The document summarizes a seminar on ontology mapping presented by Samhati Soor. The seminar covered the need for ontology mapping due to the proliferation of ontologies, and the purpose of mapping ontologies to achieve interoperability and sharing knowledge. It defined ontologies and ontology mapping and discussed categories of mapping including between global and local ontologies, between local ontologies, and for merging ontologies. Tools for ontology mapping discussed included GLUE and SAM. Evaluation criteria and challenges of ontology mapping were also summarized along with conclusions and references.
The document summarizes and compares schema matching and ontology mapping. It discusses how schema matching approaches can be applied to ontology mapping given the similarities between schemas and ontologies. The document outlines different categories of schema matching techniques (element-based, structure-based) and provides examples. It also summarizes several ontology mapping tools and approaches that utilize different matching strategies like string, structure, and semantic similarity.
Yang Yu is proposing research on improving machine learning based ontology mapping by automatically obtaining training samples from the web. The proposed system would parse two input ontologies to generate queries to search engines and collect documents to use as samples for each ontology class. These samples would then be used to train text classifiers, which would produce probabilistic mappings between classes in the two ontologies. The results would be evaluated by comparing to mappings from human experts. Current work involves exploring alternative text classification tools and ways to utilize the probabilistic mapping values generated by the classifiers.
An ontology is a specification of a conceptualization that allows us to represent domain knowledge so that we can share a common understanding, enable reuse, make domain assumptions explicit, and separate domain knowledge from operational knowledge. Ontologies offer reasoning services like consistency checking, subsumption, and query answering that are different from those found in XML and relational databases. OWL ontologies use semantics rather than just syntax to represent knowledge about concepts, individuals, and relationships between them.
This document discusses ontology mapping. It begins with an introduction to the semantic web and ontologies. Ontology mapping is important for allowing different ontologies to be aligned and related. There are different types of ontology mapping including alignment, merging, and mapping. The document then surveys some popular ontology mapping techniques including GLUE, PROMPT, and QOM. It evaluates these techniques and discusses their inputs, outputs, and approaches. The document concludes that semantic web research is important for advancing web technologies and realizing the goals of web 3.0. Future work could involve developing new ontology mapping techniques and publishing research on existing mapping methods.
This document summarizes a workshop on data integration using ontologies. It discusses how data integration is challenging due to differences in schemas, semantics, measurements, units and labels across data sources. It proposes that ontologies can help with data integration by providing definitions for schemas and entities referred to in the data. Core challenges discussed include dealing with multiple synonyms for entities and relationships between biological entities that depend on context. The document advocates for shared community ontologies that can be extended and integrated to facilitate flexible and responsive data integration across multiple sources.
The document discusses ontology alignment, which is the process of finding correspondences between concepts in different ontologies to allow them to be used together. It notes that there is no single unified ontology, so alignment helps integrate overlapping conceptualizations. The key constructs for expressing alignments are relations like equivalence and subclass between concepts. Techniques discussed for finding mappings include string-based, linguistic/language-based, taxonomy comparison, and using example instances. The challenges of alignment evaluation and interpretation of results are also covered.
The document discusses ontology matching, which is the process of finding relationships between entities in different ontologies. It describes various techniques for ontology matching including basic techniques that operate at the element-level or structure-level, as well as classifications of matching techniques based on the type of input used and level of interpretation. The document also provides examples of commonly used methods for ontology matching like string-based, language-based, and structure-based techniques.
The document summarizes a seminar on ontology mapping presented by Samhati Soor. The seminar covered the need for ontology mapping due to the proliferation of ontologies, and the purpose of mapping ontologies to achieve interoperability and sharing knowledge. It defined ontologies and ontology mapping and discussed categories of mapping including between global and local ontologies, between local ontologies, and for merging ontologies. Tools for ontology mapping discussed included GLUE and SAM. Evaluation criteria and challenges of ontology mapping were also summarized along with conclusions and references.
The document summarizes and compares schema matching and ontology mapping. It discusses how schema matching approaches can be applied to ontology mapping given the similarities between schemas and ontologies. The document outlines different categories of schema matching techniques (element-based, structure-based) and provides examples. It also summarizes several ontology mapping tools and approaches that utilize different matching strategies like string, structure, and semantic similarity.
Yang Yu is proposing research on improving machine learning based ontology mapping by automatically obtaining training samples from the web. The proposed system would parse two input ontologies to generate queries to search engines and collect documents to use as samples for each ontology class. These samples would then be used to train text classifiers, which would produce probabilistic mappings between classes in the two ontologies. The results would be evaluated by comparing to mappings from human experts. Current work involves exploring alternative text classification tools and ways to utilize the probabilistic mapping values generated by the classifiers.
An ontology is a specification of a conceptualization that allows us to represent domain knowledge so that we can share a common understanding, enable reuse, make domain assumptions explicit, and separate domain knowledge from operational knowledge. Ontologies offer reasoning services like consistency checking, subsumption, and query answering that are different from those found in XML and relational databases. OWL ontologies use semantics rather than just syntax to represent knowledge about concepts, individuals, and relationships between them.
This document discusses ontology mapping. It begins with an introduction to the semantic web and ontologies. Ontology mapping is important for allowing different ontologies to be aligned and related. There are different types of ontology mapping including alignment, merging, and mapping. The document then surveys some popular ontology mapping techniques including GLUE, PROMPT, and QOM. It evaluates these techniques and discusses their inputs, outputs, and approaches. The document concludes that semantic web research is important for advancing web technologies and realizing the goals of web 3.0. Future work could involve developing new ontology mapping techniques and publishing research on existing mapping methods.
This document summarizes a workshop on data integration using ontologies. It discusses how data integration is challenging due to differences in schemas, semantics, measurements, units and labels across data sources. It proposes that ontologies can help with data integration by providing definitions for schemas and entities referred to in the data. Core challenges discussed include dealing with multiple synonyms for entities and relationships between biological entities that depend on context. The document advocates for shared community ontologies that can be extended and integrated to facilitate flexible and responsive data integration across multiple sources.
The document discusses ontology alignment, which is the process of finding correspondences between concepts in different ontologies to allow them to be used together. It notes that there is no single unified ontology, so alignment helps integrate overlapping conceptualizations. The key constructs for expressing alignments are relations like equivalence and subclass between concepts. Techniques discussed for finding mappings include string-based, linguistic/language-based, taxonomy comparison, and using example instances. The challenges of alignment evaluation and interpretation of results are also covered.
The document discusses ontology matching, which is the process of finding relationships between entities in different ontologies. It describes various techniques for ontology matching including basic techniques that operate at the element-level or structure-level, as well as classifications of matching techniques based on the type of input used and level of interpretation. The document also provides examples of commonly used methods for ontology matching like string-based, language-based, and structure-based techniques.
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...Khirulnizam Abd Rahman
Application of Ontology in Semantic Information Retrieval
by Prof Shahrul Azman from FSTM, UKM
Presentation for MyREN Seminar 2014
Berjaya Hotel, Kuala Lumpur
27 November 2014
This document summarizes a survey on string similarity matching search techniques. It discusses how string similarity matching is used to find relevant information in text collections. The document reviews different algorithms for string matching, including edit distance, NR-grep, n-grams, and approaches based on hashing and locality-sensitive hashing. It analyzes techniques like pattern matching, threshold-based joins, and vector representations. The goal is to present an overview of the field and compare algorithm performance for similarity searches.
The document presents a new ontology matching system based on a multi-agent architecture. The system takes ontologies described in XML, RDF Schema, and OWL as input. It uses multiple matchers and filtering to generate mappings between ontology entities. The mappings are then validated. The system is implemented as a multi-agent system with different agent types responsible for resources, matching, generating mappings, and filtering/validating mappings. The architecture allows for robust, flexible, and scalable ontology matching.
This document discusses ontology-based data access. It begins by defining ontology as a representation of concepts and relationships that define a domain. It then provides examples of ontology elements like concepts, attributes, and relations. It describes how ontologies can be used to share understanding, enable knowledge reuse, and separate domain from operational knowledge. The document outlines the process for developing ontologies including scope, capture, encoding, integration, and evaluation. It discusses using ontologies to provide a user-oriented view of data and facilitate query access across data sources. The document concludes by discussing ongoing work on semantic query analysis and graphical ontology mapping tools.
Using Text Comprehension Model for Learning Concepts, Context, and Topic of...Kent State University
Concepts in web ontologies help machines to un-
derstand data through the meanings they hold. Furthermore,
learning contexts and topics of web documents also have helped
in better semantic-oriented structuring and retrieval of data on
the web. In this short paper we present a novel approach for
domain-independent open learning of domain concepts, context
and topic of any given web document. Our approach is based on a
computational version of the Construction-Integration (CI) model
of text comprehension. Our proposed system mimics the way
humans learn the meanings of textual units and identify domain
concepts, contexts and topics in the form of semantic networks.
We apply our system on a number of web documents with a
range of topics and domains. The resulting semantic networks
provide a quantitative and qualitative insights into the nature of
the given web documents.
semantic data integration the process of using a conceptual representation of the data and of their relationships to eliminate possible heterogeneities.
The document introduces ontology and describes what it is from both philosophical and computer science perspectives. An ontology in computers consists of a vocabulary to describe a domain, specifications of the meaning of terms, and constraints capturing additional knowledge about the domain. It then provides an example ontology and discusses applications of ontologies such as for the semantic web. It also discusses important considerations for building ontologies such as collaboration, versioning, and ease of use.
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
Lect6-An introduction to ontologies and ontology developmentAntonio Moreno
The document provides an overview of ontologies and ontology development:
1. It defines ontologies as explicit specifications of conceptualizations in a domain that define concepts, properties, attributes, and relationships to enable knowledge sharing.
2. Ontology components include concepts, properties, restrictions, and individuals. Ontologies can range from single large ontologies to several specialized smaller ones.
3. OWL is introduced as the standard language for representing ontologies, with features like classes, properties, restrictions, and logical operators.
4. A general methodology for ontology development is outlined, including determining scope, reusing existing ontologies, enumerating terms, and defining classes, properties, and other components in an iterative
(1) The document discusses the Semantic Web, ontologies, and ontology learning. It defines the Semantic Web as an extension of the current web that gives information well-defined meaning. (2) Ontologies are formal specifications of concepts and relations that provide shared meanings between machines and humans. (3) Ontology learning is the automatic or semi-automatic process of extracting ontological concepts and relations from text to build or enrich ontologies. The document outlines methods for ontology learning and its applications.
SPARQL is a semantic query language used to retrieve and manipulate data stored in RDF format. An ontology represents concepts within a domain and provides specific meanings of terms within that domain, such as modeling playing cards within a poker ontology. While ontologies are similar to object-oriented class hierarchies, ontologies are meant to evolve constantly to represent diverse internet data, whereas class hierarchies evolve slowly from structured corporate databases. The Protege tool can be used to create domain-specific ontologies and publish them on the web with the .owl extension to then run SPARQL queries to retrieve information.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
Ontology Building and its Application using HozoKouji Kozaki
The document provides information about an upcoming tutorial on ontology building and its applications using the Hozo ontology development tool. The tutorial will take place on November 9th, 2014 in Chiang Mai, Thailand and will cover how to build ontologies using Hozo, some characteristic functions of Hozo, and examples of ontology-based application developments. The tutorial agenda outlines the topics to be covered in each time block, including hands-on experience building ontologies with Hozo.
Many applications required integration of data from different sources, such as data mining and data / information fusion, etc., and the problem facing any project like this is that data structure different way and the terms and their meanings different from each other, and in this paper we will discuss the most important problems and how solve it using ontology.
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.
Ekaw ontology learning for cost effective large-scale semantic annotationShahab Mokarizadeh
This document discusses using ontology learning to semantically annotate a corpus of 15,000 web service interfaces. It proposes extracting terms from the interfaces at a fine-grained level and using pattern-based methods to discover taxonomic and non-taxonomic relations to automatically generate an ontology. The method achieved 62% accuracy for common concepts and 71% for common instances compared to a golden ontology.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
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.
Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.
Introduction to Ontology Concepts and TerminologySteven Miller
The document introduces an ontology tutorial that will cover basic concepts of the Semantic Web, Linked Data, and the Resource Description Framework data model as well as the ontology languages RDFS and OWL. The tutorial is intended for information professionals who want to gain an introductory understanding of ontologies, ontology concepts, and terminology. The tutorial will explain how to model and structure data as RDF triples and create basic RDFS ontologies.
Project number: 224348
Project acronym: AEGIS
Project title: Open Accessibility Everywhere: Groundwork, Infrastructure, Standards
Starting date: 1 September 2008
Duration: 48 Months
AEGIS is an Integrated Project (IP) within the ICT programme of FP7
Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...hamidnazary2002
This document discusses integrating enterprise and data mining ontologies to extract actionable knowledge. It notes that existing data mining techniques provide large volumes of knowledge but much of it is not useful for making business decisions. The objectives are to 1) design an artifact to formally apply business understanding in data mining and 2) semi-automate the business understanding phase to help users. The expected outcomes are an enterprise ontology and relations between enterprise and data mining ontologies to bridge the gap between business needs and data mining results.
Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...Khirulnizam Abd Rahman
Application of Ontology in Semantic Information Retrieval
by Prof Shahrul Azman from FSTM, UKM
Presentation for MyREN Seminar 2014
Berjaya Hotel, Kuala Lumpur
27 November 2014
This document summarizes a survey on string similarity matching search techniques. It discusses how string similarity matching is used to find relevant information in text collections. The document reviews different algorithms for string matching, including edit distance, NR-grep, n-grams, and approaches based on hashing and locality-sensitive hashing. It analyzes techniques like pattern matching, threshold-based joins, and vector representations. The goal is to present an overview of the field and compare algorithm performance for similarity searches.
The document presents a new ontology matching system based on a multi-agent architecture. The system takes ontologies described in XML, RDF Schema, and OWL as input. It uses multiple matchers and filtering to generate mappings between ontology entities. The mappings are then validated. The system is implemented as a multi-agent system with different agent types responsible for resources, matching, generating mappings, and filtering/validating mappings. The architecture allows for robust, flexible, and scalable ontology matching.
This document discusses ontology-based data access. It begins by defining ontology as a representation of concepts and relationships that define a domain. It then provides examples of ontology elements like concepts, attributes, and relations. It describes how ontologies can be used to share understanding, enable knowledge reuse, and separate domain from operational knowledge. The document outlines the process for developing ontologies including scope, capture, encoding, integration, and evaluation. It discusses using ontologies to provide a user-oriented view of data and facilitate query access across data sources. The document concludes by discussing ongoing work on semantic query analysis and graphical ontology mapping tools.
Using Text Comprehension Model for Learning Concepts, Context, and Topic of...Kent State University
Concepts in web ontologies help machines to un-
derstand data through the meanings they hold. Furthermore,
learning contexts and topics of web documents also have helped
in better semantic-oriented structuring and retrieval of data on
the web. In this short paper we present a novel approach for
domain-independent open learning of domain concepts, context
and topic of any given web document. Our approach is based on a
computational version of the Construction-Integration (CI) model
of text comprehension. Our proposed system mimics the way
humans learn the meanings of textual units and identify domain
concepts, contexts and topics in the form of semantic networks.
We apply our system on a number of web documents with a
range of topics and domains. The resulting semantic networks
provide a quantitative and qualitative insights into the nature of
the given web documents.
semantic data integration the process of using a conceptual representation of the data and of their relationships to eliminate possible heterogeneities.
The document introduces ontology and describes what it is from both philosophical and computer science perspectives. An ontology in computers consists of a vocabulary to describe a domain, specifications of the meaning of terms, and constraints capturing additional knowledge about the domain. It then provides an example ontology and discusses applications of ontologies such as for the semantic web. It also discusses important considerations for building ontologies such as collaboration, versioning, and ease of use.
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
Lect6-An introduction to ontologies and ontology developmentAntonio Moreno
The document provides an overview of ontologies and ontology development:
1. It defines ontologies as explicit specifications of conceptualizations in a domain that define concepts, properties, attributes, and relationships to enable knowledge sharing.
2. Ontology components include concepts, properties, restrictions, and individuals. Ontologies can range from single large ontologies to several specialized smaller ones.
3. OWL is introduced as the standard language for representing ontologies, with features like classes, properties, restrictions, and logical operators.
4. A general methodology for ontology development is outlined, including determining scope, reusing existing ontologies, enumerating terms, and defining classes, properties, and other components in an iterative
(1) The document discusses the Semantic Web, ontologies, and ontology learning. It defines the Semantic Web as an extension of the current web that gives information well-defined meaning. (2) Ontologies are formal specifications of concepts and relations that provide shared meanings between machines and humans. (3) Ontology learning is the automatic or semi-automatic process of extracting ontological concepts and relations from text to build or enrich ontologies. The document outlines methods for ontology learning and its applications.
SPARQL is a semantic query language used to retrieve and manipulate data stored in RDF format. An ontology represents concepts within a domain and provides specific meanings of terms within that domain, such as modeling playing cards within a poker ontology. While ontologies are similar to object-oriented class hierarchies, ontologies are meant to evolve constantly to represent diverse internet data, whereas class hierarchies evolve slowly from structured corporate databases. The Protege tool can be used to create domain-specific ontologies and publish them on the web with the .owl extension to then run SPARQL queries to retrieve information.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
Ontology Building and its Application using HozoKouji Kozaki
The document provides information about an upcoming tutorial on ontology building and its applications using the Hozo ontology development tool. The tutorial will take place on November 9th, 2014 in Chiang Mai, Thailand and will cover how to build ontologies using Hozo, some characteristic functions of Hozo, and examples of ontology-based application developments. The tutorial agenda outlines the topics to be covered in each time block, including hands-on experience building ontologies with Hozo.
Many applications required integration of data from different sources, such as data mining and data / information fusion, etc., and the problem facing any project like this is that data structure different way and the terms and their meanings different from each other, and in this paper we will discuss the most important problems and how solve it using ontology.
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.
Ekaw ontology learning for cost effective large-scale semantic annotationShahab Mokarizadeh
This document discusses using ontology learning to semantically annotate a corpus of 15,000 web service interfaces. It proposes extracting terms from the interfaces at a fine-grained level and using pattern-based methods to discover taxonomic and non-taxonomic relations to automatically generate an ontology. The method achieved 62% accuracy for common concepts and 71% for common instances compared to a golden ontology.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
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.
Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.
Introduction to Ontology Concepts and TerminologySteven Miller
The document introduces an ontology tutorial that will cover basic concepts of the Semantic Web, Linked Data, and the Resource Description Framework data model as well as the ontology languages RDFS and OWL. The tutorial is intended for information professionals who want to gain an introductory understanding of ontologies, ontology concepts, and terminology. The tutorial will explain how to model and structure data as RDF triples and create basic RDFS ontologies.
Project number: 224348
Project acronym: AEGIS
Project title: Open Accessibility Everywhere: Groundwork, Infrastructure, Standards
Starting date: 1 September 2008
Duration: 48 Months
AEGIS is an Integrated Project (IP) within the ICT programme of FP7
Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...hamidnazary2002
This document discusses integrating enterprise and data mining ontologies to extract actionable knowledge. It notes that existing data mining techniques provide large volumes of knowledge but much of it is not useful for making business decisions. The objectives are to 1) design an artifact to formally apply business understanding in data mining and 2) semi-automate the business understanding phase to help users. The expected outcomes are an enterprise ontology and relations between enterprise and data mining ontologies to bridge the gap between business needs and data mining results.
X-SOM is an ontology-based data integration system that uses ontologies to mediate between different data schemas by mapping concepts between source ontologies through properties like equivalentClass and subclassOf. It combines multiple ontology matching techniques using a neural network and performs semantic consistency checks to identify and resolve inconsistencies that may arise from the mappings. The document evaluates X-SOM's performance on ontology mapping tasks and discusses areas for further improvement.
This document discusses local search and mobile usage. It contains questions and answers about searching for local businesses like doctors and hotels from desktop versus mobile. Mobile is more likely to be used for immediate local searches when seeking things like food or gas. Location is prioritized over other factors like reviews or branding on mobile. The document also discusses how search results can differ depending on if the search originates from desktop or mobile due to differences in location.
[DSBW Spring 2010] Unit 10: XML and Web And beyondCarles Farré
The document provides an overview of XML, web services, and the semantic web. It defines XML as a flexible text format used to represent structured information. It describes web services as software systems that support machine-to-machine interactions over a network using standards like SOAP, WSDL, and UDDI. It introduces the semantic web as using standards like RDF, RDF Schema, and OWL to make web resources more machine-understandable to enable greater data sharing and interoperability.
A Data Fusion System for Spatial Data Mining, Analysis and Improvement Silvij...Beniamino Murgante
The document describes a data fusion system that automatically fuses imperfect geospatial data from multiple sources to produce a single, higher quality dataset. The system has three main components - preprocessing input data, filtering/fusing the data, and validating the merged output. It uses a modular architecture and processes data through conversion, analysis, relationship detection, attribute transfer, and quality assessment steps. The system provides both command line and graphical user interfaces and aims to improve on existing data through automated harmonization.
Horizontal Integration of Big Intelligence DataDataTactics
This document discusses the use of ontology to enable horizontal integration of big intelligence data. It describes challenges in integrating diverse data sources, known as big data, due to data silos and differing lexicons and semantics. The authors propose an approach called semantic enhancement that uses ontologies to annotate data from multiple sources without changing the underlying data. This allows the data to be queried and analyzed together by leveraging the shared semantics defined in the ontologies.
This document discusses localization and mapping for robotics. It introduces topics like gyroscopes, odometry, GPS, and landmarks for localization. It discusses uncertainty models using Gaussian distributions and error propagation. Methods for belief representation are presented, including parametric single/multi hypothesis and non-parametric particle filters. Environment representations like continuous, discrete, and topological maps are described. The document provides an example of Google Maps and discusses belief representation in topological maps. It also covers multi-hypothesis belief representation, sensor data to topological maps using exact and Voronoi decompositions, and adaptive cell-size. The document assigns homework on navigation algorithms and reactive vs. deliberative planning.
Data integration involves providing unified access to data stored across multiple heterogeneous data sources. There are several data integration architectures including data warehouses, virtual mediators, and peer-to-peer integration. Key challenges in data integration include modeling the global schema, source schemas, and mappings between them, as well as reformulating queries over the global schema to retrieve answers from the source schemas. Languages for modeling schema mappings include GAV, LAV, and GLAV, with different advantages for query reformulation and modularity when new sources are added.
Pal gov.tutorial2.session13 2.gav and lav integrationMustafa Jarrar
This document discusses Global-As-View (GAV) and Local-As-View (LAV) integration approaches. GAV defines the global schema in terms of the local schemas by writing views over the local schemas. LAV defines the local schemas in terms of the global schema by writing views from the global schema to the local schemas. The document provides an example of each approach and discusses how queries are executed differently under GAV versus LAV.
DSBW Final Exam (Spring Sementer 2010)Carles Farré
The document describes a UX model for a "light" version of Twitter called Chirper. It provides details on the following screens and functionality:
1. Home - The main page where users can see chirps from those they follow, send new chirps, search topics, and view their profile and followers/following.
2. Profile - A screen to view and edit a user's profile details.
3. User Page - A screen displaying a user's chirps and profile.
4. People - A screen listing users a profile follows/follows them.
It also includes instructions to design the internal class diagram and sequence diagrams for these screens and navigation between them
The document discusses concepts, functions, architecture, and design of distributed database management systems (DDBMS). It covers topics such as data allocation strategies, distributed relational database design, levels of transparency provided by DDBMSs, and Date's 12 rules for distributed database management. The overall goal of a DDBMS is to manage distributed databases across a computer network while hiding the distribution from users.
This document provides an overview of localization and mapping techniques for robotics, including:
- Markov localization and particle filters for estimating robot location as a probability distribution.
- The Kalman filter for optimally fusing uncertain sensor measurements and updating location estimates.
- Simultaneous localization and mapping (SLAM) and the "hen-egg" problem of needing a map to localize and a location to build a map.
- Feature-based SLAM approaches that build maps from distinct environmental features.
- FastSLAM which uses a particle filter to estimate robot location and build maps from sensor measurements.
- Key challenges in SLAM like recognizing previously visited places and handling dynamic environments.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/data-schema-integration.html and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=VJtF_7ptln4
The lecture covers:
- Challenges of Data Schema Integration
- Framework for Schema Integration
- Schema Transformation
- Reverse Engineering
This document discusses various topics related to distributed databases and the web, including:
- The structure and properties of web data, including its lack of strict schemas, volatility, scale, and difficulty of querying.
- Models for representing web data, including graph-based and semistructured models.
- Architectures for web search engines, including crawling, indexing, and ranking web pages.
- Approaches for querying web data, including structured query languages, semantic data querying, and question answering systems.
- Issues around searching the "hidden web" or deep web through techniques like crawling search interfaces and metasearching.
- The use of XML for representing web and other distributed data, and techniques for querying
Distributed databases allow data to be stored across multiple computers or sites connected through a network. The data is logically interrelated but physically distributed. A distributed database management system (DDBMS) makes the distribution transparent to users and allows sites to operate autonomously while participating in global applications. Key aspects of DDBMS include distributed transactions, concurrency control, data fragmentation and replication, distributed query processing, and ensuring transparency of the distribution.
This document discusses distributed object database management systems (ODBMS). It covers fundamental ODBMS concepts like objects, classes, and object distribution. Object distribution can be based on fragmenting state, method definitions, or method implementations. The document also discusses object server and page server architectures, cache consistency algorithms, object identifier management, object migration, distributed object storage, object query processing, and transaction management in distributed ODBMS.
This document outlines the key concepts of distributed database management systems (DBMS). It begins with an introduction and background on relational database systems and computer networks. The rest of the document covers important topics in distributed DBMS including distributed database design, query processing, transaction management, and data replication. Normal forms like 1NF, 2NF, 3NF and BCNF are also discussed as ways to reduce data anomalies in distributed databases. Relational algebra operators such as selection, projection, join and union are also covered.
This document discusses data stream management systems (DSMS). It begins by describing the inputs and outputs of a DSMS, which include continuous streams of data from sources like sensors. It then contrasts DSMS with traditional database management systems, noting that DSMS handle persistent queries over transient data streams. The document outlines several challenges of DSMS, such as their push-based computation model and need for non-blocking operators. It also discusses implementation choices, system architectures, stream data models, query languages, and query processing techniques for DSMS.
The document discusses the basics of ontologies, including their origin in philosophy, definitions, types, benefits and application areas. Some key points are:
- An ontology is a formal specification of a conceptualization used to help humans and programs share knowledge. It establishes a shared vocabulary for exchanging information.
- Ontologies describe domain knowledge and provide an agreed-upon understanding of a domain through concepts and relations. They help solve problems of ambiguity and enable knowledge sharing.
- Ontologies benefit applications like information retrieval, digital libraries, knowledge engineering and natural language processing by facilitating semantic search and integration of data.
In this paper we present the SMalL Ontology for malicious software classification, SMalL Java Application for antivirus systems comparison and the SMalL knowledge based file format for malware related attacks. We believe that our ontology is able to aid the development of malware prevention software by offering a common knowledge base and a clear classification of the existing malicious software. The application is a prototype regarding how this ontology might be used in conjunction with known antivirus capabilities to offer a comprehensive comparison.
This document surveys ontology visualization methods. It begins by defining ontologies as sets of concepts and relationships in a domain that have proven useful for digital libraries, the semantic web, and personalized information management. However, effectively visualizing ontologies is challenging due to the complex relationships and attributes involved. The document aims to categorize existing ontology visualization techniques and their characteristics in order to help with method selection and further research. It provides context on related work reviewing data visualization techniques before analyzing ontology visualization methods in detail.
The repository ecology: an approach to understanding repository and service i...R. John Robertson
An increasing number of university institutions and other organisations are deciding to deploy repositories and a growing number of formal and informal distributed services are supporting or capitalising on the information these repositories provide. Despite reasonably well understood technical architectures, early majority adopters may struggle to articulate their place within the actualities of a wider information environment. The idea of a repository ecology provides developers and administrators with a useful way of articulating and analysing their place in the information environment, and the technical and organisational interactions they have, or are developing, with other parts of such an environment. This presentation will provide an overview of the concept of a repository ecology and examine some examples from the domains of scholarly communications and elearning.
The document provides an overview of ontology and its various aspects. It discusses the origin of the term ontology, which derives from Greek words meaning "being" and "science," so ontology is the study of being. It distinguishes between scientific and philosophical ontologies. Social ontology examines social entities. Perspectives on ontology include philosophy, library and information science, artificial intelligence, linguistics, and the semantic web. The goal of ontology is to encode knowledge to make it understandable to both people and machines. It provides motivations for developing ontologies such as enabling information integration and knowledge management. The document also discusses ontology languages, uniqueness of ontologies, purposes of ontologies, and provides references.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
This document discusses an integrated approach to ontology development methodology and provides a case study using a shopping mall domain. It begins by reviewing existing ontology development methodologies and identifying their pitfalls. An integrated methodology is then proposed which aims to reduce these pitfalls. The key steps in the proposed methodology are: 1) capturing motivating user scenarios or keywords, 2) generating formal/informal questions and answers from the scenarios, 3) extracting terms and constraints, and 4) building the ontology using a top-down approach. The methodology is applied to developing an ontology for a shopping mall domain to provide multilingual information to visitors.
An Engineering-to-Biology Thesaurus for Engineering Design.pdfNaomi Hansen
This document presents an engineering-to-biology thesaurus that aims to help engineers leverage biological information during the design process by providing synonymous biological terms mapped to engineering function and flow terminology. The thesaurus integrates terms from research at Oregon State University, the Indian Institute of Science, and the University of Toronto. Biological terms in the thesaurus correspond to terms in the Functional Basis lexicon, an established set of engineering function and flow terms. The thesaurus is intended to ease the use of biological knowledge for engineers without extensive biological backgrounds. An example application of comprehension and functional modeling using the thesaurus is also presented.
SWSN UNIT-3.pptx we can information about swsn professionalgowthamnaidu0986
Ontology engineering involves constructing ontologies through various methods. It begins with defining the scope and evaluating existing ontologies for reuse. Terms are enumerated and organized in a taxonomy with defined properties, facets, and instances. The ontology is checked for anomalies and refined iteratively. Popular tools for ontology development include Protege and WebOnto. Methods like Meth ontology and On-To-Knowledge methodology provide processes for building ontologies from scratch or reusing existing ones. Ontology sharing requires mapping between ontologies to allow interoperability, and libraries exist for storing and accessing ontologies.
The document discusses moving from traditional service-oriented architectures to nature-inspired self-aware pervasive service ecosystems. It outlines limitations of SOA and requirements of new systems, including spatial awareness, adaptivity, and decentralization. Various natural metaphors are considered as inspiration, including physical, chemical, biological, and social systems. No single metaphor meets all requirements. The SAPERE project aims to develop a new synthesis from existing metaphors through a reference architecture for nature-inspired pervasive service ecosystems.
Abstract:
A growing number of resources are available for enriching documents with semantic annotations. While originally focused on a few standard classes of annotations, the ecosystem of annotators is now becoming increasingly diverse. Although annotators often have very different vocabularies, with both high-level and specialist
concepts, they also have many semantic interconnections. We will show that both the overlap and the diversity in annotator vocabularies motivate the need for semantic annotation integration: middleware that produces a unified annotation on top of diverse semantic annotators. On the one hand, the diversity of vocabulary allows applications
to benefit from the much richer vocabulary available in
an integrated vocabulary. On the other hand, we present evidence that the most widely-used annotators on the web suffer from serious accuracy deficiencies: the overlap in vocabularies from individual annotators allows an integrated annotator to boost accuracy by exploiting inter-annotator agreement and disagreement.
The integration of semantic annotations leads to new challenges, both compared to usual data integration scenarios and to standard aggregation of machine learning tools. We overview an approach to these challenges that performs ontology-aware aggregation. We
introduce an approach that requires no training data, making use of ideas from database repair. We experimentally compare this with a supervised approach, which adapts maximal entropy Markov models to the setting of ontology-based annotations. We further experimentally compare both these approaches with respect to ontology-unaware
supervised approaches, and to individual annotators.
Swoogle: Showcasing the Significance of Semantic SearchIDES Editor
The World Wide Web hosts vast repositories of
information. The retrieval of required information from the
Internet is a great challenge since computer applications
understand only the structure and layout of web pages and
they do not have access to their intended meaning. Semantic
web is an effort to enhance the Internet, so that computers
can process the information presented on WWW, interpret
and communicate with it, to help humans find required
essential knowledge. Application of Ontology is the
predominant approach helping the evolution of the Semantic
web. The aim of our work is to illustrate how Swoogle, a
semantic search engine, helps make computer and WWW
interoperable and more intelligent. In this paper, we discuss
issues related to traditional and semantic web searching. We
outline how an understanding of the semantics of the search
terms can be used to provide better results. The experimental
results establish that semantic search provides more focused
results than the traditional search.
A Comparative Study Ontology Building Tools for Semantic Web Applications IJwest
This document provides a comparative study of four popular ontology building tools: Protégé 3.4, IsaViz, Apollo, and SWOOP. It discusses the features and functionalities of each tool, including their capabilities for ontology editing, browsing, documentation, import/export of formats, and visualization. The document aims to identify existing ontology tools that are freely available and can be used to develop ontologies for various application domains such as transport, tourism, health, and natural language. It evaluates the tools based on criteria like interoperability, openness, ease of updating/maintaining ontologies, and market penetration.
A Comparative Study Ontology Building Tools for Semantic Web Applications dannyijwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing, especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d) market status and penetration. The results of the review in ontologies are analyzed for each application area, such as transport, tourism, personal services, health and social services, natural languages and other HCI-related domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks. Although each tool provides different functionalities, most of the users just use only one, because they are not able to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different ontologies with different development and management tools. The paper is also concerns the detection of commonalities and differences between the examined ontologies, both on the same domain (application area) and among different domains.
A Comparative Study of Ontology building Tools in Semantic Web Applications dannyijwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing,
especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms
and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all
possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely
available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d)
market status and penetration. The results of the review in ontologies are analyzed for each application area, such
as transport, tourism, personal services, health and social services, natural languages and other HCI-related
domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks.
Although each tool provides different functionalities, most of the users just use only one, because they are not able
to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different
ontologies with different development and management tools. The paper is also concerns the detection of
commonalities and differences between the examined ontologies, both on the same domain (application area) and
among different domains.
Visualizing Consensus with Online Ontologies to Support Quality in Ontology D...Mathieu d'Aquin
Presentation at the workshop on ontology quality at EKAW 2010, on using measures of agreement, disagreement, consensus and controversy to support ontology assessment in ontology engineering.
The document describes Earthster Core Ontology (ECO), a domain ontology for Life Cycle Assessment (LCA). ECO aims to provide a vocabulary for core LCA concepts to publish LCA data on the web in a semantically interoperable way. It defines concepts like Process, Quantified Effect, and Elementary Flow. ECO is still under development with feedback from the LCA community. Its goals are to extend existing LCA data structures, link data sources, and allow for flexible extension over time as the field evolves.
This document summarizes an academic paper that describes an ontology for representing web services using the Web Services Description Language (WSDL) and the Resource Description Framework (RDF). The paper discusses how ontologies provide a set of rules for describing domains and supporting reasoning. It then provides background on WSDL for describing web services and RDF for representing ontologies using graphs. The paper proposes using WSDL and RDF together to describe ontologies for web services.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document summarizes several agent-based modeling projects done by students at the University of East London. It describes projects using StarLogo, where students modeled emergent urban forms and traffic patterns. It also discusses modeling the growth of traditional Yemeni cities and experiments deforming NURBS surfaces using agent-based modeling in Microstation. The document provides examples of how agent-based modeling can be used as a design tool to explore emergent patterns and behaviors.
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2. Adriel Café <aac3@cin.ufpe.br>
ONTOLOGY INTEGRATION
The concept of “integration” means anything ranging from
integration, merges, use, mapping, extending, approximatio
n, unified views and more. [Keet]
3. Adriel Café <aac3@cin.ufpe.br>
ONTOLOGY INTEGRATION
Mapping
“Given two ontologies, how do we find similarities between
them, determine which concepts and properties represent similar
notions, and so on.” [Noy]
Matching & Alignment
“Ontology matching is the process of finding the relations between
ontologies, and we call alignment the result of this process expressing
declaratively these relations.” [Euzenat, Mocan]
Merging
“The process of ontology merging takes as input two (or more) source
ontologies and returns a merged ontology based on the given source
ontologies.” [Stumme, Maedche]
6. Adriel Café <aac3@cin.ufpe.br>
FEATURES OF ONTOLOGY
Establishes a formal vocabulary to share information
between applications
Inference is one of the main characteristics of ontologies
Top-level Ontology
Describes very general concepts that are present in several
areas, e.g., SUMO (Suggested Upper Merged Ontology), DOLCE
(Descriptive Ontology for Linguistic and Cognitive
Engineering), BFO (Basic Formal Ontology)
Domain Ontology
Ontologies specialize in a subset of generic ontologies in a
domain or subdomain, e.g., Gene Ontology, Protein
Ontology, Health Indicator Ontology, Environment Ontology
7. Adriel Café <aac3@cin.ufpe.br>
NON-DISRUPTIVE INTEGRATION AND (RE)USE OF
ONTOLOGIES – GLOBAL AS VIEW (GAV)
[Calvanese et al.] considers mapping between one global and
several local ontologies leaving the local ontologies intact by
querying the local ontologies and converting the query result
into a concept in the global ontology.
Single Ontology approaches use one global ontology providing a
shared vocabulary for the specification of the semantics [Wache
et al.]
8. Adriel Café <aac3@cin.ufpe.br>
NON-DISRUPTIVE INTEGRATION AND (RE)USE OF
ONTOLOGIES – LOCAL AS VIEW (LAV)
In LaV, each information source is described by its own
ontology. Each source ontology can be developed without
respect to other sources or their ontologies. [Wache et al.]
This ontology architecture can simplify the integration task and
supports the change, i.e. the adding and removing of sources.
[Wache et al.]
On the other hand, the lack
of a common vocabulary
makes it difficult to
compare different source
ontologies. [Wache et al.]
9. Adriel Café <aac3@cin.ufpe.br>
NON-DISRUPTIVE INTEGRATION AND (RE)USE OF
ONTOLOGIES – HYBRID APPROACH
Similar to LaV the semantics of each source is described by its
own ontology. But in order to make the local ontologies
comparable to each other they are built from a global shared
vocabulary. [Wache et al.]
The advantage of a hybrid approach is that new sources can
easily be added without the need of modification. It also
supports the acquisition and evolution of ontologies. [Wache et
al.]
But the drawback of hybrid approaches
is that existing ontologies can not easily
be reused, but have to be redeveloped
from scratch. [Wache et al.]
10. Adriel Café <aac3@cin.ufpe.br>
THE PROBLEM OF HETEROGENEITY
Even with all these advantages, we still need to map the
sources
Top-level Ontologies and
Domain Ontologies can
drastically decrease the
complexity
11. Adriel Café <aac3@cin.ufpe.br>
DIFFERENCES BETWEEN ONTOLOGIES [Goh, 1996]
1 Schematic
Data type, the most obvious one being numbers as integers or as
strings.
Labelling, only the strings of the name of the concept differ but
not the definition. This also includes labelling of attributes and
their values.
Aggregation, e.g. organizing organisms by test site or by species
in biodiversity
Generalization, e.g. one system may have separate
representations for managers and engineers, whereas another
may model all of the information collectively in an employee
entity type
12. Adriel Café <aac3@cin.ufpe.br>
DIFFERENCES BETWEEN ONTOLOGIES [Goh, 1996]
2 Semantic
Naming, includes problems with synonyms (e.g. maize and corn).
Scaling and units, on scaling: one system with possible values
white, pink, red and the other uses the full range of RGB; units:
metric and imperial system.
Confounding, a concept that is the same, but in reality different;
primarily has an effect on the attribute values, like
latestMeasuredTemperature, that does not refer to one and the
same over time.
13. Adriel Café <aac3@cin.ufpe.br>
DIFFERENCES BETWEEN ONTOLOGIES [Goh, 1996]
3 Intensional
Domain, refer to discrepancies in the universe of discourse, e.g.
two sources may provide financial information on
companies, but the first reports “all US Fortune 500 companies
in the manufacturing sector”, whereas the second may report
information for “all companies listed on US stock exchanges with
total assets above one billion US Dollars”
Integrity constraint, the identifier in one model may not suffice
for another, for example one animal taxonomic model uses an
[automatically generated and assigned] ID number to identify
each instance, whereas another system assumes each animal has
a distinct name.
14. Adriel Café <aac3@cin.ufpe.br>
DIFFERENCES BETWEEN ONTOLOGIES [Klein, 2001]
Language Level
Languages can differ in their syntax
Constructs available in one language are not available in
another, e.g., disjoint, negation
OWL Full, OWL DL, OWL Lite, OWL 2 EL, OWL 2 QL, OWL 2 RL
Ontology Level
Using the same terms to describe different concepts
Use different terms to describe the same concept, e.g., Maize
and Corn
Using different modeling paradigms
Use different levels of granularity
16. Adriel Café <aac3@cin.ufpe.br>
DIFFERENCES BETWEEN ONTOLOGIES
Example [Noy]
Two ontologies that describe people, projects, publications…
Portal Ontology
http://www.aktors.org/ontology/portal
Person Ontology
http://ebiquity.umbc.edu/ontology/person.owl
17. Adriel Café <aac3@cin.ufpe.br>
DIFFERENCES BETWEEN ONTOLOGIES
Portal Ontology
Person Ontology
Different names for the same
concept
PhD-Student
PhDStudent
Same term for different
concepts
Project
Only the current project
Project
Past projects and proposals
Scope
Includes journals and
publications composed
Includes students and guest
speakers
Different modeling
conventions
Journal is a class
journal is a property
Granularity
Professor-In-Academia
adjunct, affiliated,
associate, principal
[Noy]
18. Adriel Café <aac3@cin.ufpe.br>
DISCOVERING MAPPINGS
Using Top-level Ontologies
They are designed to support the integration of information
Examples: SUMO, DOLCE
Using the ontology structure
Metrics to compare concepts
Explore the semantic relations in the
ontology, e.g., SubClassOf, PartOf, class properties, range of
properties
Examples : Similarity Flooding, IF-Map, QOM, Chimaera, Prompt
19. Adriel Café <aac3@cin.ufpe.br>
DISCOVERING MAPPINGS
Using lexical information
String normalization
String distance
Soundex
Phonetic algorithm, e.g., Kennedy, Kennidi, Kenidy, Kenney...
Thesaurus
Dictionary with words grouped by similarity
Through user intervention
Providing information at the beginning of the mapping
Providing feedback on the maps generated
20. Adriel Café <aac3@cin.ufpe.br>
DISCOVERING MAPPINGS
Methods based on rules
Methods based on graphs
Structural and lexical analysis
These ontologies as graphs and compares the corresponding
subgraphs
Machine learning approaches
Probabilistic approaches
Reasoning and theorem proving
23. WE HAVE THE MAPPING.
NOW WHAT?
Ontology Merging
Query answering
Examples: OntoMerge
Peer-to-Peer (P2P) Architecture
Ontology Integration System (OIS)
Reasoning with mappings
Examples: Pellet, HermiT, FaCT++
Adriel Café <aac3@cin.ufpe.br>
24. Adriel Café <aac3@cin.ufpe.br>
CONCLUSION
Ontologies have a great power of expression
Semantic integration is a major challenge of the Semantic
Web
Questions to be answered
Imperfect and inconsistent mappings are useful?
How to maintain mappings when ontologies evolve?
How do we evaluate and compare different tools?
25. Adriel Café <aac3@cin.ufpe.br>
REFERENCES
D. Calvanese, “A framework for ontology integration” Emerg. Semant. …, 2002.
D. Calvanese, G. De Giacomo, and M. Lenzerini, “Ontology of Integration and Integration of
Ontologies” Descr. Logics, 2001.
A. Doan and J. Madhavan, “Learning to map between ontologies on the semantic web” …
World Wide Web, pp. 662–673, 2002.
M. Ehrig, S. Staab, and Y. Sure, “Framework for Ontology Alignment and Mapping” pp. 1–
34, 2005.
J. Euzenat and P. Valtchev, “Similarity-based ontology alignment in OWL-Lite1” … Artif. Intell.
August 22-27, …, 2004.
N. Noy, “Ontology Mapping and Alignment” Fifth Int. Work. Ontol. Matching …, 2012.
N. Noy, “Semantic integration: a survey of ontology-based approaches” ACM Sigmod Rec., vol.
33, no. 4, pp. 65–70, 2004.
P. Shvaiko and J. Euzenat, “Ontology matching: state of the art and future challenges” vol.
X, no. X, pp. 1–20, 2012.
M. Uschold, “Ontologies and Semantics for Seamless Connectivity” vol. 33, no. 4, pp. 58–
64, 2004.
M. Keet, “Aspects of Ontology Integration”, 2004.