• If a property is inverse functional, then its inverse
property is functional.
– For a given range, domain must be unique.
55. Functional v/s Inverse Functional
Properties
• FunctionalProperty vs InverseFunctionalProperty
domain range example
Functional For a given Range is hasFather: A hasFather
Property domain unique B, A hasFather C B=C
InverseFunctional Domain is For a given hasID: A hasID B, C
Property unique range hasID B A=C
56. Transitive Properties
• If a property is transitive, and the property related individual
a to individual b, and also individual b to individual c, then
we can infer that individual a is related to individual c via
property P.
57. Symmetric Properties
• If a property P is symmetric, and the property relates
individual a to individual b, then individual b is also related
to individual a via property P.
58. Property: domains and ranges
• Properties link individuals from the domain to individuals
from the range
• Let us see the live demo in Protégé Software
59.
60. Ontology Application
The topic can be discussed using two approaches:
• Discussing the Ontology application domains
• Discussing the Ontology integration in Applications (i.e. Context-aware Applications
using Ontology)
61. Ontology Application Domains/ Key
Areas
• Information retrieval procedure
• Knowledge representation/sharing
• Semantic Digital Libraries
• Software engineering
• Natural-Language processing
• Multi-agent systems
62. Information retrieval procedure
• Agricultural Ontology Service (AOS)
– The AOS/CS will serve as a multilingual repository of concepts
in the agricultural domain providing ontological relationships
and a rich, semantically sound terminology.
• the purpose of the AOS is to achieve:
• better indexing of resources,
• better retrieval of resources, and
• increased interaction within the agricultural community.
63. Information retrieval procedure
The Agricultural Ontology Service (AOS) (A Tool for Facilitating Access to Knowledge)
Food and Agriculture Organization of the United Nations (FAO)
Library and Documentation Systems Division, AGRIS/CARIS and Documentation Group
Rome, Italy, June 2001, Draft 5a, September 2001
64. Agricultural Ontology Service Concept
Server (AOS/CS)
• Initially developed using relational database
• Now new model is developed using Web Ontology Language (OWL)
• The new developed model in OWL will serve as a skeleton for building agriculture
domain ontologies.
*Lauser , B., Sini, M., Liang, A., Keizer, J. and Katz, S., “From AGROVOC to the Agricultural Ontology Service / Concept Server. An OWL
model for creating ontologies in the agricultural domain”, Networked Knowledge Organization Systems and Services, The 5th European
Networked Knowledge Organization Systems (NKOS) Workshop, Workshop at the 10th ECDL Conference, Alicante, Spain, September
21, 2006.
65. Agricultural Ontology Service Concept
Server (AOS/CS)
• The multilingual issue (lexicalization) is handled using three levels
of representations i.e.
– Concepts (the abstract meaning),
– Term ( language-specific lexical form) and
– Term variant ( the range of forms that can occur for each term)
• On the Bases on the above representation inter-level relations are
defined i.e.
– Concept to Term (has_lexicalization)
– Term to String (has_acronym, has_spelling_variant, has_abbreviation)
– Concept to Concept (is_a)
– Term to Term (is_synonym_of, is_translation_of)
66. Agricultural Ontology Service Concept
Server (AOS/CS)
The Basic Model
URI Disambiguation
The Concept-to-Concept
interface
67. Agricultural Ontology Service Concept
Server (AOS/CS)
Term-to-Term Interface
Term-to-String Interface
Classification Schemes
e.g. University of Bekkeley has the
following variants Model has the support of two
l UCB, Cal, UC Berkeley, University of clasification schemes namely
Calfornia at Berkeley AGRIS/CARIS and FAO priority areas
l These relationships are modeled as l c_classification_scheme
properties of the data type l r_belongs_to_scheme
r_has_term_variant
l r_has_category
l r_has_sub_category
68. Semantic Digital Libraries*
• To provide uniform access to Digital Libraries to deal with structural and semantic
heterogeneities
Three application areas of ontologies (referred JeromeDL and BRICKS semantic digital
library projects)
– Bibliographic Ontologies
– Ontologies for Content Structures
– Community-aware Ontologies
*Kruk, R.S., Haslhofer, B., Piotrowski, Westerski, A. and Woroniecki, T. “The Role of Ontologies in Semantic Digital Libraries”, Networked
Knowledge Organization Systems and Services, The 5th European Networked Knowledge Organization Systems (NKOS)
Workshop, Workshop at the 10th ECDL Conference, Alicante, Spain, September 21, 2006.
69. Semantic Digital Libraries
• Ontologies for Content Structures
– By including structural concepts in ontologies, electronic
contents can be retrieved.
• Community-Aware Ontologies
– In semantic digital libraries, besides storing contents and meta
data, track of users, their interactions, and their knowledge
can be incorporated into the systems using community-aware
ontologies
71. Recent Developments
Semantic Search
Ontology Based Information Retrieval
1)Mental Model
2)User-Question Model
3)System Resource Model
4)System Query Model
72. Semantic Digital libraries
1.Ontologies can be used to:
(i) organize bibliographic descriptions,
(ii) represent and expose document contents,
(iii) share knowledge amongst users
73. • Semantic Social Network
Social Network + Semantic Web
1)Social Layer
2)Ontology Layer
3) Concept Layer
74. Use of Ontology in Linked Data
The IRW ontology can be used as a tool to make Linked Data more self-
describing and to allow inference to be used to test for membership in various
classes of resources
The IRW ontology this in turn allows the semantic validation, to be able to
describe and infer in detail the types of resources that can be interacted with
via HTTP, which is useful for both tools like EARL that record validation of Web
standards to be implemented in a reliable fashion, which is useful for error-
reporting on the Web in general and HTTP in particular
IRW clarifies the interactions between the hypertext Web and Linked
Data, allowing Linked Data spiders to keep track of important provenance
regarding the identity of resources, and to characterise the resources correctly
for semantic validation and error detection.
75. • Notion of consistency: The notion of consistency which is appropriate in this network
of ontologies in order to meet the requirements of future real-life application needs to
be analyze.
• Evolution of ontologies and metadata: One has to investigate which kind of metadata
are suitable for supporting the evolution of these network ontology.
• Reasoning: A basic open issue is the development of reasoning mechanisms in the
presence of inconsistencies between these networked ontology.
76. • Semi-automatic methods: Major obstacle to developing ontology-based application in
commercial setting. Therefore, the tight coupling of manual methods with automatic
methods is needed.
• Design patterns: Analogous to the development of design patterns in software
engineering of ontologies has to be improved by the development of pattern libraries
that provide ontology engineers with well engineered and application proven ontology
patterns that might be used on building block.
• Economic aspects: In commercial settings, one needs well-grounded estimations for
the effort one has to invest for building up the required ontologies in order to be able
to analyses and justify that investment. Up to now, only very preliminary methods exist
to cope with these economic aspect
77. Conclusion
• Ontologies enable a sound reasoning framework for making machines to be
contextual, discernable and relevant tool to produce semantic information
retrieval results
• Helps to reason and turn on the meaning in searching, i.e, thus add more
relevance in searching information
78. References
1. Amandeep S. Sidhu, Tharam S. Dillon,Fellow IEEE, Elizabeth Chang,Member
IEEE, Creating a Protein Ontology Resource
2.David Vallet, Miriam Fernández, and Pablo Castells, A n Ontology-Based
Information Retrieval Model
3. Fran¸cois Bry, Tim Furche, Paula-Lavinia Patranjan, and Sebastian Schaffert,
Data Retrieval and Evolution on the (Semantic) Web: A Deductive Approach
Protege Ontology Libraries
http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library
Protege tutorial
http://www.co-ode.org/resources/tutorials/
Protege Website
http://protege.stanford.edu/doc/users.html
http://protege.stanford.edu/
79. 4.Guoqian Jiang, Katsuhiko Ogasawara, Naoki Nishimoto, Akira Endoh, Tsunetaro
Sakurai, FCAView Tab: A Concept-oriented View Generation Tool for
Clinical Data Using Formal Concept Analysis
5.G. Marcos, H. Eskudero, C. Lamsfus , M.T. Linaza, Data Retrieval From a
Cultural Knowledge Database
6. Jacob Köhler and Steffen Schulze-Kremer, The Semantic Metadatabase
(SEMEDA): Ontology based integration of federated molecular biological
data sources
7. Jeff Heflin and James Hendler, Searching the Web with SHOE
Editor's Notes
An ontology language is a formal language used to encode the ontology. There are a number of such languages for ontologies, both proprietary and standards-based:Common Algebraic SpecificationCommon logic is ISO standard 24707, a specification for a family of ontology languages that can be accurately translated into each other.The Cyc project has its own ontology language called CycLDOGMA (Developing Ontology-Grounded Methods and ApplicationsGellishIDEF5 is a software engineeringKIF is a syntaxRule Interchange Format (RIF)OWL is a language for making ontological statements, developed as a follow-on from RDF and RDFS, as well as earlier ontology language projects including OIL, DAML, and DAML+OIL. OWL is intended to be used over the World Wide Web, and all its elements (classes, properties and individuals) are defined as RDF resources, and identified by URIs.Semantic Application Design Language (SADL)SBVR (Semantics of Business Vocabularies and Rules)OBO
A well defined syntax is a necessary condition for machine processing of information. Also it should be user friendly.Formal semantics defines precisely the meaning of knowledge. “Precisely” here means that the semantics does not refer to subjective intuitions, nor is it open to different interpretations by different persons (or machines)Semantics is a pre-requisite for reasoning support. Reasoning support is important because it allows one to - check the consistency of the ontology and the knowledge - check for unintended relationships between classes - automatically classify instances in classesAutomatic reasoning support allows one to check many more classes than what can be done manually. Checks like this are valuable for - designing large ontologies where multiple authors are involved - integrating and sharing ontologies from various resources
RDF stands for Resource Description FrameworkRDF is designed to be read and understood by computersRDF is not designed for being displayed to peopleRDF is written in XMLRDF is a part of the W3C's Semantic Web ActivityRDF is a W3C RecommendationRDF Schema and Application ClassesRDF describes resources with classes, properties, and values.In addition, RDF also needs a way to define application-specific classes and properties. Application-specific classes and properties must be defined using extensions to RDF.One such extension is RDF Schema.RDF Schema (RDFS)RDF Schema does not provide actual application-specific classes and properties.Instead RDF Schema provides the framework to describe application-specific classes and properties.Classes in RDF Schema are much like classes in object oriented programming languages. This allows resources to be defined as instances of classes, and subclasses of classes.
Local Scope of properties:Rdfs:range defines the range of a property, say eats for all classes. Thus in RDF Schema we cannot declare range restrictions that apply to some classes only. For example, we cannot say that cows eat only plants while other animals may eat meat too.Disjointness of classes:Sometimes we wish to say that classes are disjoint. For example, male and female are disjoint. But in RDF schema, we can only state sub class relationship. E.g.: female is a subclass of person.Boolean combinations of classes:Sometimes we wish to build new classes by combining other classes using union, intersection and complement. For example, we may wish to define the class person to be the disjoint union of the class male and female. RDF schema does not allow such definitions.Cardinality restrictions:Sometimes we wish to place restrictions on how many distinct values a property may or must take. For example, we would like to say that a person has exactly two parents, and that a course is taught by at least one lecturer. Again such restrictions are impossible to express in RDF Schema.Special characteristics of properties:Sometimes it is useful to say that a property is transitive (like “greater than”), unique (“is mother of”), or the inverse of another property, (like “eats” and “is eaten by”)So we need an ontology language that is richer than RDF Schema, a language that offers these features and more. In designing such a language one should be aware of the tradeoff between expressive power and efficiency reasoning support. Generally speaking, the richer the language is, the more inefficient the reasoning support becomes, often crossing the border of non-computability. Thus, we need a compromise, a language that can be supported by reasonably efficient reasoners, while being sufficiently expressive to express large classes of ontologies and knowledge.
Why OWL?OWL is a part of the "Semantic Web Vision" - a future where:-Web information has exact meaning-Web information can be processed by computers-Computers can integrate information from the web OWL was Designed for Processing InformationOWL was designed to provide a common way to process the content of web information (instead of displaying it).OWL was designed to be read by computer applications (instead of humans).OWL is Different from RDFOWL and RDF are much of the same thing, but OWL is a stronger language with greater machine interpretability than RDF.OWL comes with a larger vocabulary and stronger syntax than RDF.OWL is Written in XMLBy using XML, OWL information can easily be exchanged between different types of computers using different types of operating system and application languages.OWL is a Web StandardOWL became a W3C (World Wide Web Consortium) Recommendation in February 2004.A W3C Recommendation is understood by the industry and the web community as a web standard. A W3C Recommendation is a stable specification developed by a W3C Working Group and reviewed by the W3C Membership.
OWL Fullis meant for users who want and the syntactic freedom of RDF with no computational guarantees. For example, in OWL Full a class can be treated simultaneously as a collection of individuals and as an individual in its own right. Another significant difference from OWL DL is that a owl:DatatypeProperty can be marked as an owl:InverseFunctionalProperty. OWL Full allows an ontology to augment the meaning of the pre-defined (RDF or OWL) vocabulary. It is unlikely that any reasoning software will be able to support every feature of OWL Full.
OWL DLsupports those users who want the maximum expressiveness without losing computational completeness (all entailments are guaranteed to be computed) and decidability (all computations will finish in finite time) of reasoning systems. OWL DL includes all OWL language constructs with restrictions such as type separation (a class can not also be an individual or property, a property can not also be an individual or class). OWL DL is so named due to its correspondence with description logics ,a field of research that has studied a particular decidable fragment of first order logic. OWL DL was designed to support the existing Description Logic business segment and has desirable computational properties for reasoning systems.
OWL Litesupports those users primarily needing a classification hierarchy and simple constraint features. For example, while OWL Lite supports cardinality constraints, it only permits cardinality values of 0 or 1. It should be simpler to provide tool support for OWL Lite than its more expressive relatives, and provide a quick migration path for thesauri and other taxonomies.
owl:TransitiveProperty defines a transitive property, such as “has better grade than”, “is taller than”, “is an ancestor of”, etcowl:SymmetricProperty defines a asymmetric property, such as “has same grade as”, “is a sibling of”, etc.owl:FunctionalProperty defines a property that has at most one unique value for each object, such as “height”, “age”, “direct supervisor”, etc.owl:InverseFunctionalProperty defines a property for which two different objects cannot have the same value, for example the property “isThePermanentAccountNumberFor” (a Permanent Account Number is assigned to one person only).