A non-technical explanation of the main ideas and notions in OWL.This talk was also recorded on video, and is available on-line at http://videolectures.net/koml04_harmelen_o/
The document discusses ontologies, vocabularies, and semantic web technologies. It provides an overview of RDF, RDF Schema, and OWL, including their semantics and capabilities. It describes how ontologies can constrain models and enable reasoning to derive inferences from class definitions and axioms. The document also addresses some common misconceptions regarding ontology modeling concepts.
This document discusses the Web Ontology Language (OWL). It begins by providing motivation for OWL, noting limitations of RDF and RDF Schema in areas like expressiveness. It then outlines the technical solution of OWL, including its design goals of being shareable, changing over time, ensuring interoperability, and balancing expressiveness with complexity. Finally, it introduces the three dialects of OWL - OWL Lite, OWL DL, and OWL Full - and their different levels of expressiveness and reasoning capabilities.
This document provides an overview of RDF semantics. It defines semantics as the study of meaning in communication and models as linking intensional and extensional meaning. It discusses RDF syntax as triples and graphs. It then defines simple interpretation and semantic conditions for RDF graphs. Finally, it defines RDFS vocabulary and semantic conditions for RDFS, including domains, ranges, and subclass/subproperty relationships.
Here are proofs for the tautologies using RDFS semantics:
1. rdfs:subPropertyOf rdfs:subPropertyOf rdfs:subPropertyOf
- By definition, if P rdfs:subPropertyOf Q then IEXT(P) ⊆ IEXT(Q)
- So if P rdfs:subPropertyOf R and R rdfs:subPropertyOf S then IEXT(P) ⊆ IEXT(R) ⊆ IEXT(S), hence P rdfs:subPropertyOf S
2. rdfs:domain rdfs:domain rdf:Property
- By definition, the domain of rdfs:domain is rdf:Property
3. rdfs:domain rdfs:range rdf:Class
The document discusses the need for semantic technologies like ontologies to help address information overload by allowing machines to extract knowledge. It describes the evolution of semantic technologies, starting with XML providing syntactic interoperability, RDF providing a semantic grammar through assertions and relationships, and RDFS providing semantic interoperability through hierarchies and taxonomies for defining vocabulary. However, RDFS is not expressive enough to model all ontologies, so OWL was created by W3C to further extend RDFS while addressing complexity through different profiles like OWL Lite, DL, and Full.
This document provides an overview of the Web Ontology Language (OWL). It discusses OWL's purpose in extending RDF Schema to provide a full knowledge representation language for the web. It outlines OWL's key features such as logical expressions, cardinality constraints, enumerated classes, and property characteristics. It also describes OWL's three sublanguages - OWL Lite, OWL DL, and OWL Full - which differ in their expressiveness and computational guarantees. The document concludes by discussing the Rule Interchange Format (RIF) and its role in defining rule languages for the semantic web.
The document discusses ontology, the Web Ontology Language (OWL) which is used to build ontologies, and how OWL enables semantic capabilities like defining classes, properties, and restrictions. It also covers ontology tools, editors, applications, and how ontologies can be queried using SPARQL to integrate and enable reasoning over data on the Semantic Web.
The document discusses ontology engineering and provides details about:
1. Ontology engineering is the process of developing ontologies for a particular domain by defining concepts, arranging them hierarchically, and defining their properties and relationships.
2. Ontology engineering is analogous to object-oriented database design but ontologies reflect the structure of the world using open world assumptions.
3. Popular ontology engineering tools include Protégé, which supports ontology development and knowledge modeling.
The document discusses ontologies, vocabularies, and semantic web technologies. It provides an overview of RDF, RDF Schema, and OWL, including their semantics and capabilities. It describes how ontologies can constrain models and enable reasoning to derive inferences from class definitions and axioms. The document also addresses some common misconceptions regarding ontology modeling concepts.
This document discusses the Web Ontology Language (OWL). It begins by providing motivation for OWL, noting limitations of RDF and RDF Schema in areas like expressiveness. It then outlines the technical solution of OWL, including its design goals of being shareable, changing over time, ensuring interoperability, and balancing expressiveness with complexity. Finally, it introduces the three dialects of OWL - OWL Lite, OWL DL, and OWL Full - and their different levels of expressiveness and reasoning capabilities.
This document provides an overview of RDF semantics. It defines semantics as the study of meaning in communication and models as linking intensional and extensional meaning. It discusses RDF syntax as triples and graphs. It then defines simple interpretation and semantic conditions for RDF graphs. Finally, it defines RDFS vocabulary and semantic conditions for RDFS, including domains, ranges, and subclass/subproperty relationships.
Here are proofs for the tautologies using RDFS semantics:
1. rdfs:subPropertyOf rdfs:subPropertyOf rdfs:subPropertyOf
- By definition, if P rdfs:subPropertyOf Q then IEXT(P) ⊆ IEXT(Q)
- So if P rdfs:subPropertyOf R and R rdfs:subPropertyOf S then IEXT(P) ⊆ IEXT(R) ⊆ IEXT(S), hence P rdfs:subPropertyOf S
2. rdfs:domain rdfs:domain rdf:Property
- By definition, the domain of rdfs:domain is rdf:Property
3. rdfs:domain rdfs:range rdf:Class
The document discusses the need for semantic technologies like ontologies to help address information overload by allowing machines to extract knowledge. It describes the evolution of semantic technologies, starting with XML providing syntactic interoperability, RDF providing a semantic grammar through assertions and relationships, and RDFS providing semantic interoperability through hierarchies and taxonomies for defining vocabulary. However, RDFS is not expressive enough to model all ontologies, so OWL was created by W3C to further extend RDFS while addressing complexity through different profiles like OWL Lite, DL, and Full.
This document provides an overview of the Web Ontology Language (OWL). It discusses OWL's purpose in extending RDF Schema to provide a full knowledge representation language for the web. It outlines OWL's key features such as logical expressions, cardinality constraints, enumerated classes, and property characteristics. It also describes OWL's three sublanguages - OWL Lite, OWL DL, and OWL Full - which differ in their expressiveness and computational guarantees. The document concludes by discussing the Rule Interchange Format (RIF) and its role in defining rule languages for the semantic web.
The document discusses ontology, the Web Ontology Language (OWL) which is used to build ontologies, and how OWL enables semantic capabilities like defining classes, properties, and restrictions. It also covers ontology tools, editors, applications, and how ontologies can be queried using SPARQL to integrate and enable reasoning over data on the Semantic Web.
The document discusses ontology engineering and provides details about:
1. Ontology engineering is the process of developing ontologies for a particular domain by defining concepts, arranging them hierarchically, and defining their properties and relationships.
2. Ontology engineering is analogous to object-oriented database design but ontologies reflect the structure of the world using open world assumptions.
3. Popular ontology engineering tools include Protégé, which supports ontology development and knowledge modeling.
The OWL Web Ontology Language enables software engineers to define ontologies of domain knowledge which can be queried and reasoned over by software agents. OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
This presentation is about:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions -Property Restrictions
- Class Expressions -Cardinality Restrictions
This document provides an overview of the Web Ontology Language (OWL). It discusses the requirements for ontology languages, the three species of OWL (Lite, DL, Full), the syntactic forms of OWL, and key elements of OWL including classes, properties, restrictions, and boolean combinations. It also covers special properties, datatypes, and versioning information. OWL builds on RDF and RDF Schema to provide a stronger language for defining ontologies with greater machine interpretability on the semantic web.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/owl-web-ontology-language.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=5Kr4JzqDO_w
The lecture covers:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions? -Property Restrictions
- Class Expressions? -Cardinality Restrictions
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection TasksMLconf
Deep Learning Architectures for Semantic Relation Detection Tasks
Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for creating labelled datasets required for training deep learning models for classifying semantic relations between phrases. I will further present various neural network architectures that integrate morphological features into integrated path-based and distributional relation detection algorithms and demonstrate that this model outperforms state-of-the-art models in distinguishing semantic relations and is capable of efficiently handling multi-word expressions.
The document discusses the Web Ontology Language (OWL). It provides an overview of OWL, describing its three sublanguages - OWL Lite, OWL DL, and OWL Full - and their increasing expressiveness and reasoning complexity. The document also reviews the requirements for ontology languages and how OWL builds upon XML, RDF, and RDF Schema as the ontology language for the Semantic Web.
OWL 2 adds several new features to OWL including:
1) Cleaner language design with axiom-centered structural specification and functional style syntax.
2) Increased expressiveness through properties such as property chains, qualified cardinality restrictions, and datatype restrictions on properties.
3) Enhanced datatypes including new datatypes, datatype definitions, and data range combinations.
3) Profiles such as OWL 2 EL, QL, and RL that provide different tradeoffs between expressiveness and reasoning complexity.
The document discusses the role of reasoning for RDF validation. It finds that:
1. Reasoning can resolve or cause validation violations and reduce redundancy. Around 43% of constraint types benefit from reasoning.
2. Validating with reasoning is more computationally expensive, ranging from PTIME to N2EXPTIME complexity depending on the reasoning type.
3. Around 57% of constraint types are independent of the closed-world assumption, while 67% depend on the unique name assumption when validating. Validation results differ depending on the semantics assumed.
EDF2012 Irini Fundulaki - Abstract Access Control Models for Dynamic RDF Da...European Data Forum
This document presents an abstract access control model for controlling access to dynamic RDF datasets. The model uses abstract tokens and operators to represent how access labels of inferred triples are computed, avoiding the need to recompute labels when updates occur. It describes annotation models that associate access labels with triples, and how RDFS inference rules can be applied to infer new triples and labels. The abstract model is evaluated by mapping abstract expressions to concrete access control policies and operators. Pros include flexibility to experiment with policies and efficiency during updates, while cons include increased storage overhead. Future work involves implementing the approach in a database engine.
This talk was given by FORTH, Greece, at the European Data Forum (EDF) 2012 took place on June 6-7, 2012 in Copenhagen (Denmark) at the Copenhagen Business School (CBS).
Abstract:
Given the increasing amount of sensitive RDF data available on the Web, it becomes increasingly critical to guarantee secure access to this content. Access control is complicated when RDFS inference rules and other dependencies between access permissions of triples need to be considered; this is necessary, e.g., when we want to associate the access permissions of inferred triples with the ones that implied it. In this paper we advocate the use of abstract provenance models that are defined by means of abstract tokens operators to support fine grained access control for RDF graphs. The access label of a triple is a complex expression that encodes how said label was produced (i.e., the triples that contributed to its computation). This feature allows us to know exactly the effects of any possible change, thereby avoiding a complete recomputation of the labels when a change occurs. In addition, the same application can choose to enforce different access control policies or, different applications can enforce different policies on the same data, avoiding the recomputation of the label of a triple. Preliminary experiments have shown the applicability and benefits of our approach.
1) The Semantic Web technologies OWL 2 and Rule Interchange Format (RIF) have recently been finalized, while technical work is ongoing for SPARQL 1.1, RDFa 1.1, and connecting relational databases to RDF.
2) A workshop will discuss a possible revision to RDF to address issues like deprecation of features and addition of new constructs like named graphs.
3) The standards organization W3C is working on finalizing current technologies while exploring new areas like provenance and revisions to the core RDF standard based on discussion at the workshop.
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This document provides an overview of SHACL (Shapes Constraint Language), a W3C recommendation for defining constraints on RDF graphs. It defines key SHACL concepts like shapes, targets, node shapes, property shapes and constraint components. Examples are provided to illustrate shape definitions and how validation of an RDF graph works against the defined shapes. The document summarizes the motivation for SHACL and inputs that influenced its development.
The document provides an overview of the work done at DERI Galway, including developing technologies like SIOC, ActiveRDF, and BrowseRDF to interconnect online communities and enable semantic applications. It also describes JeromeDL, a digital library system that uses semantic metadata and services to allow users to collaboratively browse and share knowledge.
Ontology and Ontology Libraries: a critical studyDebashisnaskar
This document provides an overview of ontology and ontology libraries. It discusses what ontologies are, languages for expressing ontologies like OWL, and tools for building ontologies such as Protégé. It also examines several ontology libraries including BioPortal for biomedical ontologies, OBO Foundry, oeGov for e-government, and TONES for general ontologies. Evaluation criteria for comparing ontology libraries and future challenges and opportunities are also reviewed.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
This document provides an introduction and examples for SHACL (Shapes Constraint Language), a W3C recommendation for validating RDF graphs. It defines key SHACL concepts like shapes, targets, and constraint components. An example shape validates nodes with a schema:name and schema:email property. Constraints like minCount, maxCount, datatype, nodeKind, and logical operators like and/or are demonstrated. The document is an informative tutorial for learning SHACL through examples.
The document provides an overview of the Semantic Web including definitions of key concepts like RDF, RDFS, OWL, and applications. It describes the Semantic Web as extending the current web to give data well-defined meaning enabling computers and people to better cooperate. The layers of the Semantic Web are outlined including XML, RDF, RDFS, OWL, and how each builds on the previous. Examples of RDF graphs and syntax are given. Semantic Web applications like Swoogle, DBpedia, and Flickr are also mentioned.
Semantic Web: From Representations to ApplicationsGuus Schreiber
This document discusses semantic web representations and applications. It provides an overview of the W3C Web Ontology Working Group and Semantic Web Best Practices and Deployment Working Group, including their goals and key issues addressed. Examples of semantic web applications are also described, such as using ontologies to integrate information from heterogeneous cultural heritage sources.
The document discusses the W3C stack for representing metadata, with XML providing syntax but no semantics, RDF and RDF Schema defining a data model for relations between resources and a vocabulary definition language, and OWL adding more expressivity with concepts such as classes, properties, and cardinality restrictions. It also covers RDF syntaxes like Turtle and XML, and how RDF can represent implied claims from XML and facilitate interoperability between systems through its abstract model.
The OWL Web Ontology Language enables software engineers to define ontologies of domain knowledge which can be queried and reasoned over by software agents. OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
This presentation is about:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions -Property Restrictions
- Class Expressions -Cardinality Restrictions
This document provides an overview of the Web Ontology Language (OWL). It discusses the requirements for ontology languages, the three species of OWL (Lite, DL, Full), the syntactic forms of OWL, and key elements of OWL including classes, properties, restrictions, and boolean combinations. It also covers special properties, datatypes, and versioning information. OWL builds on RDF and RDF Schema to provide a stronger language for defining ontologies with greater machine interpretability on the semantic web.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/owl-web-ontology-language.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=5Kr4JzqDO_w
The lecture covers:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions? -Property Restrictions
- Class Expressions? -Cardinality Restrictions
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection TasksMLconf
Deep Learning Architectures for Semantic Relation Detection Tasks
Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for creating labelled datasets required for training deep learning models for classifying semantic relations between phrases. I will further present various neural network architectures that integrate morphological features into integrated path-based and distributional relation detection algorithms and demonstrate that this model outperforms state-of-the-art models in distinguishing semantic relations and is capable of efficiently handling multi-word expressions.
The document discusses the Web Ontology Language (OWL). It provides an overview of OWL, describing its three sublanguages - OWL Lite, OWL DL, and OWL Full - and their increasing expressiveness and reasoning complexity. The document also reviews the requirements for ontology languages and how OWL builds upon XML, RDF, and RDF Schema as the ontology language for the Semantic Web.
OWL 2 adds several new features to OWL including:
1) Cleaner language design with axiom-centered structural specification and functional style syntax.
2) Increased expressiveness through properties such as property chains, qualified cardinality restrictions, and datatype restrictions on properties.
3) Enhanced datatypes including new datatypes, datatype definitions, and data range combinations.
3) Profiles such as OWL 2 EL, QL, and RL that provide different tradeoffs between expressiveness and reasoning complexity.
The document discusses the role of reasoning for RDF validation. It finds that:
1. Reasoning can resolve or cause validation violations and reduce redundancy. Around 43% of constraint types benefit from reasoning.
2. Validating with reasoning is more computationally expensive, ranging from PTIME to N2EXPTIME complexity depending on the reasoning type.
3. Around 57% of constraint types are independent of the closed-world assumption, while 67% depend on the unique name assumption when validating. Validation results differ depending on the semantics assumed.
EDF2012 Irini Fundulaki - Abstract Access Control Models for Dynamic RDF Da...European Data Forum
This document presents an abstract access control model for controlling access to dynamic RDF datasets. The model uses abstract tokens and operators to represent how access labels of inferred triples are computed, avoiding the need to recompute labels when updates occur. It describes annotation models that associate access labels with triples, and how RDFS inference rules can be applied to infer new triples and labels. The abstract model is evaluated by mapping abstract expressions to concrete access control policies and operators. Pros include flexibility to experiment with policies and efficiency during updates, while cons include increased storage overhead. Future work involves implementing the approach in a database engine.
This talk was given by FORTH, Greece, at the European Data Forum (EDF) 2012 took place on June 6-7, 2012 in Copenhagen (Denmark) at the Copenhagen Business School (CBS).
Abstract:
Given the increasing amount of sensitive RDF data available on the Web, it becomes increasingly critical to guarantee secure access to this content. Access control is complicated when RDFS inference rules and other dependencies between access permissions of triples need to be considered; this is necessary, e.g., when we want to associate the access permissions of inferred triples with the ones that implied it. In this paper we advocate the use of abstract provenance models that are defined by means of abstract tokens operators to support fine grained access control for RDF graphs. The access label of a triple is a complex expression that encodes how said label was produced (i.e., the triples that contributed to its computation). This feature allows us to know exactly the effects of any possible change, thereby avoiding a complete recomputation of the labels when a change occurs. In addition, the same application can choose to enforce different access control policies or, different applications can enforce different policies on the same data, avoiding the recomputation of the label of a triple. Preliminary experiments have shown the applicability and benefits of our approach.
1) The Semantic Web technologies OWL 2 and Rule Interchange Format (RIF) have recently been finalized, while technical work is ongoing for SPARQL 1.1, RDFa 1.1, and connecting relational databases to RDF.
2) A workshop will discuss a possible revision to RDF to address issues like deprecation of features and addition of new constructs like named graphs.
3) The standards organization W3C is working on finalizing current technologies while exploring new areas like provenance and revisions to the core RDF standard based on discussion at the workshop.
On the off chance that you are keen on beginning your profession as a software engineer then cncwebworld is ideal for you. Join java classes in pune and take in every one of the essentials and gain the information you should construct your mastery for this programming dialect.
Click here: https://cncwebworld.com/pune/java-training-institute
This document provides an overview of SHACL (Shapes Constraint Language), a W3C recommendation for defining constraints on RDF graphs. It defines key SHACL concepts like shapes, targets, node shapes, property shapes and constraint components. Examples are provided to illustrate shape definitions and how validation of an RDF graph works against the defined shapes. The document summarizes the motivation for SHACL and inputs that influenced its development.
The document provides an overview of the work done at DERI Galway, including developing technologies like SIOC, ActiveRDF, and BrowseRDF to interconnect online communities and enable semantic applications. It also describes JeromeDL, a digital library system that uses semantic metadata and services to allow users to collaboratively browse and share knowledge.
Ontology and Ontology Libraries: a critical studyDebashisnaskar
This document provides an overview of ontology and ontology libraries. It discusses what ontologies are, languages for expressing ontologies like OWL, and tools for building ontologies such as Protégé. It also examines several ontology libraries including BioPortal for biomedical ontologies, OBO Foundry, oeGov for e-government, and TONES for general ontologies. Evaluation criteria for comparing ontology libraries and future challenges and opportunities are also reviewed.
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
This document provides an introduction and examples for SHACL (Shapes Constraint Language), a W3C recommendation for validating RDF graphs. It defines key SHACL concepts like shapes, targets, and constraint components. An example shape validates nodes with a schema:name and schema:email property. Constraints like minCount, maxCount, datatype, nodeKind, and logical operators like and/or are demonstrated. The document is an informative tutorial for learning SHACL through examples.
The document provides an overview of the Semantic Web including definitions of key concepts like RDF, RDFS, OWL, and applications. It describes the Semantic Web as extending the current web to give data well-defined meaning enabling computers and people to better cooperate. The layers of the Semantic Web are outlined including XML, RDF, RDFS, OWL, and how each builds on the previous. Examples of RDF graphs and syntax are given. Semantic Web applications like Swoogle, DBpedia, and Flickr are also mentioned.
Semantic Web: From Representations to ApplicationsGuus Schreiber
This document discusses semantic web representations and applications. It provides an overview of the W3C Web Ontology Working Group and Semantic Web Best Practices and Deployment Working Group, including their goals and key issues addressed. Examples of semantic web applications are also described, such as using ontologies to integrate information from heterogeneous cultural heritage sources.
The document discusses the W3C stack for representing metadata, with XML providing syntax but no semantics, RDF and RDF Schema defining a data model for relations between resources and a vocabulary definition language, and OWL adding more expressivity with concepts such as classes, properties, and cardinality restrictions. It also covers RDF syntaxes like Turtle and XML, and how RDF can represent implied claims from XML and facilitate interoperability between systems through its abstract model.
This document discusses modelling and representing social network data ontologically. It covers representing social individuals and relationships ontologically, as well as aggregating and reasoning with social network data. It discusses ontology languages like RDF, OWL, and FOAF that can be used to represent social network data and individuals semantically. It also talks about state-of-the-art approaches for representing network structure and attribute data, and the need for representations that can integrate different data sources and maintain identity.
This document provides an overview of description logics, which are a family of formal knowledge representation languages. Description logics are based on semantic networks and modal logic. They allow modeling a domain in terms of concepts, roles, and individuals. Description logics have a formal semantics, decidable reasoning problems, and highly optimized implemented systems. The document describes the basics of description logics, including concept and role constructors, knowledge base structure and semantics, the DL family of languages, and applications of description logic systems.
a system called natural language interface which transforms user's natural language question into SPARQL query
find related papers here https://sites.google.com/site/fadhlinams81/publication
Presentation at the ESWC 2011 PhD Symposium in May 2011, by Michael Schneider, FZI. Included are backup slides that have not been presented at the event. The corresponding PhD proposal can be found in the ESWC proceedings at <http: />. Alternatively, the PhD proposal can be downloaded from <http: />.
This document provides an overview of semantic web technologies for publishing data. It introduces the semantic web and describes semantic web languages like RDF, RDF Schema, and OWL. These languages allow modeling data as graphs and defining ontologies to provide unambiguous meaning to information. The document discusses using these languages to publish structured data on the web in ways that enable semantic annotation, integration, and reasoning across interconnected data sources.
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.
Bernhard Haslhofer is a postdoc researcher at Cornell University studying linked data, user-contributed data, and data interoperability. He discusses Linked (Open) Data, which uses URIs and RDF to publish and link structured data on the web. The key principles are using URIs to identify things, providing useful information about those URIs when dereferenced, and including links to other URIs. Enabling technologies include URIs, RDF, RDFS/OWL for vocabularies, SPARQL for querying, and best practices for publishing vocabularies and data. Useful tools are also presented.
Presentation of the paper "Reasoning in the OWL 2 Full Ontology Language using First-Order Automated Theorem Proving" by Michael Schneider, FZI Karlsruhe, and Geoff Sutcliffe, University of Miami, at the 23rd International Conference on Automated Deduction (CADE 23), August 2011.
The document defines ontologies as explicit descriptions of a domain that define concepts, properties, attributes, and constraints. It discusses the history of categorization in philosophy and the development of knowledge models like semantic nets and conceptual graphs. The document outlines different methods for building ontologies and different types of ontologies. It also discusses ontology tools like Protege and TopBraid Composer and how ontologies are used on the semantic web through languages like OWL.
The formulation of constraints and the validation of RDF data against these constraints is a common requirement and a much sought-after feature, particularly as this is taken for granted in the XML world. Recently, RDF validation as a research field gained speed due to shared needs of data practitioners from a variety of domains. For constraint formulation and RDF data validation, several languages exist or are currently developed. Yet, none of the languages is able to meet all requirements raised by data professionals.
We have published a set of constraint types that are required by diverse stakeholders for data applications. We use these constraint types to gain a better understanding of the expressiveness of solutions, investigate the role that reasoning plays in practical data validation, and give directions for the further development of constraint languages.
We introduce a validation framework that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type in a way that mappings from high-level constraint languages to an intermediate generic representation can be created straight-forwardly. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, and consists of a very simple conceptual model with a small lightweight vocabulary. We demonstrate that using another layer on top of SPARQL ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages.
This document discusses knowledge representation and management technologies for extended minds. It covers various aspects of knowledge representation including expressiveness versus computability and how the choice of representation limits what can be captured. Desired properties of knowledge representation systems include coverage, understandability, consistency, efficiency and ease of modification. The document then reviews historical attempts at knowledge representation and discusses current approaches like the semantic web, ontologies, topic maps and open source tools.
The document discusses Simple Knowledge Organization System (SKOS), machine-readable data in HTML documents using RDFa, Microformats, and Microdata, and how search engines can process this machine-readable data. It also briefly mentions the Facebook Graph API and Open Graph Protocol.
The document discusses Simple Knowledge Organization System (SKOS), machine-readable data in web pages like RDFa and microformats, and Linked Data. It provides an overview of SKOS including that it allows concepts to be composed and published as Linked Data, identifies concepts with URIs, labels concepts with strings, and links concepts semantically.
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 defines key terms related to semantic technologies and the semantic web including:
- Linked Open Data (LOD) which publishes open data according to semantic web standards and links it to other sources to create a web of data.
- LOD2, an EU project developing infrastructure for building LOD.
- OWL, a language for more expressive semantic modeling.
- R2RML, a standard for mapping data in relational databases to RDF.
- RDF, the standard data model using triples to represent information.
An ontology is a computational artifact used to describe a conceptualization of some part of the world via precise, descriptive statements. In this presentation, we discuss the features of the W3C's Ontology Web Language (OWL) and how it can be used to reduce ambiguity in the semantics (i.e., the meaning) of Data Dictionary terminology.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
I claim that none of the commonly used embedding methods capture any semantics.
It's fine if you want to move from a symbolic to a numeric or geometric representation, but when you do, don't throw the semantic baby out with the symbolic bathwater.
I argue that a useful definition of semantics is "predictable inference". This makes it possible to have semantics outside a logical framework.
A methodological warning from 1976: don't fool yourself that wishful mnemonics in your knowledge graph are "semantics". Therefore, knowledge graphs without a schema/ontology is just a data graph, without much semantics.
Finally, a discussion of some embedding methods that do manage to take semantics into account (TransOWL, ball embeddings like ELEm and EmEL++, and box embeddings like BoxEL and Box^2EL.
So: even if you do move to a non-symbolic representation (numerical, geometric), make sure you keep the semantics: don't throw the semantic baby out with the symbolic bathwater.
Logo's of companies for which I have verified evidence that they use knowledge graphs and ontologies in production. This list is (of course) incomplete.
Modular design patterns for systems that learn and reason: a boxologyFrank van Harmelen
A set of modular design patterns that can describe a large number of neuro-symbolic architectures from the literature. Corresponding paper is at https://arxiv.org/abs/2102.11965
This document discusses empirical semantics and insights that can be gained from observing large knowledge bases. It notes that formal semantics often does not accurately model real-world knowledge and proposes some challenges for developing alternative semantic models. Specifically, it suggests empirical study has shown that identity, meaningful names, and different predicate patterns are not adequately captured. The goal is to develop descriptive theories of knowledge based on observation rather than prescriptive theories.
We now have larger Knowledge Bases than ever before. (10 billion facts is now a small number).
We now have the instruments to observe and analyse these very large Knowledge Bases.
We can use these insights for better tools for querying, inferencing, publishing, maintaining, visualising and explaining.
The end of the scientific paper as we know it (or not...)Frank van Harmelen
Two talks in one: the first talk expanding on the great promises of nanopublications, the second talk pointing out why much of that is too difficult (and some of it wrong).
On the nature of AI, and the relation between symbolic and statistical approa...Frank van Harmelen
The document discusses the differences between symbolic and statistical approaches to artificial intelligence (AI). It notes that while modern AI is dominated by machine learning, the two approaches have different strengths and weaknesses. Symbolic AI is better for reasoning, planning, and explanation, while statistical AI excels at pattern recognition, motor skills, and tasks using large datasets. The semantic web allowed symbolic knowledge representation to scale up significantly using web technologies, but introduced challenges that require machine learning techniques to address.
The end of the scientific paper as we know it (in 4 easy steps)Frank van Harmelen
Scientific publishing hasn't really changed in over 300 years. By changing papers from a single narrative text (readable by people only) into a rich network of snippets of knowledge ("nano-publications") we would allow computers to become our colleagues instead of just our tools
An increasing number of patients suffer from multiple diseases at the same time. This makes their treatment much more complex, and the standard medical treatment guidelines no longer apply (they are typically written for patients with just a single disease). We present computer-based techniques for analysing medical guidelines to detect how multiple guidelines may interact in unexpected ways, and how Linked Open Data can be used to recognise and avoid such adverse effects.
The Web of Data: do we actually understand what we built?Frank van Harmelen
Despite its obvious success (largest knowledge base ever built, used in practice by companies and governments alike), we actually understand very little of the structure of the Web of Data. Its formal meaning is specified in logic, but with its scale, context dependency and dynamics, the Web of Data has outgrown its traditional model-theoretic semantics.
Is the meaning of a logical statement (an edge in the graph) dependent on the cluster ("context") in which it appears? Does a more densely connected concept (node) contain more information? Is the path length between two nodes related to their semantic distance?
Properties such as clustering, connectivity and path length are not described, much less explained by model-theoretic semantics. Do such properties contribute to the meaning of a knowledge graph?
To properly understand the structure and meaning of knowledge graphs, we should no longer treat knowledge graphs as (only) a set of logical statements, but treat them properly as a graph. But how to do this is far from clear.
In this talk, I report on some of our early results on some of these questions, but I ask many more questions for which we don't have answers yet.
Talk given at the SSSW 2013 Semantic Web Summerschool.
Part 1: What is "Semantic Web" (in 4 principles and 1 movie)
Part 2: What question can we ask now that we couldn't ask 10 years ago
Part 3: Treat Computer Science as a *science*, not just as engineering!
(this part a short version of http://slidesha.re/SaUhS4 )
Knowledge Engineering rediscovered, Towards Reasoning Patterns for the Semant...Frank van Harmelen
We show how it is possible to apply the problem solving patterns from knowledge engineering to systems developed on the semantic web. This give us re-usable problem solving patterns for the semantic web, and would greatly help us to build and understand such systems.
I argue why I think that Computer Science (or better: Informatics) is a "natural science", in the same sense that physics, astronomy, biology, psychology and sociology are a natural science: they study a part of the world around us. In that same sense, I think Informatics studies a part of the world around us.
For a similar talk (including script), but more aimed at a Semantic Web audience in particular, see http://www.cs.vu.nl/~frankh/spool/ISWC2011Keynote/
(or http://videolectures.net/iswc2011_van_harmelen_universal/ for a video registration)
How the Web can change social science research (including yours)Frank van Harmelen
A presentation for a group of PhD students from the Leibniz Institutes (section B, social sciences) to discuss how they could use the Web, and even better the Web of Data, as an instrument in their research.
The document discusses and refutes four popular fallacies about the Semantic Web. It clarifies that the Semantic Web enforces languages but not meanings, does not require a single predefined meaning for terms but allows for different vocabularies to be bridged, and does not require users to understand formalized knowledge representation as this is done automatically behind the scenes. It also notes the Semantic Web does not require manually marking up all existing web pages as techniques are being developed to automatically add semantic markup.
The document discusses open data for open government and the benefits of publishing government data in a semantic, linked, and open format on the web. It provides examples of open data initiatives in the US, UK, and other countries that have led to the development of many applications by third parties using publicly available government data. The speaker advocates that governments publish not just documents but the underlying data to allow others to build new sites and applications to make use of the information.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
OWL briefing
1. OWL Briefing
Frank van Harmelen
Vrije Universiteit Amsterdam
2. vocabularies:
Ontologies
Formal, machine
processable
explicit specification concepts, properties,
relations, functions
of a
Consensual
shared
knowledge
conceptualisation Abstract model of
some domain
5. Stack of languages
XML:
• Surface syntax, no semantics
XML Schema:
• Describes structure of XML documents
RDF:
• Datamodel for “relations” between “things”
RDF Schema:
• RDF Vocabulary Definition Language
OWL:
• A more expressive
Vocabulary Definition Language
6. Bluffer’s guide to RDF (1)
Object ->Attribute-> Value triples
Author-of
pers05 ISBN...
objects are web-resources
Value is again an Object:
• triples can be linked
• data-model = graph
Author-of Publ-
pers05 ISBN... by MIT
Au t
hor
-of Pub l-
ISBN...
by
7. Bluffer’s guide to RDF (2)
Every identifier is a URL
= world-wide unique naming!
Has XML syntax <rdf:Description rdf:about=“#pers05”>
<authorOf>ISBN...</authorOf>
</rdf:Description>
Any statement can be an object
• graphs can be nested
claims Author-of
NYT pers05 ISBN...
8. What does RDF Schema add?
• Defines vocabulary for RDF
• Organizes this vocabulary in a
typed hierarchy
• Class, subClassOf, type
• Property, subPropertyOf
• domain, range
Person
subClassOf
subClassOf
domain range
Supervisor s u p e r v is e s Student
type type
s u p e r v is e s
Frank Marta
9. Things RDF(S) can’t do
(in)equality
enumeration
boolean algebra
• Union, complement
number restrictions
• Single-valued/multi-valued
• Optional/required values
inverse, symmetric, transitive
…
11. Design Goals for OWL
Shareable
Changing over time
Interoperability between ontologies
Inconsisteny detection (requires a logic)
Balancing expressivity and complexity
Ease of use
Compatible with existing standards
Internationalisation
12. Requirements for OWL
Ontologies are objects on the Web
with their own meta-data, versioning, etc
Ontologies are extendable
They contain classes, properties,
data-types, range/domain, individuals
Equality (for classes, for individuals)
Classes as instances
Cardinality constraints
XML syntax
14. Layered language
OWL Lite:
Classification hierarchy
Simple constraints Full
OWL DL:
DL
Maximal expressiveness
While maintaining tractability
Lite
Standard formalisation
OWL Full:
Very high expressiveness
Loosing tractability
Syntactic layering
Non-standard formalisation
Semantic layering
All syntactic freedom of RDF
(self-modifying)
15. OWL Light Language Layers
(sub)classes, individuals
(sub)properties, domain, RDF Schema
range
conjunction
(in)equality Full
cardinality 0/1
datatypes DL
inverse, transitive, symmetric
hasValue
someValuesFrom Lite
allValuesFrom
OWL DL OWL Full
Negation Allow meta-classes etc
Disjunction
Full Cardinality
Enumerated types
16. Language Layers: Full
No restriction on use of vocabulary
(as long as legal RDF)
Full
• Classes as instances
(and much more)
RDF style model theory
• Reasoning using FOL engines
• via axiomatisation
• Semantics should correspond with OWL DL
for suitably restricted KBs
17. Language Layers: DL
Use of OWL vocabulary restricted
• Can’t be used to do “nasty things”
(I.e., modify OWL)
• No classes as instances
DL
• Defined by abstract syntax
Standard FOL model theory (definitive)
• Direct correspondence with FOL
• Reasoning via DL engines
• Reasoning for full language via FOL engines
• No need for axiomatisation (unlike full)
• Would need built in datatypes for performance
18. Language Layers: Lite
No explicit negation or union
Restricted cardinality (0/1)
No nominals (oneOf)
Semantics as in DL Lite
• Reasoning via DL engines
(+datatypes)
• E.g., FaCT, RACER, Cerebra
21. Different syntaxes
RDF
• Official exchange syntax
• Hard for humans
UML
• Large user base
• Masahiro Hori, IBM Japan
XML
• Not the RDF syntax
• Better for humans
• Masahiro Hori, IBM Japan
“Abstract” syntax
• Human readable/writeable
22. Things OWL doesn’t do
– default values
– closed world option
– property chaining
– arithmetic
– string operations
– partial imports
– view definitions
– procedural
attachment