ontology based- data_integration.

•Download as PPTX, PDF•

1 like•572 views

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.

Technology

Knowledge Engineering Course (SCOM7348)
University of Birzeit, Palestine
December, 2012

Synthesis Paper Talk

Ontology-Based
Data Integration
Ali Ahmad Al Jadaa
Master of Computing, Birzeit University
Ali.ps@live.com

This is a student talk, at the Knowledge Engineering course, each student is is
asked to present his/her synthesis paper.
Course Page: http://jarrar-courses.blogspot.com/2011/09/knowledgeengineering-fall2011.html

1

Content

• Abstract
• What is data integration
• What is Semantic
• What is Ontology
• Heterogeneity conflicts
• References

2

Abstract

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.

3

What is data integration

• Data coming from different sources
and providing users with a unified view
of these data .

4

What is Semantic

• Understand the meaning .
• Relation between signifiers .
• Difference with other

≠

5

What is Ontology

• Set of concepts within a domain, and the
relationships between pairs of concepts. It
can be used to model a domain and support
reasoning about entities .
• Dictionary Computers can be understand
• List of things that exist
• Description of the kinds of entities there are
and how they are related

6

Quick Review

• Now we know About semantic and
ontology and data integration .
• We see if we make an Ontology of data
integration ,so we will Collide of
different heterogeneity of data source .
• So we must know about Heterogeneity
conflicts

7

Heterogeneity conflicts

• Syntactic heterogeneity : differences between
data models (entity-relationship, relational,
object oriented) .

• Structural heterogeneity (schematic
heterogeneity) : different information systems
store their data in different structures.

• Semantic heterogeneity :the meaning of the
information that is interchanged must be
understood across the systems

8

References

• Ontology-Based Integration of Data Sources Michel Gagnon Defence
R&D Canada – Valcartier Department National Defence Québec,
Canada Michel.Gagnon@drdc-rddc.g

• Ontology-Based Data Integration Methods: A Framework for
Comparison Agustina Buccella*, Alejandra Cechich* and Nieves R.
Brisaboa
• Wikipedia, the free encyclopedia
en.wikipedia.org/wiki/Data_integration

9

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.

Hyponymy extraction of domain ontology

IJwest

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.

Ontology integration - Heterogeneity, Techniques and more

Adriel Café

A category theoretic model of rdf ontology

IJwest

Ontology languages are used in modelling the semantics of concepts within a particular domain and the relationships between those concepts. The Semantic Web standard provides a number of modelling languages that differ in their level of expressivity and are organized in a Semantic Web Stack in such a way that each language level builds on the expressivity of the other. There are several problems when one attempts to use independently developed ontologies. When existing ontologies are adapted for new purposes it requires that certain operations are performed on them. These operations are currently performed in a semi-automated manner. This paper seeks to model categorically the syntax and semantics of RDF ontology as a step towards the formalization of ontological operations using category theory.

Data Integration Ontology Mapping

Pradeep B Pillai

Automatically converting tabular data to

IJwest

Information residing in relational databases and delimited file systems are inadequate for reuse and sharing over the web. These file systems do not adhere to commonly set principles for maintaining data harmony. Due to these reasons, the resources have been suffering from lack of uniformity, heterogeneity as well as redundancy throughout the web. Ontologies have been widely used for solving such type of problems, as they help in extracting knowledge out of any information system. In this article, we focus on extracting concepts and their relations from a set of CSV files. These files are served as individual concepts and grouped into a particular domain, called the domain ontology. Furthermore, this domain ontology is used for capturing CSV data and represented in RDF format retaining links among files or concepts. Datatype and object properties are automatically detected from header fields. This reduces the task of user involvement in generating mapping files. The detail analysis has been performed on Baseball tabular data and the result shows a rich set of semantic information.

The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.

Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...

Khirulnizam Abd Rahman

Ontology Mapping

samhati27

Ekaw ontology learning for cost effective large-scale semantic annotationShahab Mokarizadeh

ONTOLOGY BASED DATA ACCESS

Kishan Patel

The Statement of Conjunctive and Disjunctive Queries in Object Oriented Datab...

Editor IJCATR

Entrance of object orienting concept in database caused the relation database gradually to replace with object oriented database in various fields. On the other hand for solving the problem of real world uncertain data, several methods were presented. One of these methods for modeling database is an approach wich couples object-oriented database modeling with fuzzy logic. Many queries that users to pose are expressed on the basis of linguistic variables. Because of classical databases are not able to support these variables, leads to fuzzy approaches are considered. We investigate databases queries in this study both simple and complex ways. In the complex way, we use conjunctive and disjunctive queries. In the following, we use the XML labels to express inqueries into fuzzy. We can also communicate with other sections of software by entering into XML world as the most reliable opportunity. Also we want to correct conjunctive and disjunctive queries related to fuzzy object oriented database using the concept of dependency measure and weight, and weight be assigned to different phrases of a query based on user emphasis. The other aim of this research is mapping fuzzy queries to fuzzy-XML. It is expected to be simple implement of query, and output of execution of queries be greatly closer to users' needs and fulfill her expect. The results show that the proposed method explains the possible conjunctive and disjunctive queries the database in the form of Fuzzy-XML.

Tools for Ontology Building from Texts: Analysis and Improvement of the Resul...

IOSR Journals

Artificial Intelligence of the Web through Domain Ontologies

International Journal of Science and Research (IJSR)

The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.

mlssMaiAGE-INRA, Paris Sud, LIMSI-CNRS

Improve information retrieval and e learning using

IJwest

The Web-based education and E-Learning has become a very important branch of new educational technology. E-learning and Web-based courses offer advantages for learners by making access to resources and learning objects very fast, just-in-time and relevance, at any time or place. Web based Learning Management Systems should focus on how to satisfy the e-learners needs and it may advise a learner with most suitable resources and learning objects. But Because of many limitations using web 2.0 for creating E-learning management system, now-a-days we use Web 3.0 which is known as Semantic web. It is a platform to represent E-learning management system that recovers the limitations of Web 2.0.In this paper we present “improve information retrieval and e-learning using mobile agent based on semantic web technology”. This paper focuses on design and implementation of knowledge-based industrial reusable, interactive, web-based training activities at the sea ports and logistics sector and use e-learning system and semantic web to deliver the learning objects to learners in an interactive, adaptive and flexible manner. We use semantic web and mobile agent to improve Library and courses Search. The architecture presented in this paper is considered an adaptation model that converts from syntactic search to semantic search. We apply the training at Damietta port in Egypt as a real-world case study. we present one of possible applications of mobile agent technology based on semantic web to management of Web Services, this model improve the information retrieval and E-learning system.

Are Data Models Superfluous Nov2003

Andries_vanRenssen

Ontologies for big data

Yu Lin

ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEW

ijait

Protégé is one of the most popular tools of the ontology visualization. The “Protégé” tools are being applied for further development in various disciplines for better understanding of knowledge. These tools commonly use four methods of ontology visualization, namely, indented list, node-link and tree, zoomable, and focus+context. The purpose of this work is to present a study on application of these four methods in the development of different kinds of protégé visualization tools and categorize their characteristics and features so that it assists in method selection and promotes further future research in the area of ontology visualization.

Ontology Mappingbutest

ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...

IJNSA Journal

In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing one single patient’s information gathered from a large volume of data over a long period of time. The main objective of this paper is to represent our ontology-based information retrieval approach for clinical Information System. We have performed a Case Study in the real life hospital settings. The results obtained illustrate the feasibility of the proposed approach which significantly improved the information retrieval process on a large volume of data over a long period of time from August 2011 until January 2012

Ontology learning techniques and applications computer science thesis writing...

Tutors India

Learning ontologies

Alexander De Leon

Swoogle: Showcasing the Significance of Semantic Search

IDES 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.

Ontology Building and its Application using Hozo

Kouji Kozaki

TRANSFORMATION RULES FOR BUILDING OWL ONTOLOGIES FROM RELATIONAL DATABASES

csandit

Relational Databases (RDB) are used as the backend database by most of information systems. RDB encapsulate conceptual model and metadata needed in the ontology construction. Schema mapping is a technique that is used by all existing approaches for ontology building from RDB. However, most of those methods use poor transformation rules that prevent advanced database mining for building rich ontologies. In this paper, we propose transformation rules for building owl ontologies from RDBs. It allows transforming all possible cases in RDBs into ontological constructs. The proposed rules are enriched by analyzing stored data to detect disjointness and totalness constraints in hierarchies, and calculating the participation level of tables in n-ary relations. In addition, our technique is generic; hence it can be applied to any RDB. The proposed rules were evaluated using a normalized and open RDB. The obtained ontology is richer in terms of non- taxonomic relationships.

X Som Graduation PresentationGiorgio Orsi

Horizontal Integration of Big Intelligence DataDataTactics

What's hot

Using linguistic analysis to translate

IJwest

Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...

Khirulnizam Abd Rahman

Ontology Mapping

samhati27

Ekaw ontology learning for cost effective large-scale semantic annotationShahab Mokarizadeh

ONTOLOGY BASED DATA ACCESS

Kishan Patel

The Statement of Conjunctive and Disjunctive Queries in Object Oriented Datab...

Editor IJCATR

Tools for Ontology Building from Texts: Analysis and Improvement of the Resul...

IOSR Journals

Artificial Intelligence of the Web through Domain Ontologies

International Journal of Science and Research (IJSR)

mlssMaiAGE-INRA, Paris Sud, LIMSI-CNRS

Improve information retrieval and e learning using

IJwest

Are Data Models Superfluous Nov2003

Andries_vanRenssen

Ontologies for big data

Yu Lin

ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEW

ijait

Ontology Mappingbutest

ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...

IJNSA Journal

Ontology learning techniques and applications computer science thesis writing...

Tutors India

Learning ontologies

Alexander De Leon

Swoogle: Showcasing the Significance of Semantic Search

IDES Editor

Ontology Building and its Application using Hozo

Kouji Kozaki

TRANSFORMATION RULES FOR BUILDING OWL ONTOLOGIES FROM RELATIONAL DATABASES

csandit

What's hot (20)

Using linguistic analysis to translate

Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azm...

Ontology Mapping

Ekaw ontology learning for cost effective large-scale semantic annotation

ONTOLOGY BASED DATA ACCESS

The Statement of Conjunctive and Disjunctive Queries in Object Oriented Datab...

Tools for Ontology Building from Texts: Analysis and Improvement of the Resul...

Artificial Intelligence of the Web through Domain Ontologies

mlss

Improve information retrieval and e learning using

Are Data Models Superfluous Nov2003

Ontologies for big data

ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEW

Ontology Mapping

ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...

Ontology learning techniques and applications computer science thesis writing...

Learning ontologies

Swoogle: Showcasing the Significance of Semantic Search

Ontology Building and its Application using Hozo

TRANSFORMATION RULES FOR BUILDING OWL ONTOLOGIES FROM RELATIONAL DATABASES

Viewers also liked

X Som Graduation PresentationGiorgio Orsi

Horizontal Integration of Big Intelligence DataDataTactics

8 ontology integration and interoperability (onto i op)

AEGIS-ACCESSIBLE Projects

Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...hamidnazary2002

Does That Belong Here? A Facebook Versus LinkedIn Infographic

Boston Interactive

Venture capital investment trends May 2014

JLL

Startup Turkey 2014 VitalCV Pitch Slides

Joshgun Karimov

Dove- Evolution of a brand

Sameer Mathur

TH TRUE HEAL - Brand Love KeyLong Nguyen

What 1 Million Tweets Taught Us About Tweeting Successfully

Buffer

The Death of Trial and Error, SMX London 2013 by Sean Malseed of SEMrush.com

Semrush

This presentation describes how to remove the element of trial and error from your PPC keyword research. Everyone always talks about testing and optimizing, but where do you start? You need to have something to test, and something to optimize. This presentation shows you how to get that something, without resorting to trial and error. It's possible to know the results before you take the action, using a few tools and a certain way of looking at things.

10 Inconvenient Truths About Creating Branded Content with Influencers

Erica Ehm

Above the Fold: Web Typography for Print Designers

Brian Miller

Management GurusMarcus9000

How to write a creative brief

Michael Fomichev

Презентация о том, как правильно писать креативный бриф и зачем это нужно. Написана специально для выступления перед бренд менеджерами компании Unilever. Создана на основе аналогичной презентации Мити Воскресенского http://duckofdoom.ru/

Viewers also liked (15)

X Som Graduation Presentation

Horizontal Integration of Big Intelligence Data

8 ontology integration and interoperability (onto i op)

Enterprise and Data Mining Ontology Integration to Extract Actionable Knowled...

Does That Belong Here? A Facebook Versus LinkedIn Infographic

Venture capital investment trends May 2014

Startup Turkey 2014 VitalCV Pitch Slides

Dove- Evolution of a brand

TH TRUE HEAL - Brand Love Key

What 1 Million Tweets Taught Us About Tweeting Successfully

The Death of Trial and Error, SMX London 2013 by Sean Malseed of SEMrush.com

10 Inconvenient Truths About Creating Branded Content with Influencers

Above the Fold: Web Typography for Print Designers

Management Gurus

How to write a creative brief

Similar to ontology based- data_integration.

ontology based- data_integration.ali_aljadaa.1125048

AliAlJadaa

ontology meets big data:immutabilityChris Partridge

Mining knowledge graphs to map heterogeneous relations between the internet o...

IJECEIAES

Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to objectoriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.

ICDMWorkshopProposal.docbutest

Concept integration using edit distance and n gram match

ijdms

Information is growing more rapidly on the World Wide Web (WWW) has made it necessary to make all this information not only available to people but also to the machines. Ontology and token are widely being used to add the semantics in data processing or information processing. A concept formally refers to the meaning of the specification which is encoded in a logic-based language, explicit means concepts, properties that specification is machine readable and also a conceptualization model how people think about things of a particular subject area. In modern scenario more ontologies has been developed on various different topics, results in an increased heterogeneity of entities among the ontologies. The concept integration becomes vital over last decade and a tool to minimize heterogeneity and empower the data processing. There are various techniques to integrate the concepts from different input sources, based on the semantic or syntactic match values. In this paper, an approach is proposed to integrate concept (Ontologies or Tokens) using edit distance or n-gram match values between pair of concept and concept frequency is used to dominate the integration process. The proposed techniques performance is compared with semantic similarity based integration techniques on quality parameters like Recall, Precision, FMeasure & integration efficiency over the different size of concepts. The analysis indicates that edit distance value based interaction outperformed n-gram integration and semantic similarity techniques.

M045067275

IJERA Editor

International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.

Sci Know Mine 2013: What can we learn from topic modeling on 350M academic do...

William Gunn

Towards Ontology Development Based on Relational Database

ijbuiiir1

Ontology is defined as the formal explicit specification of a shared conceptualization. It has been widely used in almost all fields especially artificial intelligence, data mining, and semantic web etc. It is constructed using various set of resources. Now it has become a very important task to improve the efficiency of ontology construction. In order to improve the efficiency, need an automated method of building ontology from database resource. Since manual construction is found to be erroneous and not up to the expectation, automatic construction of ontology from database is innovated. Then the construction rules for ontology building from relational data sources are put forward. Finally, ontology for �automated building of ontology from relational data sources� has been implemented

IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline

119 128Editor IJARCET

seminarphasdfjgdhhgbjdfghbdjfgbdjfgbdfgdfgdfge2.ppt

LakshmishaRALakshmis

Research Inventy : International Journal of Engineering and Science

researchinventy

Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.

Knowledge Management in the AI Driven Scintific System

Subhasis Dasgupta

In this dynamic talk, we'll explore the transformative role of AI in scientific knowledge management. We'll delve into how AI revolutionizes data organization, analysis, and hypothesis testing, enhancing efficiency and discovery. Highlighting the seamless integration with existing research processes, we'll address the training and ethical considerations of AI adoption. Through real-world examples, we'll demonstrate AI's impact on scientific breakthroughs, emphasizing the shift towards more collaborative and innovative research landscapes. This presentation aims to inspire the scientific community to embrace AI, leveraging its potential to redefine the boundaries of knowledge and innovation.

USING ONTOLOGIES TO OVERCOMING DRAWBACKS OF DATABASES AND VICE VERSA: A SURVEY

cseij

For a same domain, several databases (DBs) exist. The emergence of classical web to the semantic web has contributed to the appearance of the notion of ontology that have shared and consensual vocabulary. For a given, it is more interesting to take advantage of existing databases, to build an ontology. Most of the data are already stored in these databases. So many DBs can be integrated to enable reuse of existing data for the semantic web. Even for existing ontologies, the relevance of the information they contain requires regular updating. These databases can be useful sources to enrich these ontologies. In the other hand, for these ontologies more than the ratio ‘size of the instances on the size of working memory’ is large more than the management of these instances, in memory, is difficult. Finding a way to store these instances in a structured manner to satisfy the needs of performance and reliability required for many applications becomes an obligation. As a consequence, defining query languages to support these structures becomes a challenge for SW community. We will show through this paper how ontologies can benefit from DBs to increase system performance and facilitate their design cycle. The DBs in their turn suffers from several drawbacks namely complexity of the design cycle and lack of semantics. Since ontologies are rich in semantic, DBs can profit from this advantage to overcoming their drawbacks.

Representation of ontology by Classified Interrelated object modelMihika Shah

2012.10 - Workshop on Semantic Statistics - 1Dr.-Ing. Thomas Hartmann

Tragedy of the (Data) Commons

James Hendler

Scientific Knowledge Graphs: an Overview

Angelo Salatino

In the last decade, several Scientific Knowledge Graphs (SKG) were released, representing scientific knowledge in a structured, interlinked, and semantically rich manner. But, what kind of information they describe? How they have been built? What can we do with them? In this lecture, I will first provide an overview of well-known SKGs, like Microsoft Academic Graph, Dimensions, and others. Then, I will present the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21M publications and 8M patents according to i) the research topics drawn from the Computer Science Ontology, ii) the type of the author's affiliations (e.g, academia, industry), and iii) 66 industrial sectors (e.g., automotive, financial, energy, electronics) from the Industrial Sectors Ontology (INDUSO). Finally, I will showcase a number of tools and approaches using such SKGs, supporting researchers, companies, and policymakers in making sense of research dynamics.

Tragedy of the Data Commons (ODSC-East, 2021)

James Hendler

Similar to ontology based- data_integration. (20)

ontology based- data_integration.ali_aljadaa.1125048

ontology meets big data:immutability

Mining knowledge graphs to map heterogeneous relations between the internet o...

ICDMWorkshopProposal.doc

Concept integration using edit distance and n gram match

M045067275

Sci Know Mine 2013: What can we learn from topic modeling on 350M academic do...

Towards Ontology Development Based on Relational Database

IJCER (www.ijceronline.com) International Journal of computational Engineerin...

119 128

seminarphasdfjgdhhgbjdfghbdjfgbdjfgbdfgdfgdfge2.ppt

Research Inventy : International Journal of Engineering and Science

Knowledge Management in the AI Driven Scintific System

USING ONTOLOGIES TO OVERCOMING DRAWBACKS OF DATABASES AND VICE VERSA: A SURVEY

Representation of ontology by Classified Interrelated object model

2012.10 - Workshop on Semantic Statistics - 1

Tragedy of the (Data) Commons

Scientific Knowledge Graphs: an Overview

Tragedy of the Data Commons (ODSC-East, 2021)

Recently uploaded

PCI PIN Basics Webinar from the Controlcase Team

ControlCase

UiPath Community Day Dubai: AI at Work..

UiPathCommunity

Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking. 📕 Curious on our agenda? Wait no more! 10:00 Welcome note - UiPath Community in Dubai Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank 10:20 A UiPath cross-region MEA overview Ashraf El Zarka, VP and Managing Director MEA, UiPath 10:35: Customer Success Journey Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank 11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more Boris Krumrey, Global VP, Automation Innovation, UiPath 12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services. Brendan Lingam, Director of Sales and Business Development, Marc Ellis

Essentials of Automations: The Art of Triggers and Actions in FME

Safe Software

In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation. We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios. Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!

Monitoring Java Application Security with JDK Tools and JFR Events

Ana-Maria Mihalceanu

SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdf

Peter Spielvogel

Building better applications for business users with SAP Fiori. • What is SAP Fiori and why it matters to you • How a better user experience drives measurable business benefits • How to get started with SAP Fiori today • How SAP Fiori elements accelerates application development • How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities • How SAP Fiori paves the way for using AI in SAP apps

zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs

Alex Pruden

This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second). Paper: https://eprint.iacr.org/2023/1886

FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf

FIDO Alliance

RESUME BUILDER APPLICATION Project for students

KAMESHS29

Enhancing Performance with Globus and the Science DMZ

Globus

Pushing the limits of ePRTC: 100ns holdover for 100 days

Adtran

Leading Change strategies and insights for effective change management pdf 1.pdf

OnBoard

Securing your Kubernetes cluster_ a step-by-step guide to success !

KatiaHIMEUR1

Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster. However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks. In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.

Quantum Computing: Current Landscape and the Future Role of APIs

Vlad Stirbu

State of ICS and IoT Cyber Threat Landscape Report 2024 preview

Prayukth K V

The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development. The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers: State of global ICS asset and network exposure Sectoral targets and attacks as well as the cost of ransom Global APT activity, AI usage, actor and tactic profiles, and implications Rise in volumes of AI-powered cyberattacks Major cyber events in 2024 Malware and malicious payload trends Cyberattack types and targets Vulnerability exploit attempts on CVEs Attacks on counties – USA Expansion of bot farms – how, where, and why In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East Why are attacks on smart factories rising? Cyber risk predictions Axis of attacks – Europe Systemic attacks in the Middle East Download the full report from here: https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/

FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf

FIDO Alliance

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Aggregage

Elizabeth Buie - Older adults: Are we really designing for our future selves?

Nexer Digital

Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf

Paige Cruz

Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack. While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack. I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:

By Design, not by Accident - Agile Venture Bolzano 2024

Pierluigi Pugliese

A tale of scale & speed: How the US Navy is enabling software delivery from l...

sonjaschweigert1

Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved: - Reduction in onboarding time from 5 weeks to 1 day - Improved developer experience and productivity through actionable findings and reduction of false positives - Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO) Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production. We will cover: - How to remove silos in DevSecOps - How to build efficient development pipeline roles and component templates - How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence) - How to streamline operations with automated policy checks on container images

Recently uploaded (20)

PCI PIN Basics Webinar from the Controlcase Team

UiPath Community Day Dubai: AI at Work..

Essentials of Automations: The Art of Triggers and Actions in FME

Monitoring Java Application Security with JDK Tools and JFR Events

SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdf

zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs

FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf

RESUME BUILDER APPLICATION Project for students

Enhancing Performance with Globus and the Science DMZ

Pushing the limits of ePRTC: 100ns holdover for 100 days

Leading Change strategies and insights for effective change management pdf 1.pdf

Securing your Kubernetes cluster_ a step-by-step guide to success !

Quantum Computing: Current Landscape and the Future Role of APIs

State of ICS and IoT Cyber Threat Landscape Report 2024 preview

FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Elizabeth Buie - Older adults: Are we really designing for our future selves?

Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf

By Design, not by Accident - Agile Venture Bolzano 2024

A tale of scale & speed: How the US Navy is enabling software delivery from l...

ontology based- data_integration.

1. Knowledge Engineering Course (SCOM7348) University of Birzeit, Palestine December, 2012 Synthesis Paper Talk Ontology-Based Data Integration Ali Ahmad Al Jadaa Master of Computing, Birzeit University Ali.ps@live.com This is a student talk, at the Knowledge Engineering course, each student is is asked to present his/her synthesis paper. Course Page: http://jarrar-courses.blogspot.com/2011/09/knowledgeengineering-fall2011.html 1

2. Content • Abstract • What is data integration • What is Semantic • What is Ontology • Heterogeneity conflicts • References 2

3. Abstract 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. 3

4. What is data integration • Data coming from different sources and providing users with a unified view of these data . 4

5. What is Semantic • Understand the meaning . • Relation between signifiers . • Difference with other ≠ 5

6. What is Ontology • Set of concepts within a domain, and the relationships between pairs of concepts. It can be used to model a domain and support reasoning about entities . • Dictionary Computers can be understand • List of things that exist • Description of the kinds of entities there are and how they are related 6

7. Quick Review • Now we know About semantic and ontology and data integration . • We see if we make an Ontology of data integration ,so we will Collide of different heterogeneity of data source . • So we must know about Heterogeneity conflicts 7

8. Heterogeneity conflicts • Syntactic heterogeneity : differences between data models (entity-relationship, relational, object oriented) . • Structural heterogeneity (schematic heterogeneity) : different information systems store their data in different structures. • Semantic heterogeneity :the meaning of the information that is interchanged must be understood across the systems 8

9. References • Ontology-Based Integration of Data Sources Michel Gagnon Defence R&D Canada – Valcartier Department National Defence Québec, Canada Michel.Gagnon@drdc-rddc.g • Ontology-Based Data Integration Methods: A Framework for Comparison Agustina Buccella*, Alejandra Cechich* and Nieves R. Brisaboa • Wikipedia, the free encyclopedia en.wikipedia.org/wiki/Data_integration 9

10. Thank You 10

ontology based- data_integration.

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (15)

Similar to ontology based- data_integration.

Similar to ontology based- data_integration. (20)

Recently uploaded

Recently uploaded (20)

ontology based- data_integration.