The document discusses semantic technologies including ontologies, RDF, RDFS and OWL. It provides examples of using these technologies to semantically annotate web pages and objects. Key concepts covered include using URIs to identify resources, semantic annotations with properties and values, and extending vocabularies with RDFS and OWL constructs like classes, properties, and restrictions. The goal is to enable more intelligent search by understanding relationships between resources.
This document discusses the Semantic Web and Linked Data. It provides an overview of key Semantic Web technologies like RDF, URIs, and SPARQL. It also describes several popular Linked Data datasets including DBpedia, Freebase, Geonames, and government open data. Finally, it discusses the Yahoo BOSS search API and WebScope data for building search applications.
This presentation is an introduction to RDFa, as the fourth assignment of the IST 681 in iSchool, Syracuse University. The presentation is made by Kai Li, who is a library student in Syracuse University..
The document discusses the Semantic Web, providing an overview of identification languages, integration, storage and querying, browsing and viewing technologies. It describes languages like RDF, RDF Schema and OWL, and how they add machine-understandable semantics and shared ontologies to the web. It also discusses tools for querying, visualizing and presenting Semantic Web data like SPARQL, RDF browsers, Fresnel lenses, and Yahoo Pipes for aggregating and filtering RDF feeds.
A review of the state of the art in Machine Learning on the Semantic WebSimon Price
Paper presentation at UK Computation Intelligence workshop 2003, Bristol. This paper reviews the current state of the art of machine learning applied to the Semantic Web. It looks at the Semantic Web and its languages, including RDF and OWL, from a machine learning perspective. Trends in the Semantic Web are mentioned throughout and the relationship with Web Services is examined. Applications are discussed with recent examples and pointers to data sets. Finally, the emerging field of Semantic Web Mining is introduced.
Integrating a Domain Ontology Development Environment and an Ontology Search ...Takeshi Morita
In order to reduce the cost of building domain ontologies manually, in this paper, we propose a method and a tool named DODDLE-OWL for domain ontology construction reusing texts and existing ontologies extracted by an ontology search engine: Swoogle. In the experimental evaluation, we applied the method to a particular field of law and evaluated the acquired ontologies.
The document provides an overview of the semantic web, which aims to extend the current web by giving information well-defined meaning. It discusses issues with traditional web searches and outlines the semantic web technology stack, including metadata, knowledge representation using XML, RDF, and ontologies with taxonomies and inference rules. The conclusion covers pros of semantic web like improved search accuracy and addressing complex questions, along with references for further reading.
This query will not return any results. The pattern specified in the WHERE clause contains two triples, but the second triple contains a syntax error - it is missing the property between ?x and ?ema. A valid property like email would need to be specified, such as:
SELECT ?name WHERE {
?x name ?name .
?x email ?email
}
This query will select and return the ?name of any resources ?x that have both a name and email property specified.
The document provides an introduction to Semantic Web and Linked Data. It discusses key concepts such as RDF, which represents data as subject-predicate-object triples that can be connected to form a graph. RDF has several syntaxes including XML, Turtle, and JSON. Properties in RDF triples can link to other resources or contain literal values. Types are identified with URIs and vocabularies are designed to be extensible. The goal of Linked Data is to publish structured data on the web and link it to other data to form a global data web.
This document discusses the Semantic Web and Linked Data. It provides an overview of key Semantic Web technologies like RDF, URIs, and SPARQL. It also describes several popular Linked Data datasets including DBpedia, Freebase, Geonames, and government open data. Finally, it discusses the Yahoo BOSS search API and WebScope data for building search applications.
This presentation is an introduction to RDFa, as the fourth assignment of the IST 681 in iSchool, Syracuse University. The presentation is made by Kai Li, who is a library student in Syracuse University..
The document discusses the Semantic Web, providing an overview of identification languages, integration, storage and querying, browsing and viewing technologies. It describes languages like RDF, RDF Schema and OWL, and how they add machine-understandable semantics and shared ontologies to the web. It also discusses tools for querying, visualizing and presenting Semantic Web data like SPARQL, RDF browsers, Fresnel lenses, and Yahoo Pipes for aggregating and filtering RDF feeds.
A review of the state of the art in Machine Learning on the Semantic WebSimon Price
Paper presentation at UK Computation Intelligence workshop 2003, Bristol. This paper reviews the current state of the art of machine learning applied to the Semantic Web. It looks at the Semantic Web and its languages, including RDF and OWL, from a machine learning perspective. Trends in the Semantic Web are mentioned throughout and the relationship with Web Services is examined. Applications are discussed with recent examples and pointers to data sets. Finally, the emerging field of Semantic Web Mining is introduced.
Integrating a Domain Ontology Development Environment and an Ontology Search ...Takeshi Morita
In order to reduce the cost of building domain ontologies manually, in this paper, we propose a method and a tool named DODDLE-OWL for domain ontology construction reusing texts and existing ontologies extracted by an ontology search engine: Swoogle. In the experimental evaluation, we applied the method to a particular field of law and evaluated the acquired ontologies.
The document provides an overview of the semantic web, which aims to extend the current web by giving information well-defined meaning. It discusses issues with traditional web searches and outlines the semantic web technology stack, including metadata, knowledge representation using XML, RDF, and ontologies with taxonomies and inference rules. The conclusion covers pros of semantic web like improved search accuracy and addressing complex questions, along with references for further reading.
This query will not return any results. The pattern specified in the WHERE clause contains two triples, but the second triple contains a syntax error - it is missing the property between ?x and ?ema. A valid property like email would need to be specified, such as:
SELECT ?name WHERE {
?x name ?name .
?x email ?email
}
This query will select and return the ?name of any resources ?x that have both a name and email property specified.
The document provides an introduction to Semantic Web and Linked Data. It discusses key concepts such as RDF, which represents data as subject-predicate-object triples that can be connected to form a graph. RDF has several syntaxes including XML, Turtle, and JSON. Properties in RDF triples can link to other resources or contain literal values. Types are identified with URIs and vocabularies are designed to be extensible. The goal of Linked Data is to publish structured data on the web and link it to other data to form a global data web.
W3C Tutorial on Semantic Web and Linked Data at WWW 2013Fabien Gandon
The document provides an introduction to Semantic Web and Linked Data. It discusses key concepts such as RDF, which represents data as subject-predicate-object triples that can be connected to form a graph. RDF has several syntaxes including XML, Turtle, and JSON. Properties in RDF triples can link to other resources or contain literal values. Types are identified with URIs and vocabularies are extensible. The goal of Linked Data is to publish structured data on the web and link it to other data to form a global data web.
MR^3: Meta-Model Management based on RDFs Revision ReflectionTakeshi Morita
We propose a tool to manage several sorts of relationships among RDF and RDFS. Our tool consists of three main functions: graphical editing of RDF contents, graphical editing of RDFS contents, and meta-model management facility. Metamodel management facility supports maintenance of relationship between RDF and RDFS contents. The above facilities are implemented based on plug-in system. We provide basic plug-in modules for consistency checking of RDFS classes and properties. The prototyping tool, called MR^3 (Meta-Model Management based on RDFs Revision Reflection), is implemented by Java language. Through the experiment of using MR^3, we show how MR^3 contributes the Semantic Web paradigm from the standpoint of RDFs contents management.
The document provides an introduction to RDF (Resource Description Framework). It discusses that RDF is a framework for describing resources using statements with a subject, predicate, and object. RDF identifies resources with URIs and describes resources and their properties and property values. An example RDF document is provided that describes CDs with properties like artist, country, and price.
This document summarizes requirements and proposed solutions for tagging and annotating scholarly resources to support reuse. It discusses using the Open Annotation data model and serialization formats like JSON-LD to create, retrieve, and search annotations. Annotations could be stored and managed in a Linked Data Platform using the Triannon application. Triannon provides a Rails-based backend for annotation storage and search/display via a Solr client. Remaining work includes user authentication, broader annotation types, and integrating tagged collections into other systems like research guides.
This document provides an overview of the Semantic Web and Case-Based Reasoning (CBR). It defines the Semantic Web and its goals of making web resources machine-understandable. It describes languages used in the Semantic Web like RDF, RDF Schema, DAML+OIL, and OWL. It also provides an overview of CBR, the CBR process, and Conversational CBR. Finally, it proposes a prototype application that uses CBR techniques to intelligently retrieve metadata about earthquake simulation codes from the Semantic Web.
Longwell is a tool that provides a graphical interface for exploring RDF data in a web browser. It displays types of resources as filters along the top and facets like properties on the right. Users can browse data by selecting types to view associated resources and properties. Queries powering Longwell return type and property frequencies to display, list properties for a selected type, and populate property panels with object values to enable interactive faceted browsing of RDF datasets.
The document discusses several options for publishing data on the Semantic Web. It describes Linked Data as the preferred approach, which involves using URIs to identify things and including links between related data to improve discovery. It also outlines publishing metadata in HTML documents using standards like RDFa and Microdata, as well as exposing SPARQL endpoints and data feeds.
Infromation Reprentation,Structured Data and SemanticsYogendra Tamang
This document provides an overview of information representation standards including XML, DTD, XML Schema, RDF, RDF Schema, and related technologies. It describes the basic structures and components of these standards for representing structured data and semantics on the web.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
This document provides an agenda and overview of semantic web and linked open data. It discusses the limitations of the current internet and the goals of the semantic web, which aims to make web content machine-readable through annotation and ontologies. It introduces key semantic web technologies like RDF, RDF schema, and OWL, and explains how they allow data to be interlinked and queried. Open linked data seeks to further evolve the web by linking data on the web through common vocabularies and enabling new types of browsers and search engines to utilize this semantic information.
The document provides an overview of semantic technologies and discusses their increasing mainstream adoption. It notes that Microsoft purchased Powerset in 2008, Apple purchased Siri in 2010, and Google bought Metaweb and released semantic search in 2013. It discusses how semantic technologies allow for interoperability through shared representations and reasoning. Examples are given of early semantic search applications from 1999-2002 and an operational semantic electronic medical record application deployed in 2006.
The document discusses Resource Description Framework (RDF), a W3C standard for describing web resources. RDF uses a graph-based data model consisting of subjects, predicates, and objects, known as triples. It provides a common framework for describing resources, along with their properties and relationships. RDF Schema builds upon RDF by defining additional vocabulary terms like class, subClassOf, and domain to organize RDF vocabularies and semantically relate terms. While useful, RDF Schema has limitations, leading to the development of OWL as a more expressive ontology language.
Semantic Search using RDF Metadata (SemTech 2005)Bradley Allen
The document summarizes a presentation about using RDF metadata for semantic search. It discusses problems with current enterprise search, and how semantic search using RDF can address these by unifying access to content and data, providing context, and capturing intellectual contributions to searches. The presentation provides examples of semantic search applications using RDF, and describes a case study of using RDF to provide faceted navigation of conference proceedings metadata.
This document discusses knowledge representation and semantic web technologies for representing knowledge. It covers the history of knowledge representation from the 1970s to today, including expert systems, Cyc, computational linguistics, KR programming languages, XML, and the semantic web. It describes the semantic web approach of representing web content as machine-readable data using languages like RDF, OWL, and vocabularies. It also discusses open-source tools and services for publishing and working with semantic web data.
The document discusses the Resource Description Framework (RDF), which is an XML format for encoding metadata about web resources. RDF allows statements to be made about resources using a subject, predicate, and object. It defines a basic syntax for encoding these statements in XML using Description and Property elements. RDF can represent multiple properties about a resource in one statement. Namespaces are also used to distinguish between RDF elements and vocabulary terms used as properties.
Year of the Monkey: Lessons from the first year of SearchMonkeyPeter Mika
This document discusses publishing content on the Semantic Web. It introduces basic concepts of RDF and the Semantic Web like resources, literals, and triples. It then describes six main ways to publish RDF data on the web: 1) standalone RDF documents, 2) metadata inside webpages using techniques like RDFa, 3) SPARQL endpoints, 4) feeds, 5) XSLT transformations, and 6) automatic markup tools. Finally, it briefly discusses the history of embedding metadata in HTML and examples of metadata standards.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
W3C Tutorial on Semantic Web and Linked Data at WWW 2013Fabien Gandon
The document provides an introduction to Semantic Web and Linked Data. It discusses key concepts such as RDF, which represents data as subject-predicate-object triples that can be connected to form a graph. RDF has several syntaxes including XML, Turtle, and JSON. Properties in RDF triples can link to other resources or contain literal values. Types are identified with URIs and vocabularies are extensible. The goal of Linked Data is to publish structured data on the web and link it to other data to form a global data web.
MR^3: Meta-Model Management based on RDFs Revision ReflectionTakeshi Morita
We propose a tool to manage several sorts of relationships among RDF and RDFS. Our tool consists of three main functions: graphical editing of RDF contents, graphical editing of RDFS contents, and meta-model management facility. Metamodel management facility supports maintenance of relationship between RDF and RDFS contents. The above facilities are implemented based on plug-in system. We provide basic plug-in modules for consistency checking of RDFS classes and properties. The prototyping tool, called MR^3 (Meta-Model Management based on RDFs Revision Reflection), is implemented by Java language. Through the experiment of using MR^3, we show how MR^3 contributes the Semantic Web paradigm from the standpoint of RDFs contents management.
The document provides an introduction to RDF (Resource Description Framework). It discusses that RDF is a framework for describing resources using statements with a subject, predicate, and object. RDF identifies resources with URIs and describes resources and their properties and property values. An example RDF document is provided that describes CDs with properties like artist, country, and price.
This document summarizes requirements and proposed solutions for tagging and annotating scholarly resources to support reuse. It discusses using the Open Annotation data model and serialization formats like JSON-LD to create, retrieve, and search annotations. Annotations could be stored and managed in a Linked Data Platform using the Triannon application. Triannon provides a Rails-based backend for annotation storage and search/display via a Solr client. Remaining work includes user authentication, broader annotation types, and integrating tagged collections into other systems like research guides.
This document provides an overview of the Semantic Web and Case-Based Reasoning (CBR). It defines the Semantic Web and its goals of making web resources machine-understandable. It describes languages used in the Semantic Web like RDF, RDF Schema, DAML+OIL, and OWL. It also provides an overview of CBR, the CBR process, and Conversational CBR. Finally, it proposes a prototype application that uses CBR techniques to intelligently retrieve metadata about earthquake simulation codes from the Semantic Web.
Longwell is a tool that provides a graphical interface for exploring RDF data in a web browser. It displays types of resources as filters along the top and facets like properties on the right. Users can browse data by selecting types to view associated resources and properties. Queries powering Longwell return type and property frequencies to display, list properties for a selected type, and populate property panels with object values to enable interactive faceted browsing of RDF datasets.
The document discusses several options for publishing data on the Semantic Web. It describes Linked Data as the preferred approach, which involves using URIs to identify things and including links between related data to improve discovery. It also outlines publishing metadata in HTML documents using standards like RDFa and Microdata, as well as exposing SPARQL endpoints and data feeds.
Infromation Reprentation,Structured Data and SemanticsYogendra Tamang
This document provides an overview of information representation standards including XML, DTD, XML Schema, RDF, RDF Schema, and related technologies. It describes the basic structures and components of these standards for representing structured data and semantics on the web.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
This document provides an agenda and overview of semantic web and linked open data. It discusses the limitations of the current internet and the goals of the semantic web, which aims to make web content machine-readable through annotation and ontologies. It introduces key semantic web technologies like RDF, RDF schema, and OWL, and explains how they allow data to be interlinked and queried. Open linked data seeks to further evolve the web by linking data on the web through common vocabularies and enabling new types of browsers and search engines to utilize this semantic information.
The document provides an overview of semantic technologies and discusses their increasing mainstream adoption. It notes that Microsoft purchased Powerset in 2008, Apple purchased Siri in 2010, and Google bought Metaweb and released semantic search in 2013. It discusses how semantic technologies allow for interoperability through shared representations and reasoning. Examples are given of early semantic search applications from 1999-2002 and an operational semantic electronic medical record application deployed in 2006.
The document discusses Resource Description Framework (RDF), a W3C standard for describing web resources. RDF uses a graph-based data model consisting of subjects, predicates, and objects, known as triples. It provides a common framework for describing resources, along with their properties and relationships. RDF Schema builds upon RDF by defining additional vocabulary terms like class, subClassOf, and domain to organize RDF vocabularies and semantically relate terms. While useful, RDF Schema has limitations, leading to the development of OWL as a more expressive ontology language.
Semantic Search using RDF Metadata (SemTech 2005)Bradley Allen
The document summarizes a presentation about using RDF metadata for semantic search. It discusses problems with current enterprise search, and how semantic search using RDF can address these by unifying access to content and data, providing context, and capturing intellectual contributions to searches. The presentation provides examples of semantic search applications using RDF, and describes a case study of using RDF to provide faceted navigation of conference proceedings metadata.
This document discusses knowledge representation and semantic web technologies for representing knowledge. It covers the history of knowledge representation from the 1970s to today, including expert systems, Cyc, computational linguistics, KR programming languages, XML, and the semantic web. It describes the semantic web approach of representing web content as machine-readable data using languages like RDF, OWL, and vocabularies. It also discusses open-source tools and services for publishing and working with semantic web data.
The document discusses the Resource Description Framework (RDF), which is an XML format for encoding metadata about web resources. RDF allows statements to be made about resources using a subject, predicate, and object. It defines a basic syntax for encoding these statements in XML using Description and Property elements. RDF can represent multiple properties about a resource in one statement. Namespaces are also used to distinguish between RDF elements and vocabulary terms used as properties.
Year of the Monkey: Lessons from the first year of SearchMonkeyPeter Mika
This document discusses publishing content on the Semantic Web. It introduces basic concepts of RDF and the Semantic Web like resources, literals, and triples. It then describes six main ways to publish RDF data on the web: 1) standalone RDF documents, 2) metadata inside webpages using techniques like RDFa, 3) SPARQL endpoints, 4) feeds, 5) XSLT transformations, and 6) automatic markup tools. Finally, it briefly discusses the history of embedding metadata in HTML and examples of metadata standards.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
2. Key Idea
• Make current search engines more “semantic” / “intelligent”
3. Examples
• When searching for “laptop”, then one is looking for
laptops or synonyms / related concepts (such as
“notebook”), but also for special kinds of laptops that
are not synonyms / related concepts, such as e.g.
IBM/Lenovo ThinkPads.
• When searching for Web pages about the first
president of the USA, “Washington”, semantic
annotations and background knowledge allow us to
restrict our search to Web pages that are actually
about Washington as the name of the president.
4. Semantic Annotations
• We assume semantic annotations to standard
Web pages and to objects on standard Web
pages:
• user-defined: starting to be widely available for a
large class of Web resources, especially with the
Web 2.0;
• automatically learned from the Web pages and
the objects to be annotated;
• automatically extracted from Web pages via user-
defined rules (i.e., mapping Web pages/objects to
an ontological knowledge base).
5. Example
• A Web page i1 may contain information about
a Ph.D. student i2, called Mary, and two of her
papers, namely, a conference paper i3 entitled
“Semantic Web search" and a journal paper i4
entitled “Semantic Web search engines" and
published in 2008.
6. Example (Cont…)
• Annotation for the Web page encodes that it
mentions Mary and the two papers:
Ai1 ={contains(i1, i2), contains(i1, i3),
contains(i1, i4)}
• Annotation for Mary may encode that she is a
Ph.D. student with the name Mary and the author
of the papers i3 and i4:
Ai2 ={PhDStudent(i2), name(i2, “mary"),
isAuthorOf(i2, i3), isAuthorOf(i2, i4)}
7. Example (Cont…)
• Annotation for the paper i3 may encode that i3 is
a conference paper and has the title “Semantic
Web search":
Ai3 ={ConferencePaper(i3), title(i3, “Semantic
Web search")}
• Annotation for the paper i4 may encode that i4 is
a journal paper, authored by Mary, has the title
“Semantic Web search engines", was published in
2008, and has the keyword “RDF”:
9. Inference Engine
• Using a background ontology, these semantic
annotations are then further enhanced in an
offline inference step, where the Inference Engine
adds all properties that can be deduced / induced
from the semantic annotations and the ontology.
• The resulting (completed) semantic annotations
are then published as Web pages, so that they
can be searched by standard Web search engines.
10. Inference Engine
• An ontology may contain the knowledge that
all journal and conference papers are also
articles, that conference papers are not journal
papers, and that “is author of” is the inverse
relation to “has author”, which is formally
expressed by the axioms.
11. Inference Engine
• Using this ontological background knowledge,
we can derive from the above annotations
that the two papers i3 and i4 are also articles,
and are both authored by Mary.
12. Inference Engine
• These searchable completed semantic annotations of (objects
on) standard Web pages are published as HTML Web pages
with pointers to the respective object pages.
13. Query Evaluator
• The Query Evaluator reduces each Semantic Web
search query in an online step to a sequence of
standard Web search queries on standard Web
and annotation pages, which are then processed
by a standard Web Search Engine, assuming
standard Web and annotation pages are
appropriately indexed.
• The Query Evaluator also collects the results and
re-transforms them into a single answer which is
returned to the user
14. Example
• Semantic Web search query, one may ask for all Ph.D. students
who have published an article in 2008 with RDF as a keyword,
which is formally expressed as follows:
• This query is transformed into the two queries Q1 =
PhDStudent AND isAuthorOf and Q2 = Article AND
“yearOfPublication 2008” AND “keyword RDF”, which are both
submitted to a standard Web search engine. The result of the
original query Q is then constructed from the results of the
two queries Q1 and Q2.
15. Semantic Web Languages
• Resource Description Framework (RDF)
– RDF is a language (XML syntax + semantics) for
representing metadata
– for describing the semantics of information in a
machine- accessible way
• RDF Schema (RDFS) extends RDF with “schema
vocabulary”
– Class, Property
– type, subClassOf, subPropertyOf
– range, domain
– RDFS is a very simple ontology language
16. The RDF Data Model
• Statements are (subject, predicate, object) triples:
(FAcosta, hasWritten, “Research Methods in IT”)
Can be represented as a graph:
• Statements describe properties of resources. A
resource is any object that can be pointed to by a
URI:Properties themselves are also resources (URIs)
FAcosta
hasWritten
subject object
predicate
Research
Methods in IT
17. Uniform Resource Identifier - URI
• "The generic set of all names/addresses that are short
strings that refer to resources"
– URLs (Uniform Resource Locators) are a particular type of
URI, used for resources that can be accessed on the WWW
(e.g., web pages)
– Accessing resource in a machine-understandable format
using a URI
• In RDF, URIs typically look like “normal” URLs, often
with fragment identifiers to point at specific parts of a
document:
• Example: http://ks.strathmore.edu/example/#facosta
– Shorthand notation strath:facosta
19. RDF Syntax
• RDF has an XML syntax
– Every Description element describes a resource
– Every attribute or nested element inside a Description is a
property of that Resource
<rdf:Description rdf:about="http://ks.strathmore.edu/example/facosta">
<homePage rdf:resource="http://www.strathmore.edu/lecturers/facosta"/>
<hasName>Freddie Acosta</strath:hasName>
<email rdf:resource="mailto:facosta@strathmore.edu"/>
<hasWritten rdf:resource=“strath:IT2145"/>
</rdf:Description>
<rdf:Description rdf:about="http://ks.strathmore.edu/example/IT2145">
<Title>Problem Based Learning Methodology</Title>
</rdf:Description>
20. Using rdf:about
• To describe a resource:
– <rdf:Description rdf:about=
“http://www.example.com/example.rdf#foo”>
• Ending a description
– <rdf:Description rdf:about=
“ http://www.example.com/example.rdf#foo”>
</rdf:Description>
– <rdf:Description rdf:about=
“http://www.example.com/example.rdf#foo”/>
21. Using rdf:ID
• rdf:ID is a local definition instead of a global
one
– <rdf:Description rdf:ID=“foo”>
22. Properties
• To create a property
<rdf:Property rdf:ID=“hasTitle”/>
<rdf:Property
rdf:about=“http://www.example.com/employment.rdf#ha
sTitle”/>
23. Property With a literal (String)
<rdf:Description rdf:ID=“Jen”>
<hasTitle>Professor</hasTitle>
</rdf:Description>
24. Property: With a resource as the
object
<rdf:Description rdf:ID=“Jen”>
<hasTitle
rdf:resource=“http://www.example.com/employment.rdf#
Professor”/>
</rdf:Description>
OR
• <rdf:Description rdf:ID=“Jen”>
– <hasTitle rdf:resource=“#Professor”/>
• </rdf:Description>
25. Benefits
• Anyone can talk about any other resource
(using rdf:about)
• Allows for annotation and expansion of
existing resources
• New statements are joined into the graph
27. RDF Schema (RDFS)
• RDF gives a language for meta data annotation, and
a way to write it down in XML, but it does not
provide any way to structure the annotations
• RDF Schema augments RDF to allow you to define
vocabulary terms and the relations between those
terms
– it gives “extra meaning” to particular RDF predicates
and resources
– e.g.
• Classes and properties
• Sub/super-classes (and properties i.e. type, subClassOf, subPropertyOf)
• Range and domain (of properties)
• These terms are the RDF Schema building blocks
(constructors) used to create vocabularies.
28. RDF Schema terms
– Class
– Property
– type
– subClassOf
– range
– domain
• These terms are the RDF Schema building blocks (constructors) used to create
vocabularies:
<Person,type,Class>
<hasColleague,type,Property>
<Professor,subClassOf,Person>
<Carole,type,Professor>
<hasColleague,range,Person>
<hasColleague,domain,Person>
29. RDFS Classes
• Classes are categories into which resources
can be grouped
• Members of classes are instances
• subClasses create a hierarchy of classes
30. Properties
• RDFS adds domains and ranges
– Limit what types of objects can be the subject of a property and what
types can be the object
E.g.
<Species,type,Class>
<Lion,type,Species>
• Properties can themselves have properties
E.g.
<hasDaughter,subPropertyOf,hasChild>
<hasDaughter,type,familyProperty>
• Datatype properties (connects an instance of a class to a literal value)
• Object properties (connect instances of classes)
31. Multiple Domains and Ranges
• Multiple domains or ranges on a property are
treated as intersection
• Example: the property hasMother has a range
of Female and also a range of Parent. That
means the object of the property must be
both a Parent and Female
32. Other Additions
• Labels and comments
– rdfs:label - commonly used to give a real-world
name to the resource being described
– rdfs:comment - can be any text that you want to
relate to the resource
33. RDF Summary
• RDF - The Resource Description Framework allows
us to describe resources by specifying their
properties and property values.
– RDF Statements are triples of the form (Subject,
Predicate, Object)
– A set of RDF triples forms an RDF Graph
• RDF Schema semantically extends RDF by
providing a means to describe RDF Vocabularies.
• RDF and RDF Schema provide basic capabilities
for describing vocabularies that describe
resources.
37. The Three Species of OWL
• OWL-Full - No restrictions on how/where language
constructs can be used. The union of OWL and RDF
Schema. OWL-Full is not decidable.
• OWL-DL - Restricted version of OWL-Full.
Corresponds to a description logic. Certain
restrictions on how/where language constructs can
be used in order to guarantee decidability.
• OWL-Lite - A subset of OWL-DL. The simplest and
easiest to implement of the three species.
38. Components of an OWL Ontology
• Individuals
• Classes
• Properties
Research
Methods in IT
Philippines
Clement
Acosta
Advanced
Accounting
Kenya
UK
isCitizenOf
hasColleague
hasWritten
Employee
Publication
Country
39.
40. Combinations
• unionOf (uses ParseType)
– E.g. European Union Citizenship is the unionOf the citizenship of the
member states
• intersectionOf (uses ParseType)
– E.g. Fire engines are found in the intersection of RedThings and Trucks
• complementOf (used like subClassOf)
– E.g. the complementOf livingThings are all things that are non-living
• disjointWith (used like subClassOf)
– E.g. Man and Woman are disjoint classes
<owl:Class rdf:ID=“Man”/>
<owl:Class rdf:ID=“Woman”>
<owl:disjointWith rdf:resource=”#Man”/>
</owl:Class>
41. Restrictions
• Property Type Restrictions
– allValuesFrom
• The hasMother property has allValuesFrom the class Woman
– someValuesFrom
• The hasChild property has someValuesFrom the class Woman
42.
43. Property Characteristics
• inverseOf
– hasParent is the inverseOf hasChild
• TransitiveProperty
– E.g. - ancestorOf - if Bob is an ancestorOf Joe and Joe is an
ancestorOf Fred, then Bob is an ancestorOf Fred
• SymmetricProperty
– E.g. if Tom is marriedTo Michelle, then Michelle is marriedTo Tom
• FunctionalProperty (unique value)
– Wine hasMaker - hasMaker is functional (there can be only one)
• InverseFunctionalProperty
– The inverse of a functional property - makesWine is the inverse of
hasMaker and is an inverseFunctionalProperty
44. Local Restrictions on Property Ranges
• Instead of setting a range for a property, each class can have
its own range
• E.g. The range of eats for vegetarians is different than for non-
vegetarians
• Done with subclasses and a restriction
<owl:Class rdf:ID="Vegetarian">
<rdfs:subClassOf>
<owl:Restriction>
<owl:onProperty rdf:resource="#eats"/>
<owl:allValuesFrom
rdf:resource="#VegetarianFood"/>
</owl:Restriction>
</rdfs:subClassOf>
…
45. Example
• ZipCode equivalentClass PostalCode
• If zip code and postal code are supposed to be different - e.g.
zip is for american addresses and postal is for foreign ones -
then we can say they are different
• ZipCode differentFrom PostalCode
<owl:Class rdf:ID=“ZipCode”>
<owl:differentFrom
rdf:resource=“http://example.com/ont.owl#PostalCode/>
</owl:Class>