This document discusses semantic web annotation technologies. It defines annotations as metadata that can be attached to web documents to provide additional information without editing the original document. It describes several annotation methods, including manual annotation, semi-automatic annotation using tools like GATE and KIM, and fully automatic annotation. It also discusses annotation at different levels, including metadata, content-level semantic annotations, and multimedia annotations of visual features. Tools for annotation discussed include Flickr, Riya, GATE, and KIM.
Towards From Manual to Automatic Semantic Annotation: Based on Ontology Eleme...IJwest
This document describes a proposed system for automatic semantic annotation of web documents based on ontology elements and relationships. It begins with an introduction to semantic web and annotation. The proposed system architecture matches topics in text to entities in an ontology document. It utilizes WordNet as a lexical ontology and ontology resources to extract knowledge from text and generate annotations. The main components of the system include a text analyzer, ontology parser, and knowledge extractor. The system aims to automatically generate metadata to improve information retrieval for non-technical users.
The document summarizes a talk on semantic technologies and interoperability. It discusses the emergence of semantic technologies from academia to industry applications, drivers like the Semantic Web and Web 2.0, and using semantic technologies to enable interoperability through methods like ontology mapping and coordination. Examples of applying these methods to scenarios in academia, government, bioinformatics, and emergency response are also provided. Issues regarding adoption of semantic technologies and their use in other domains like mobile web 2.0 are briefly discussed.
An intelligent expert system for location planning is proposed that uses semantic web technologies and a Bayesian network. The system integrates heterogeneous information through an ontology. It develops an integrated knowledge process to guide the engineering procedure. Based on a Bayesian network technique, the system recommends well-planned attractions to users.
Ontology is a formal explicit specification of a conceptualization that provides a shared understanding of a domain. An ontology for software engineering can help facilitate communication between distributed development teams by providing a common vocabulary and conceptualization of key software engineering concepts and their relationships. Such an ontology can be modeled using notations like UML class diagrams and activity diagrams to represent important software engineering concepts like classes, activities, and relationships. The software engineering ontology then allows for improved knowledge sharing and communication framework among distributed development teams.
This document summarizes an article about adaptive information extraction. It discusses how information extraction research has grown with the increasing availability of online text sources. However, one drawback of information extraction is its domain dependence. To address this, machine learning techniques have been used to develop adaptive information extraction systems that can be applied to new domains with less manual adaptation. The document provides an overview of information extraction and different machine learning approaches used for adaptive information extraction.
Simon Butler is a part-time PhD student at the Centre for Research in Computing since October 2008. His research focuses on analyzing semantic networks of identifier names in source code to understand how they relate to code quality and maintainability. He hypothesizes that meaningful relationships between identifier names based on their natural language content are related to higher quality and more maintainable code. His research will involve developing techniques to model these semantic relationships, validating the models against code quality metrics, and using the results to create a tool to support identifier naming and program comprehension activities.
Semantic Annotation: The Mainstay of Semantic WebEditor IJCATR
Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal
knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the
web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its
vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this
paper introduces the semantic annotation categories, tools, domains and models
The document discusses research into enabling customized accessibility for users by combining personal needs and preferences profiles (PNPs) with digital resource description metadata. PNPs describe a user's display, control, and content needs, while digital resource descriptions indicate how resources can be adapted across different modalities. Together, PNPs and resource descriptions allow for automatic, personalized accessibility adaptation of online content to individual user needs.
Towards From Manual to Automatic Semantic Annotation: Based on Ontology Eleme...IJwest
This document describes a proposed system for automatic semantic annotation of web documents based on ontology elements and relationships. It begins with an introduction to semantic web and annotation. The proposed system architecture matches topics in text to entities in an ontology document. It utilizes WordNet as a lexical ontology and ontology resources to extract knowledge from text and generate annotations. The main components of the system include a text analyzer, ontology parser, and knowledge extractor. The system aims to automatically generate metadata to improve information retrieval for non-technical users.
The document summarizes a talk on semantic technologies and interoperability. It discusses the emergence of semantic technologies from academia to industry applications, drivers like the Semantic Web and Web 2.0, and using semantic technologies to enable interoperability through methods like ontology mapping and coordination. Examples of applying these methods to scenarios in academia, government, bioinformatics, and emergency response are also provided. Issues regarding adoption of semantic technologies and their use in other domains like mobile web 2.0 are briefly discussed.
An intelligent expert system for location planning is proposed that uses semantic web technologies and a Bayesian network. The system integrates heterogeneous information through an ontology. It develops an integrated knowledge process to guide the engineering procedure. Based on a Bayesian network technique, the system recommends well-planned attractions to users.
Ontology is a formal explicit specification of a conceptualization that provides a shared understanding of a domain. An ontology for software engineering can help facilitate communication between distributed development teams by providing a common vocabulary and conceptualization of key software engineering concepts and their relationships. Such an ontology can be modeled using notations like UML class diagrams and activity diagrams to represent important software engineering concepts like classes, activities, and relationships. The software engineering ontology then allows for improved knowledge sharing and communication framework among distributed development teams.
This document summarizes an article about adaptive information extraction. It discusses how information extraction research has grown with the increasing availability of online text sources. However, one drawback of information extraction is its domain dependence. To address this, machine learning techniques have been used to develop adaptive information extraction systems that can be applied to new domains with less manual adaptation. The document provides an overview of information extraction and different machine learning approaches used for adaptive information extraction.
Simon Butler is a part-time PhD student at the Centre for Research in Computing since October 2008. His research focuses on analyzing semantic networks of identifier names in source code to understand how they relate to code quality and maintainability. He hypothesizes that meaningful relationships between identifier names based on their natural language content are related to higher quality and more maintainable code. His research will involve developing techniques to model these semantic relationships, validating the models against code quality metrics, and using the results to create a tool to support identifier naming and program comprehension activities.
Semantic Annotation: The Mainstay of Semantic WebEditor IJCATR
Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal
knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the
web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its
vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this
paper introduces the semantic annotation categories, tools, domains and models
The document discusses research into enabling customized accessibility for users by combining personal needs and preferences profiles (PNPs) with digital resource description metadata. PNPs describe a user's display, control, and content needs, while digital resource descriptions indicate how resources can be adapted across different modalities. Together, PNPs and resource descriptions allow for automatic, personalized accessibility adaptation of online content to individual user needs.
Pathema-Clostridium A NIAID Bioinformatics Resource Center (BRC)Pathema
The document describes PATHEMA-CLOSTRIDIUM, a bioinformatics resource center that provides genomic data and analysis tools for Clostridium pathogens. It hosts genomes for Clostridium botulinum and Clostridium perfringens. The center aims to meet the needs of the Clostridium research community through curation, community workshops, and prioritizing community feedback. It provides tools for gene/genome searches, pathway comparisons, and more. Future goals include expanding community annotation and developing customized tools.
This document defines some key terms related to slot machines:
- Bonuses are special features of slot games that award extra payouts or spins. They vary by game.
- Pay lines are the lines across the reels that must match symbols to win. More pay lines are available with higher bets.
- The theoretical hold percentage is how much of deposits a slot will pay out over time, usually 95-97%. This is specified in documents from manufacturers.
- The credit meter displays available credits to play, but players can lose track of spending, so starting with coins instead of loading a card is advised.
Blackjack is one of the most popular games about; it has been around for centuries and has only grown with more and more players joining the craze. It is straightforward as well as entertaining and anyone can play.
The document provides an overview of the Resource Description Framework (RDF). It describes RDF as a standard for describing web resources using metadata. RDF uses a simple data model based on making statements about resources in the form of subject-predicate-object expressions. This allows data to be shared across different applications. The document discusses key RDF concepts including resources, properties, and statements. It provides examples of RDF statements and illustrates the RDF triple format. The goal of RDF is to enable the encoding, exchange, and reuse of structured metadata about Web resources between applications.
Semantic Web in Action: Ontology-driven information search, integration and a...Amit Sheth
Amit Sheth's Keynote talk given at: “Semantic Web in Action: Ontology-driven information search, integration and analysis,” Net Object Days 2003 and MATES03, Erfurt, Germany, September 23, 2003. http://knoesis.org
Note: slides 51-55 have audio.
The document proposes a knowledge-based approach to semi-automatic annotation of multimedia documents that takes into account the needs of producers, annotators, and consumers. It argues that current annotation approaches focus too much on the annotator's perspective and neglect the roles of other users. The proposed approach uses user modeling and intelligent interfaces to manage annotations from different types and competence levels of users, as well as relationships between multimedia annotations.
This document describes a knowledge workbench called KnowBench that was developed to facilitate knowledge sharing and reuse among software developers. KnowBench integrates semantic web technologies into the Eclipse IDE to allow developers to semantically annotate software artifacts like source code. This helps developers document problems and solutions and more easily find relevant information. KnowBench uses ontologies and a semantic wiki to organize and navigate the captured development knowledge.
The document discusses semantic web technology, which aims to make information on the web better understood by machines by giving data well-defined meaning. It outlines the evolution of web technologies from the initial web to the semantic web. Key aspects of semantic web technology include ontologies to define common vocabularies, semantic annotations to associate meaning with data, and reasoning capabilities to enable complex queries and analyses. Languages, tools, and applications are needed to implement these semantic web standards and make the web of linked data usable.
This document discusses SHOE (Simple HTML Ontology Extensions), a language for specifying semantics on the World Wide Web. It describes the basic architecture of a SHOE system and some general purpose tools for SHOE, including the Knowledge Annotator for adding semantic annotations and Exposé for crawling and storing SHOE content. The document then presents the syntax and semantics of the SHOE language, including how ontologies are defined to specify valid elements and rules for assertions. It also briefly introduces some other tools that can improve usage and analysis of semantic web languages, such as the SHOE KB Library and XSB deductive database system.
The document discusses automatic data unit annotation in search results. It proposes a method that clusters data units on result pages into groups containing semantically similar units. Then, multiple annotators are used to predict annotation labels for each group based on features of the units. An annotation wrapper is constructed for each website to annotate new result pages from that site. The method aims to improve search response by providing meaningful annotations of data units within results. It is evaluated based on precision and recall for the alignment of data units and text nodes during the annotation process.
The document discusses and classifies various knowledge management tools. It describes tools for knowledge generation, codification, and transfer. Some tools enhance knowledge creation, while others enable knowledge sharing and application. Tools include blogs, wikis, content management systems, data mining, and intelligent filtering. Proper tool selection depends on the organization's business strategy and knowledge management needs.
Modular Documentation Joe Gelb Techshoret 2009Suite Solutions
Designing, building and maintaining a coherent content model is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when implementing a modular or topic-based XML standard such as DITA, SCORM and S1000D, and is essential for successfully facilitating content reuse, multi-purpose conditional publishing and user-driven content.
During this presentation we will review basic concepts and methods for implementing information architecture. We will then introduce an innovative, comprehensive methodology for information modeling and content development that employs recognized XML standards for representation and interchange of knowledge, such as Topic Maps and SKOS. In this way, semantic technologies designed for taxonomy and ontology development can be brought to bear for creating and managing technical documentation and training content, and ultimately impacting the usability and findability of technical information.
Amit P. Sheth, “Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships,” Keynote at the 29th Conference on Current Trends in Theory and Practice of Informatics (SOFSEM 2002), Milovy, Czech Republic, November 22–29, 2002.
Keynote: http://www.sofsem.cz/sofsem02/keynote.html
Related paper: http://knoesis.wright.edu/?q=node/2063
This document provides a 50-hour roadmap for building large language model (LLM) applications. It introduces key concepts like text-based and image-based generative AI models, encoder-decoder models, attention mechanisms, and transformers. It then covers topics like intro to image generation, generative AI applications, embeddings, attention mechanisms, transformers, vector databases, semantic search, prompt engineering, fine-tuning foundation models, orchestration frameworks, autonomous agents, bias and fairness, and recommended LLM application projects. The document recommends several hands-on exercises and lists upcoming bootcamp dates and locations for learning to build LLM applications.
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
Pathema-Clostridium A NIAID Bioinformatics Resource Center (BRC)Pathema
The document describes PATHEMA-CLOSTRIDIUM, a bioinformatics resource center that provides genomic data and analysis tools for Clostridium pathogens. It hosts genomes for Clostridium botulinum and Clostridium perfringens. The center aims to meet the needs of the Clostridium research community through curation, community workshops, and prioritizing community feedback. It provides tools for gene/genome searches, pathway comparisons, and more. Future goals include expanding community annotation and developing customized tools.
This document defines some key terms related to slot machines:
- Bonuses are special features of slot games that award extra payouts or spins. They vary by game.
- Pay lines are the lines across the reels that must match symbols to win. More pay lines are available with higher bets.
- The theoretical hold percentage is how much of deposits a slot will pay out over time, usually 95-97%. This is specified in documents from manufacturers.
- The credit meter displays available credits to play, but players can lose track of spending, so starting with coins instead of loading a card is advised.
Blackjack is one of the most popular games about; it has been around for centuries and has only grown with more and more players joining the craze. It is straightforward as well as entertaining and anyone can play.
The document provides an overview of the Resource Description Framework (RDF). It describes RDF as a standard for describing web resources using metadata. RDF uses a simple data model based on making statements about resources in the form of subject-predicate-object expressions. This allows data to be shared across different applications. The document discusses key RDF concepts including resources, properties, and statements. It provides examples of RDF statements and illustrates the RDF triple format. The goal of RDF is to enable the encoding, exchange, and reuse of structured metadata about Web resources between applications.
Semantic Web in Action: Ontology-driven information search, integration and a...Amit Sheth
Amit Sheth's Keynote talk given at: “Semantic Web in Action: Ontology-driven information search, integration and analysis,” Net Object Days 2003 and MATES03, Erfurt, Germany, September 23, 2003. http://knoesis.org
Note: slides 51-55 have audio.
The document proposes a knowledge-based approach to semi-automatic annotation of multimedia documents that takes into account the needs of producers, annotators, and consumers. It argues that current annotation approaches focus too much on the annotator's perspective and neglect the roles of other users. The proposed approach uses user modeling and intelligent interfaces to manage annotations from different types and competence levels of users, as well as relationships between multimedia annotations.
This document describes a knowledge workbench called KnowBench that was developed to facilitate knowledge sharing and reuse among software developers. KnowBench integrates semantic web technologies into the Eclipse IDE to allow developers to semantically annotate software artifacts like source code. This helps developers document problems and solutions and more easily find relevant information. KnowBench uses ontologies and a semantic wiki to organize and navigate the captured development knowledge.
The document discusses semantic web technology, which aims to make information on the web better understood by machines by giving data well-defined meaning. It outlines the evolution of web technologies from the initial web to the semantic web. Key aspects of semantic web technology include ontologies to define common vocabularies, semantic annotations to associate meaning with data, and reasoning capabilities to enable complex queries and analyses. Languages, tools, and applications are needed to implement these semantic web standards and make the web of linked data usable.
This document discusses SHOE (Simple HTML Ontology Extensions), a language for specifying semantics on the World Wide Web. It describes the basic architecture of a SHOE system and some general purpose tools for SHOE, including the Knowledge Annotator for adding semantic annotations and Exposé for crawling and storing SHOE content. The document then presents the syntax and semantics of the SHOE language, including how ontologies are defined to specify valid elements and rules for assertions. It also briefly introduces some other tools that can improve usage and analysis of semantic web languages, such as the SHOE KB Library and XSB deductive database system.
The document discusses automatic data unit annotation in search results. It proposes a method that clusters data units on result pages into groups containing semantically similar units. Then, multiple annotators are used to predict annotation labels for each group based on features of the units. An annotation wrapper is constructed for each website to annotate new result pages from that site. The method aims to improve search response by providing meaningful annotations of data units within results. It is evaluated based on precision and recall for the alignment of data units and text nodes during the annotation process.
The document discusses and classifies various knowledge management tools. It describes tools for knowledge generation, codification, and transfer. Some tools enhance knowledge creation, while others enable knowledge sharing and application. Tools include blogs, wikis, content management systems, data mining, and intelligent filtering. Proper tool selection depends on the organization's business strategy and knowledge management needs.
Modular Documentation Joe Gelb Techshoret 2009Suite Solutions
Designing, building and maintaining a coherent content model is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when implementing a modular or topic-based XML standard such as DITA, SCORM and S1000D, and is essential for successfully facilitating content reuse, multi-purpose conditional publishing and user-driven content.
During this presentation we will review basic concepts and methods for implementing information architecture. We will then introduce an innovative, comprehensive methodology for information modeling and content development that employs recognized XML standards for representation and interchange of knowledge, such as Topic Maps and SKOS. In this way, semantic technologies designed for taxonomy and ontology development can be brought to bear for creating and managing technical documentation and training content, and ultimately impacting the usability and findability of technical information.
Amit P. Sheth, “Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships,” Keynote at the 29th Conference on Current Trends in Theory and Practice of Informatics (SOFSEM 2002), Milovy, Czech Republic, November 22–29, 2002.
Keynote: http://www.sofsem.cz/sofsem02/keynote.html
Related paper: http://knoesis.wright.edu/?q=node/2063
This document provides a 50-hour roadmap for building large language model (LLM) applications. It introduces key concepts like text-based and image-based generative AI models, encoder-decoder models, attention mechanisms, and transformers. It then covers topics like intro to image generation, generative AI applications, embeddings, attention mechanisms, transformers, vector databases, semantic search, prompt engineering, fine-tuning foundation models, orchestration frameworks, autonomous agents, bias and fairness, and recommended LLM application projects. The document recommends several hands-on exercises and lists upcoming bootcamp dates and locations for learning to build LLM applications.
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
Library automation involves applying computers to traditional library activities like acquisition, cataloguing, circulation and reference services. The objectives are to improve control over collections, share resources among libraries, and use staff more effectively. The main steps are preparing for automation, selecting software and hardware, preparing collections, implementing the system, networking, and training staff and users. Early systems had little module integration while modern systems are fully integrated, use relational databases, and have graphical user interfaces. Automation aims to improve services, relieve staff workload, and facilitate resource sharing. Popular commercial and open source library automation software are discussed.
@lis agent communication, ontologies, protocols, semantic web 2003Luigi Ceccaroni
This document discusses agent communication, ontologies, protocols, and the semantic web. It identifies some key issues, such as getting data to transfer between isolated "bubbles" of databases and services. The proposed solution is to develop software that can bridge these bubbles autonomously using ontologies and intelligent agents. It then provides recommendations for modeling agent services, including deploying agent platforms, communication stacks, design methodologies, and the need for ontologies, structured content, and interaction protocols.
The document discusses various technologies for metasearching or cross-searching multiple databases at once, including Z39.50 for real-time searching, SRU/SRW web services, and OAI-PMH for metadata harvesting. It explains concepts like XML, web services, SOAP, and WSDL, and provides examples of how technologies like Z39.50, SRU, and OAI-PMH enable searching across different data sources.
This document provides an overview of digital libraries, including definitions, benefits, limitations, components, standards, and challenges. It defines a digital library as a collection of information stored and accessed electronically, extending the functions of a traditional library digitally. Benefits include improved access and searchability, easier information sharing and preservation. Emerging technologies discussed include metadata standards, XML, and protocols like OAI-PMH for metadata harvesting. Common digital library software includes DSpace, Greenstone, and EPrints. Challenges involve digitization, description, legal issues, presentation of heterogeneous resources, and economic sustainability.
This document provides an overview of digital libraries, including definitions, benefits, limitations, components, standards, and challenges. It defines a digital library as a collection of information stored and accessed electronically, extending the functions of a traditional library digitally. Benefits include improved access, information sharing, and preservation, while limitations include technological obsolescence and rights management. Key components discussed include digital objects, metadata, and tools like DSpace and Greenstone for developing digital libraries. Emerging standards around identifiers, encoding, and metadata are also summarized.
What does content management mean to technical communicators? It's no mystery! The real core of content management is an organized way of creating, collecting, managing and delivering content. Using a business process based on requirements, software supports business goals. Software is a part of the implementation, not the foundation. This session takes a high level view of the content management pieces and how they work together in a system.
IRJET- Semantic based Automatic Text Summarization based on Soft ComputingIRJET Journal
This document discusses semantic-based automatic text summarization using soft computing techniques. It begins with an introduction describing how large amounts of data are generated daily and the need for automated summarization. The next sections cover related work on text summarization methods including syntactic parsing, extractive techniques using n-gram language models and A* search, and mathematical reduction techniques like singular value decomposition and non-negative matrix factorization. The document also discusses using part-of-speech tagging, hidden Markov models, and named entity recognition for extractive summarization in Indian languages.
A Logic-Based Approach To Semantic Information ExtractionAmber Ford
The document describes a logic-based approach to semantic information extraction from unstructured documents. It represents documents as a two-dimensional plane composed of nested rectangular regions called portions. Each portion contains a piece of text annotated according to an ontology. It uses DLP+, an extension of DLP with object-oriented features, to represent the ontology and encode extraction patterns as rules. The patterns are used to automatically extract semantic information from documents by associating portions with ontology elements. The approach allows extracting information according to semantics rather than just syntax, and can extract from different document formats like text and HTML. It enables semantic classification of documents for applications like email filtering and skills extraction from resumes.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
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.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
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.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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.
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.
1. Semantic Web Technologies Annotation Presented By : AlbaraAbdalkhalig Mansour Sudan University-Web Technology E-mail : Brra51@hotmail.com Tel : 00249121200239
2. Definition : Annotations are comments, notes, explanations, or other types of external remarks that can be attached to a Web document or a selected part of the document. As they are external, it is possible to annotate any Web document independently, without needing to edit that document. From the technical point of view, annotations are usually seen as metadata, as they give additional information about an existing piece of data. 2
3. What is annotation? People make notes to themselves in order to preserve ideas that arise during a variety of activities. The purpose of these notes is often to summarize, criticize, or emphasize specific phrases or events. Semantic annotations are to tag ontology class instance data and map it into ontology classes. 3
4. Why use annotation? To have the world knowledge at one's finger tips seems possible. The Internet is the platform for information. Unfortunately most of the information is provided in an unstructured and non-standardized form. 4
6. (1) Manually Manual annotation is the transformation of existing syntactic resources into interlinked knowledge structures that represent relevant underlying information. Manual annotation is an expensive process, and often does not consider that multiple perspectives of a data source, requiring multiple ontologies, can be beneficial to support the needs of different users. 6
7. (2) Semi-automatic Annotation Semi-automatic annotation systems rely on human intervention at some point in the annotation process. The platforms vary in their architecture, information extraction tools and methods, initial ontology, amount of manual work required to perform annotation, performance and other features, such as storage management. 7
8. (3) Automatic Annotation The fully automatic creation of semantic annotations is an unsolved problem. Automatic semantic annotation for the natural language sentences in these pages is a daunting task and we are often forced to do it manually or semi-automatically using handwritten rules 8
9. Semantic Annotation Concerns Scale, Volume Existing & new documents on the Web Manual annotation Expensive – economic, time Subject to personal motivation Schema Complexity Storage support for multiple ontologies within or external to source document? Knowledge base refinement Access - How are annotations accessed? API, custom UI, plug-ins 9
15. Annotation of text Many systems apply rules or wrappers that were manually created that try to recognize patterns for the annotations. Some systems learn how to annotate with the help of the user. Supervised systems learn how to annotate from a training set that was manually created beforehand. Semi-automatic approaches often apply information extraction technology, which analyzes natural language for pulling out information the user is interested in. 12
16. A Walk-Through Example: GATE GATEis a tool for : scientists performing experiments that involve processing human language; companies developing applications with language processing components; teachers and students of courses about language and language computation. GATE comprises an architecture, framework (or SDK) and development environment, and has been in development since 1995 in the Sheffield NLP group. The system has been used for many language processing projects; in particular for Information Extraction in many languages. GATE is funded by the EPSRC and the EU. 13
17. KIM platform KIM = Knowledge and Information Management developed by semantic technology lab “Ontotext“ based on GATE 14
18. KIM platform KIM performs IE based on an ontology and a massive knowledge base. 15
19. KIM KB KIM KB consists of above 80,000 entities (50,000 locations, 8,400 organization instances, etc.) Each location has geographic coordinates and several aliases (usually including English, French, Spanish, and sometimes the local transcription of the location name) as well as co-positioning relations (e.g. subRegionOf.) The organizations have locatedInrelations to the corresponding Countryinstances. The additionally imported information about the companies consists of short description, URL, reference to an industry sector, reported sales, net income,and number of employees. 16
20. KIM platform The KIM platform provides a novel infrastructure and services for: automatic semantic annotation, indexing, retrieval of unstructured and semi-structured content. 17
21. KIM platform The most direct applications of KIM are: Generation of meta-data for the Semantic Web, which allows hyper-linking and advanced visualization and navigation. Knowledge Management, enhancing the efficiency of the existing indexing, retrieval, classification and filtering applications. 18
22. KIM platform The automatic semantic annotation is seen as a named-entity recognition (NER) and annotation process. The traditional flat NE type sets consist of several general types (such as Organization, Person, Date, Location, Percent, Money). In KIM the NE type is specified by reference to an ontology. The semantic descriptions of entities and relations between them are kept in a knowledge base (KB) encoded in the KIM ontology and residing in the same semantic repository. Thus KIM provides for each entity reference in the text (i) a link (URI) to the most specific class in the ontology and (ii) a link to the specific instance in the KB. Each extracted NE is linked to its specific type information (thus Arabian Sea would be identified as Sea, instead of the traditional – Location). 19
24. Multimedia Annotation Different levels of annotations Metadata Often technical metadata Content level Semantic annotations Keywords, domain ontologies, free-text Multimedia level low-level annotations Visual descriptors, such as dominant color 21
25. Metadata refers to information about technical details creation details creator, creationDate, … camera details settings resolution format EXIF access rights administrated by the OS owner, access rights, … 22
26. Content Level Describes what is depicted and directly perceivable by a human usually provided manually keywords/tags classification of content seldom generated automatically scene classification object detection different types of annotations global vs. local different semantic levels 23
27. Global vs. Local Annotations Global annotations most widely used flickr: tagging is only global organization within categories free-text annotations provide information about the content as a whole no detailed information Local annotations are less supported e.g. flickr, PhotoStuff allow to provide annotations of regions especially important for semantic image understanding allow to extract relations provide a more complete view of the scene provide information about different regions and about the depicted relations and arrangements of objects 24
28. Semantic Levels Free-Text annotations cover large aspects, but less appropriate for sharing, organization and retrieval Free-Text Annotations probably most natural for the human, but provide least formal semantics Tagging provides light-weight semantics Only useful if a fixed vocabulary is used Allows some simple inference of related concepts by tag analysis (clustering) No formal semantics, but provides benefits due to fixed vocabulary Requires more effort from the user Ontologies Provide syntax and semantic to define complex domain vocabularies Allow for the inference of additional knowledge Leverage interoperability Powerful way of semantic annotation, but hardly comprehensible by “normal users” 25
30. flickr Web2.0 application tagging photos globally add comments to image regions marked by bounding box large user community and tagging allows for easy sharing of images partly fixed vocabularies evolved e.g. Geo-Tagging 27
31. riya Similar to flickr in functionality Adds automatic annotation features Face Recognition Mark faces in photos associate name train system automatic recognition of the person in the future 28
33. References Further Reading: B. Popov, A. Kiryakov, A.Kirilov, D. Manov, D.Ognyanoff, M. Goranov: „KIM – Semantic Annotation Platform“, 2003. GATE: http://gate.ac.uk/overview.html M-OntoMat-Annotizer: http://www.acemedia.org/aceMedia/results/software/m-ontomat-annotizer.html KIM platform: http://www.ontotext.com/kim/ ALIPR: http://www.alipr.com Wikipedia links: http://en.wikipedia.org/wiki/Automatic_image_annotation http://en.wikipedia.org/wiki/Games_with_a_purpose http://en.wikipedia.org/wiki/General_Architecture_for_Text_Engineering 30
Editor's Notes
KIMprovides a Knowledge and Information Management (KIM) infrastructure and services for automatic semantic annotation, indexing, and retrieval of unstructured and semi-structured content. Within the process of annotation, KIM also performs ontology population. As a base line, KIM analyzes texts and recognizes references to entities (like persons, organizations, locations, dates). Then it tries to match the reference with a known entity, having a unique URI and description in the knowledge base. Alternatively, a new URI and entity description are automatically generated. Finally, the reference in the document gets annotated with the URI of the entity. This process, as well, as the result of it, are the KIM’s offer for semantic annotation. This sort of meta-data is later on used for semantic indexing, retrieval, visualization, and automatic hyper-linking of documents. KIM is a platform which offers a server, web user interface, and Internet Explorer plug-in. KIM is equipped with an upper-level ontology (KIMO) of about 250 classes and 100 properties. Further, a knowledge base (KIM KB), pre-populated with up to 200 000 entity descriptions, is bundled with KIM. In terms of underlying technology, KIM is using GATE, Sesame, and Lucene.