Arabic Ontology. Mustafa Jarrar. Reference: Mustafa Jarrar: Lecture Notes on the Arabic Ontology Birzeit University, Palestine, 2015
|
|
|
- Clinton Neal
- 5 years ago
- Views:
Transcription
1 Reference: Mustafa Jarrar: Lecture Notes on the Arabic Ontology Birzeit University, Palestine, 2015 Arabic Ontology االلا أننططووللووججيياا االلععررببييةة Mustafa Jarrar Birzeit University, Palestine wwwjarrarinfo Jarrar
2 ce: Referen s lides lyse nload S ual Ana and Dow oncept tine, 2015 C Watch on es Notes Pales Cours Lecture ity of Birzeit, Online Jarrar: Jarrar: Univers Mustafa Mustafa Course Online Slides nload w o D Watch Mustafa Jarrar estine ity, Pal Univers Birzeit ar@birzeitedu mjarr rarinfo wwwjar Watch this lecture and download the slides from Jarrar
3 The lecture is based on: Mustafa Jarrar: Building A Formal Arabic Ontology (Invited Paper) In proceedings of the Experts Meeting On Arabic Ontologies And Semantic Networks Alecso, Arab League Tunis, July 26-28, 2011Article Slides: Mustafa Jarrar: Towards The Notion Of Gloss, And The Adoption Of Linguistic Resources In Formal Ontology Engineering In proceedings of the 15th International World Wide Web Conference (WWW2006) Edinburgh, Scotland Pages ACM Press ISBN: May 2006http://wwwjarrarinfo/publications/J06pdfhtm Please use both articles if when citing the Arabic Ontology Jarrar
4 االلا أننططووللووججيياا االلععررببييةة The Arabic Ontology Part 1: The Arabic Ontology Design Part 2: Gloss in the Arabic Ontology Part 3: The Top Levels of the Arabic Ontology Part 4: Arabic Ontology Vs WordNet Part 5: Building Synsets Automatically Lecture Keywords: Arabic Ontology, Why Arabic Ontology, Ontology, Linguistic Ontology, Lexical Semantics, Semantics, Meaning, Concept, Upper Level Ontology, Lexical Relation, Semantic Relation, Subtype, subsumption, Hyponymy, Meronymy, Inheritance, Part-whole, WordNet, Arabic WordNet, Synonymy, Polysemy, Gloss, Ontology versus WordNet, Ontology Matching, Thesaurus construction, االلااننططووووللووججيياا االلععررببييةة ااسستتخخدداامم االلااننططووللووججيياا االلععررببييةة االلااننططووللووججيياا ااططووللووججيياا االلللغغةة ععللمم االلددللااللةة االلددللااللةة االلممععننىى االلممففههوومم ححددوودد االلااننططووللووججيياا االلععلليياا االلععللااققااتت االلللغغووييةة االلععللااققااتت االلممففااههييممييةة ععللااققةة االلججننسس االلووررااثثةة ججززء-ككلل, ششببككةة االلممففررددااتت ششببككةة االلممففررددااتت االلععررببييةة تتععدددد االلممععااننيي االلتتررااددفف تتععررييفف االلححدد االلففررقق ببيينن االلااننططووللووججيياا ووششببككةة االلممففررددااتت ررببطط االلااننططووللووججييااتت ممففااههييمميياا ممككننزز ببننااء االلممككااننزز ا آلليياا Jarrar
5 Application ontology vs Linguistic Ontology The importance of linguistic ontologies is growing rapidly An application ontology is a representation of the semantics of a certain domain/application Such as, the FOAF ontology, the Palestinian e- government ontology, the CContology, etc Each word convey one concept (no polysemy) Represents application s knowledge and data structure Used only by a certain application, or a class of applications A linguistic ontology is a representation the semantics of all words of a human language, independently of a particular application Such as WordNet for English Each word may convey several concepts (Polysemy) Represents common-sense knowledge (lexical semantics) Can be used for general purposes è Let s first understand the relations between a word and its meaning(s) Jarrar
6 The Arabic Ontology Project A project started in 2010, at Sina Institute, Birzeit University, Palestine The Arabic Ontology is can be use an Arabic WordNet, but it is more Unlike WordNet, the ArabicOntology is logically and philosophically well-founded, as it follows strict ontological principles è but can be used an Arabic WordNet Built semi-manually è The project is partially funded (Seed funding) by Birzeit University (VP academic Office, Research Committee) Jarrar
7 Why the Arabic Ontology? In application scenarios such as Information Search and Retrieval -to enrich queries and improve the quality of the results, ie meaningful search rather than string-matching search; Machine Translation and Term Disambiguation -by finding the exact mapping of concepts across languages, specially that the Arabic ontology is also mapped to the WordNet; Data Integration and Interoperability -in which the Arabic ontology can be used as a semantic reference to several autonomous information systems; Semantic Web and Web 30 -by using the Arabic ontology as a semantic reference to disambiguate the meanings used in the web sites; among many, many other applications Jarrar
8 Arabic Ontology: Data Model (Simplified) ConceptID (as a synsetid in WordNet) to identify a concept Polysemy and synonymy: like in WordNet, several words (ie, lexical units) can be used to lexicalize one concept (synonymy); and one word might be used to lexicalize several concepts Lexical Unit Gloss: describes a concept Concept ID: concept unique reference Semantic Relations Jarrar
9 Lexical vs Semantic Relationships Semantic relations ممففااههييممييةة) (ععللااققااتت are relationships between concepts (not words), eg, subtype, part-of, etc Lexical relations للغغووييةة) (ععللااققااتت are relationships between words (not concepts), eg, synonym-of, root-of, abbreviation-of, etc Ontologies are mainly concerned with semantic relations Lexical Unit Gloss: describes a concept Semantic Relations Concept ID: concept unique reference Jarrar
10 Arabic Ontology Arabic Ontology: the set of concepts (of all Arabic terms), and the semantic (not lexical) relationships between these concepts االلااننططووللووججيياا االلععررببييةة ههيي ممججممووععةة ممععااننيي ككللممااتت االلللغغةة االلععررببييةة ووععللااققااتت ببيينن ههذذهه Arabic االلممععااننييevery for )ووللييسس ببيينن set of concepts االلككللممااتت( the To build an Arabic Ontology: Identify word (Polysemy), and define semantic relations between these concepts Most important relation is the subtype relation, which leads to a (tree of concepts) Jarrar
11 Arabic Ontology: Subtype Relationships Subtype relation ججننسس ممنن) :(ععللااققةة is a mathematical relations (subset: A B ), such that every instance in A must also be an instance of B Inheritance :(االلتتووااررثث) subtypes inherit all properties of their super types Hyponymy in WordNet is close to (but not the same as) the subtype relation General-Specific :(ا أععمم-ا أخخصص) relations, as in thesauri, are not subtype relations Attention: instance vs Type (Mustafa vs Person) world Jarrar
12 Arabic Ontology: Subtype Relationships It is recommended to use proper subtypes, as it is more strict That is, A and B are never equal, B is always a super set of A It is recommended to classify concepts based on rigidity For example it is wrong to say that a WorkTable is type of Table as being a work table is a non-rigid property As such, subtypes form a tree ووببااللتتاالليي تتصصببحح االلااططووللووججيياا ششججررةة ووللييسس ششببككةة ممععااننيي Please see my lecture about Ontology Modeling (OntoClean) Jarrar
13 االلا أننططووللووججيياا االلععررببييةة The Arabic Ontology Part 1: The Arabic Ontology Design Part 2: Gloss in the Arabic Ontology Part 3: The Top Levels of the Arabic Ontology Part 4: Arabic Ontology Vs WordNet Part 5: Building Synsets Automatically Jarrar
14 Glosses in the Arabic Ontology Writing glosses in the Arabic Ontology follow strict ontological rules (see [J06]) Gloss: describes a concept Jarrar
15 What/ Why a Gloss according to strict ontological guidelines[j06] االلتتععررييففااتت A gloss: is an auxiliary informal (but controlled) account of the intended meaning of a linguistic term, for the commonsense perception of humans A gloss is supposed to render factual knowledge that is critical to understand a concept, but that eg is implausible, unreasonable, or very difficult to formalize and/or articulate explicitly (NOT) to catalogue general information and comments, as eg conventional dictionaries and encyclopedias usually do, or as <rdfs:comment> Jarrar
16 Arabic Ontology: Gloss Guidelines What should and what should not be provided in a gloss: 1 Start with the principal/super type of the concept being defined Eg Search engine : A computer program that, Invoice : A business document that, University : An institution of 2 Focus on distinguishing characteristics and intrinsic prosperities that differentiate the concept out of other concepts Eg Compare, Laptop computer : A computer that is designed to do pretty much anything a desktop computer can do, it runs for a short time (usually two to five hours) on batteries A portable computer small enough to use in your lap للججننسس االلااععللىى للللممففههوومم االلممرراادد تتععررييففةة تتللييههاا االلصصففااتت االلججووههووررييةة/ االلممممييززةة للللممففههوومم االلممرراادد تتععررييففةة ققووااععدد ككتتااببةة االلتتععررييففااتت 3 Written in a form of propositions, offering the reader inferential knowledge that help him to construct the image of the concept Eg Compare Search engine : A computer program for searching the internet, it can be defined as one of the most useful aspects of the World Wide Web Some of the major ones are Google, ; A computer program that enables users to search and retrieves documents or data from a database or from a computer network ققضضاايياا ببططررييققةة تتققوودد االلققااررىىء للااسستتننببااطط االلممععننىى Jarrar
17 Arabic Ontology: Gloss Guidelines 4 Use supportive examples : - To clarify cases that are commonly known to be false but they are true, or that are known to be true but they are false; - To strengthen and illustrate distinguishing characteristics (eg define by examples, counter-examples) Examples can be types and/or instances of the concept being defined 5 Be consistent with formal definitions/axioms 6 Be sufficient, clear, and easy to understand ا إسستتخخددمم االلااممثثللةة ممسسممووحح ووللككنن ببتتححففظظ ششددييدد ووححااللااتت ممععييننةة è WordNet glosses do not follow such ontological guidelines Jarrar
18 Arabic Ontology: Gloss Guidelines As a gloss starts with a supertype of concept being defined, try to read the gloss as the following, to verify what you do is correct: جدول: مصفوفة بيانات مكونة من صفوف وأعمدة جدول: ترتيب بيانات جنبBا الى جنب على شكل صفوف وأعمدة جدول: تنظيم بيانات بصورة ممنهجة جنبBا الى جنب على شكل صفوف وأعمدة Jarrar
19 االلا أننططووللووججيياا االلععررببييةة The Arabic Ontology Part 1: The Arabic Ontology Design Part 2: Gloss in the Arabic Ontology Part 3: The Top Levels of the Arabic Ontology Part 4: Arabic Ontology Vs WordNet Part 5: Building Synsets Automatically Jarrar
20 The Core (Top Levels) of the Arabic Ontology االلححددوودد االلععلليياا: ههيي أمهات املعاني جلميع الكلمات العربية Arabic Core Ontology: the top levels of the Arabic Ontology - built manually, and carefully considering the the philosophical and historical aspects of the Arabic concepts\terms, as well as BFO and DOLCE upper level ontologies Different from the 25 unique beginners in WordNet Top 3 levels shown here, for simplicity Jarrar
21 Why these Core Concepts (Top Levels)? االلححددوودد االلععلليياا: ههيي أمهات املعاني جلميع الكلمات العربية Why this these top levels are so important: The 10 th level of this core ontology should top all Arabic concepts and levels This allow us to detect any problems in the tree/relations! The core Ontology governs the correctness and the evolution of the whole Arabic Ontology Top 3 levels shown here, for simplicity Jarrar
22 االلا أننططووللووججيياا االلععررببييةة The Arabic Ontology Part 1: The Arabic Ontology Design Part 2: Gloss in the Arabic Ontology Part 3: The Top Levels of the Arabic Ontology Part 4: Arabic Ontology Vs WordNet Part 5: Building Synsets Automatically Jarrar
23 Ontology Versus WordNet Meaning (called Ontological Precision): WordNet: based on what native speakers agree roughly Ontology: based on Scientific and philosophical findings Classification: WordNet: based on what native speakers agree roughly eg, (Student IsA person) (Nile IsA River) Ontology: based on strict formal methodologies eg, (Student IsA role) (Nile InstanceOf River) Formal Specification: WordNet: logically vague Ontology: strictly formal à WordNet as a linguistic ontology, though it needs lots of cleaning! à Linguistic ontologies are difficult to build but they are immune to changes Jarrar
24 Arabic Ontology Vs WordNet Unlike WordNet, the Arabic Ontology is: 1 Philosophically well founded: Focuses on intrinsic properties; All types are rigid; The top level is derived from known Top Level Ontologies 2 Strictly formal: Semantic relations are well-defined relations 3 Strictly-controlled glosses The content and structure of the glosses is strictly based on ontological principles è The Arabic Ontology can be used an Arabic WordNet, but is more è The Arabic Ontology follows a similar to WordNet, and its well-mapped Jarrar
25 االلا أننططووللووججيياا االلععررببييةة The Arabic Ontology Part 1: The Arabic Ontology Design Part 2: Gloss in the Arabic Ontology Part 3: The Top Levels of the Arabic Ontology Part 4: Arabic Ontology Vs WordNet Part 5: Building Synsets Automatically (this is a research part) Jarrar
26 The Goal Given a bilingual dictionary (eg Arabic-English) can we derive an Arabic-Arabic thesaurus? For example: جدول: نهر قناة ماء جدول: مصفوفة قائمة è This will help finding possible Arabic synsets, which help detecting possible subtype relations and/or validate the existing relations Jarrar
27 The Algorithm Build Graph: Convert the bilingual dictionary into a graph or words, where the first the first level is an Arabic word, the 2 nd level is the English translations of the 1 st level words, the 3 rd level is the translation of 2 nd Level, and so on, until no translation or same the word is repeated (ie, cycle) A synset can be then the set of words in each branch with cycle Jarrar
28 Example: Arabic English Dictionary ققييننا أ tidy ببتترر sort ممخخضض tidy ببتترر arrange ففيي ظظنن tidy ببتترر order ييججههننمم tidy ببتترر set ممظظننمم tidy ببتترر tidy ممددننههمم tidy ببتترر pack ةةممززحح pack ببتترر shape ةةممززرر pack ببتترر form ببضضوو pack عع وونن sort ممووككتت pack للككشش sort ااششحح pack ببررضض sort sort shape ععووننللاا ااذذهه للككشش sort shape ففننصص ةةللااحح sort shape ةةققييررطط ةةي ئييهه arrange shape ممظظنن ررههظظمم arrange shape ذذخختتاا ددسس ججتت arrange form فف االلخخللاا ىىووسس للككشش arrange form للددعع ةةررااممتتسساا order form ممااظظننللاا ةةرر ووصص order form ببييتتررتت ععوونن order form ررمما أ ةةي ئييهه set form ةةععووممججمم ةةغغييصص set tune ععضضوو ببتترر ببررضض form ططببضض set Jarrar 2015 ببتترر organize ا أددبب set 28
29 Level 1 Jarrar
30 Level 3 Jarrar
31 Level 4 Jarrar
32 Cycle Jarrar
33 Synsets found }ررتتبب ععددلل{=} regulate }arrange, }ررتتبب ننظظمم{={ arrange, {organize }ررتتبب ضضببطط tune}=} {set, }ررتتبب ا أننقق{={ neat {tidy, Jarrar
34 Project 1 Build a program that takes a bilingual dictionary (any two language) as text file, and generates all possible synsets (the output is also a text file) You may bring any Arabic-English dictionary to try Jarrar
35 Project 2 Given a set of 15 Arabic words build an Arabic Ontology for these words (for all possible meanings), and link these concepts with WordNet synsets Jarrar
Uniqueness and Identity Rules in ORM
Reference: Mustafa Jarrar: Lecture Notes on Uniqueness and Identity Rules in ORM Birzeit University, Palestine, 2015 Uniqueness and Identity Rules in ORM (Chapter 4) Mustafa Jarrar Birzeit University,
Data Integration and Fusion using RDF
Lecture Notes on Data Integration and Fusion Using RDF University of Birzeit, Palestine 2013 Data Integration and Fusion using RDF Dr. Mustafa Jarrar University of Birzeit mjarrar@birzeit.edu www.jarrar.info
Subset, Equality, and Exclusion Rules In ORM
Reference: Mustafa Jarrar: Lecture Notes on Subset, Equality, and Exclusion Rules in ORM Birzeit University, Palestine, 2015 Subset, Equality, and Exclusion Rules In ORM (Chapter 6) Mustafa Jarrar Birzeit
Uniqueness and Identity Rules in ORM
Mustafa Jarrar: Lecture Notes on Uniqueness and Identity Rules in ORM. University of Birzeit, Palestine, 2018 Version 4 Uniqueness and Identity Rules in ORM (Chapter 4) Mustafa Jarrar Birzeit University
Quick Mathematical Background for Conceptual Modeling
Reference: Mustafa Jarrar: Lecture Notes on Mathematics for Conceptual Modeling University of Birzeit, Palestine, 2015 Quick Mathematical Background for Conceptual Modeling (Chapter 6) Dr. Mustafa Jarrar
Mandatory Roles. Dr. Mustafa Jarrar. Knowledge Engineering (SCOM7348) (Chapter 5) University of Birzeit
Lecture Notes on Mandatory Roles Birzeit University 2011 Knowledge Engineering (SCOM7348) Mandatory Roles (Chapter 5) Dr. Mustafa Jarrar University of Birzeit mjarrar@birzeit.edu www.jarrar.info Jarrar
Dr. Mustafa Jarrar. Knowledge Engineering (SCOM7348) (Chapter 4) University of Birzeit
Mustafa Jarrar Lecture Notes, Knowledge Engineering (SCOM7348) University of Birzeit 1 st Semester, 2011 Knowledge Engineering (SCOM7348) Uniqueness (Chapter 4) Dr. Mustafa Jarrar University of Birzeit
Schema Equivalence and Optimization
Reference: Mustafa Jarrar: Lecture Notes on Schema Equivalence and Optimization in ORM Birzeit University, Palestine, 2015 Schema Equivalence and Optimization Mustafa Jarrar Birzeit University, Palestine
Subset, Equality, and Exclusion Rules In ORM
Reference: Mustafa Jarrar: Lecture Notes on Subset, Equality, and Exclusion Rules in ORM University of Birzeit, Palestine, 2015 Subset, Equality, and Exclusion Rules In ORM (Chapter 6) Dr. Mustafa Jarrar
Shrey Patel B.E. Computer Engineering, Gujarat Technological University, Ahmedabad, Gujarat, India
International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2018 IJSRCSEIT Volume 3 Issue 3 ISSN : 2456-3307 Some Issues in Application of NLP to Intelligent
ORM Modeling Tips and Common Mistakes
Reference: Mustafa Jarrar: Lecture Notes on ORM Modeling Tips and Common Mistakes University of Birzeit, Palestine, 2015 ORM Modeling Tips and Common Mistakes Dr. Mustafa Jarrar University of Birzeit mjarrar@birzeit.edu
Number Theory and Proof Methods
9/6/17 Lecture Notes on Discrete Mathematics. Birzeit University Palestine 2016 and Proof Methods Mustafa Jarrar 4.1 Introduction 4.3 Divisibility 4.4 Quotient-Remainder Theorem mjarrar 2015 1 Watch this
"Towards Automatic Construction of Arabic Synonyms"
Faculty of Engineering and Technology Master Program in Computing Thesis report "Towards Automatic Construction of Arabic Synonyms" " نحو بناء مكنز مترادفات للغة العربية اليا " Student Number:1105486 Supervised
SIERA: A Multilingual Knowledge Sharing Project
Seminar on Multilingualism and Terminology April 18, 2013 - Paris SIERA: A Multilingual Knowledge Sharing Project Pr. Christophe Roche - Universidade Nova de Lisboa, CLUNL - Portugal - Université de Savoie,
0.1 Knowledge Organization Systems for Semantic Web
0.1 Knowledge Organization Systems for Semantic Web 0.1 Knowledge Organization Systems for Semantic Web 0.1.1 Knowledge Organization Systems Why do we need to organize knowledge? Indexing Retrieval Organization
Introduction to Information Retrieval
Mustafa Jarrar: Lecture Notes on Information Retrieval University of Birzeit, Palestine 2014 Introduction to Information Retrieval Dr. Mustafa Jarrar Sina Institute, University of Birzeit mjarrar@birzeit.edu
Introduction to Data Integration
Lecture Notes, Web Data Management Birzeit University, Palestine 2013 Introduction to Data Integration Dr. Mustafa Jarrar University of Birzeit mjarrar@birzeit.edu www.jarrar.info Jarrar 2013 1 Watch this
Schema Equivalence and Optimization
Reference: Mustafa Jarrar: Lecture Notes on Schema Equivalence and Optimization in ORM Birzeit University, Palestine, 2015 Schema Equivalence and Optimization Dr. Mustafa Jarrar University of Birzeit mjarrar@birzeit.edu
Data Schema Integration
Mustafa Jarrar Lecture Notes, Web Data Management (MCOM7348) University of Birzeit, Palestine 1 st Semester, 2013 Data Schema Integration Dr. Mustafa Jarrar University of Birzeit mjarrar@birzeit.edu www.jarrar.info
INTERCONNECTING AND MANAGING MULTILINGUAL LEXICAL LINKED DATA. Ernesto William De Luca
INTERCONNECTING AND MANAGING MULTILINGUAL LEXICAL LINKED DATA Ernesto William De Luca Overview 2 Motivation EuroWordNet RDF/OWL EuroWordNet RDF/OWL LexiRes Tool Conclusions Overview 3 Motivation EuroWordNet
Mustafa Jarrar: Lecture Notes on RDF Schema Birzeit University, Version 3. RDFS RDF Schema. Mustafa Jarrar. Birzeit University
Mustafa Jarrar: Lecture Notes on RDF Schema Birzeit University, 2018 Version 3 RDFS RDF Schema Mustafa Jarrar Birzeit University 1 Watch this lecture and download the slides Course Page: http://www.jarrar.info/courses/ai/
! " # Formal Classification. Logics for Data and Knowledge Representation. Classification Hierarchies (1) Classification Hierarchies (2)
,!((,.+#$),%$(-&.& *,(-$)%&.'&%!&, Logics for Data and Knowledge Representation Alessandro Agostini agostini@dit.unitn.it University of Trento Fausto Giunchiglia fausto@dit.unitn.it Formal Classification!$%&'()*$#)
2 Experimental Methodology and Results
Developing Consensus Ontologies for the Semantic Web Larry M. Stephens, Aurovinda K. Gangam, and Michael N. Huhns Department of Computer Science and Engineering University of South Carolina, Columbia,
Text Similarity. Semantic Similarity: Synonymy and other Semantic Relations
NLP Text Similarity Semantic Similarity: Synonymy and other Semantic Relations Synonyms and Paraphrases Example: post-close market announcements The S&P 500 climbed 6.93, or 0.56 percent, to 1,243.72,
University of Rome Tor Vergata GENOMA. GENeric Ontology Matching Architecture
University of Rome Tor Vergata GENOMA GENeric Ontology Matching Architecture Maria Teresa Pazienza +, Roberto Enea +, Andrea Turbati + + ART Group, University of Rome Tor Vergata, Via del Politecnico 1,
Ontology Research Group Overview
Ontology Research Group Overview ORG Dr. Valerie Cross Sriram Ramakrishnan Ramanathan Somasundaram En Yu Yi Sun Miami University OCWIC 2007 February 17, Deer Creek Resort OCWIC 2007 1 Outline Motivation
KOSO: A Reference-Ontology for Reuse of Existing Knowledge Organization Systems
KOSO: A Reference-Ontology for Reuse of Existing Knowledge Organization Systems International Workshop on Knowledge Reuse and Reengineering over the Semantic Web (KRRSW 2008) ESWC 2008 Tenerife, Spain,
Contributions to the Study of Semantic Interoperability in Multi-Agent Environments - An Ontology Based Approach
Int. J. of Computers, Communications & Control, ISSN 1841-9836, E-ISSN 1841-9844 Vol. V (2010), No. 5, pp. 946-952 Contributions to the Study of Semantic Interoperability in Multi-Agent Environments -
From Scratch to the Web: Terminological Theses at the University of Innsbruck
Peter Sandrini University of Innsbruck From Scratch to the Web: Terminological Theses at the University of Innsbruck Terminology Diploma Theses (TDT) have been well established in the training of translators
Chapter 8: Enhanced ER Model
Chapter 8: Enhanced ER Model Subclasses, Superclasses, and Inheritance Specialization and Generalization Constraints and Characteristics of Specialization and Generalization Hierarchies Modeling of UNION
WonderWeb Deliverable D19
WonderWeb Deliverable D19 Impact of foundational ontologies on standardization activities Aldo Gangemi, Nicola Guarino ISTC-CNR email: a.gangemi@istc.cnr.it, guarino@loa-cnr.it Identifier D19 Class Deliverable
On Transformation from The Thesaurus into Domain Ontology
On Transformation from The Thesaurus into Domain Ontology Ping Li, Yong Li Department of Computer Science and Engineering, Qujing Normal University Qujing, 655011, China E-mail: qjncliping@126.com Abstract:
Introduction to Conceptual Data Modeling
Mustafa Jarrar: Lecture Notes on Introduction to Conceptual Data Modeling. University of Birzeit, Palestine, 2018 Version 4 Introduction to Conceptual Data Modeling (Chapter 1 & 2) Mustafa Jarrar Birzeit
Semantic Web. Ontology Engineering and Evaluation. Morteza Amini. Sharif University of Technology Fall 93-94
ه عا ی Semantic Web Ontology Engineering and Evaluation Morteza Amini Sharif University of Technology Fall 93-94 Outline Ontology Engineering Class and Class Hierarchy Ontology Evaluation 2 Outline Ontology
Making Sense Out of the Web
Making Sense Out of the Web Rada Mihalcea University of North Texas Department of Computer Science rada@cs.unt.edu Abstract. In the past few years, we have witnessed a tremendous growth of the World Wide
Module 8. Other representation formalisms. Version 2 CSE IIT, Kharagpur
Module 8 Other representation formalisms 8.1 Instructional Objective The students should understand the syntax and semantic of semantic networks Students should learn about different constructs and relations
Semantic Web. Ontology Engineering and Evaluation. Morteza Amini. Sharif University of Technology Fall 95-96
ه عا ی Semantic Web Ontology Engineering and Evaluation Morteza Amini Sharif University of Technology Fall 95-96 Outline Ontology Engineering Class and Class Hierarchy Ontology Evaluation 2 Outline Ontology
HELP ON THE VIRTUAL LIBRARY
HELP ON THE VIRTUAL LIBRARY The Virtual Library search system allows accessing in a quick way to the information the students are interested in and that are available in the Didactic Cyberspace. In its
VISO: A Shared, Formal Knowledge Base as a Foundation for Semi-automatic InfoVis Systems
VISO: A Shared, Formal Knowledge Base as a Foundation for Semi-automatic InfoVis Systems Jan Polowinski Martin Voigt Technische Universität DresdenTechnische Universität Dresden 01062 Dresden, Germany
PRIOR System: Results for OAEI 2006
PRIOR System: Results for OAEI 2006 Ming Mao, Yefei Peng University of Pittsburgh, Pittsburgh, PA, USA {mingmao,ypeng}@mail.sis.pitt.edu Abstract. This paper summarizes the results of PRIOR system, which
The UNESCO Thesaurus
UNESCO Thesaurus Thésaurus de l UNESCO Tesauro de la UNESCO Тезаурус ЮНЕСКО The UNESCO Thesaurus UN-LINKS Meeting 28-30 November Meron Ewketu UNESCO Library Introduction There are too many ways of expressing
RDF Graph Data Model
Mustafa Jarrar: Lecture Notes on RDF Data Model Birzeit University, 2018 Version 7 RDF Graph Data Model Mustafa Jarrar Birzeit University 1 Watch this lecture and download the slides Course Page: http://www.jarrar.info/courses/ai/
Web-page Indexing based on the Prioritize Ontology Terms
Web-page Indexing based on the Prioritize Ontology Terms Sukanta Sinha 1, 4, Rana Dattagupta 2, Debajyoti Mukhopadhyay 3, 4 1 Tata Consultancy Services Ltd., Victoria Park Building, Salt Lake, Kolkata
CHAPTER-26 Mining Text Databases
CHAPTER-26 Mining Text Databases 26.1 Introduction 26.2 Text Data Analysis and Information Retrieval 26.3 Basle Measures for Text Retrieval 26.4 Keyword-Based and Similarity-Based Retrieval 26.5 Other
SKOS. COMP62342 Sean Bechhofer
SKOS COMP62342 Sean Bechhofer sean.bechhofer@manchester.ac.uk Ontologies Metadata Resources marked-up with descriptions of their content. No good unless everyone speaks the same language; Terminologies
Punjabi WordNet Relations and Categorization of Synsets
Punjabi WordNet Relations and Categorization of Synsets Rupinderdeep Kaur Computer Science Engineering Department, Thapar University, rupinderdeep@thapar.edu Suman Preet Department of Linguistics and Punjabi
Semantic Interoperability. Being serious about the Semantic Web
Semantic Interoperability Jérôme Euzenat INRIA & LIG France Natasha Noy Stanford University USA 1 Being serious about the Semantic Web It is not one person s ontology It is not several people s common
Contextual Information Retrieval Using Ontology-Based User Profiles
Contextual Information Retrieval Using Ontology-Based User Profiles Vishnu Kanth Reddy Challam Master s Thesis Defense Date: Jan 22 nd, 2004. Committee Dr. Susan Gauch(Chair) Dr.David Andrews Dr. Jerzy
Sense-based Information Retrieval System by using Jaccard Coefficient Based WSD Algorithm
ISBN 978-93-84468-0-0 Proceedings of 015 International Conference on Future Computational Technologies (ICFCT'015 Singapore, March 9-30, 015, pp. 197-03 Sense-based Information Retrieval System by using
Ontologies SKOS. COMP62342 Sean Bechhofer
Ontologies SKOS COMP62342 Sean Bechhofer sean.bechhofer@manchester.ac.uk Metadata Resources marked-up with descriptions of their content. No good unless everyone speaks the same language; Terminologies
Introduction to Web 2.0 Data Mashups
Lecture Notes on Web Data Management Birzeit University, Palestine 2013 Introduction to Web 2.0 Data Mashups Dr. Mustafa Jarrar University of Birzeit mjarrar@birzeit.edu www.jarrar.info Jarrar 2013 1 Watch
Solving problem of semantic terminology in digital library
International Journal of Advances in Intelligent Informatics ISSN: 2442-6571 20 Solving problem of semantic terminology in digital library Herlina Jayadianti Universitas Pembangunan Nasional Veteran Yogyakarta,
An Improving for Ranking Ontologies Based on the Structure and Semantics
An Improving for Ranking Ontologies Based on the Structure and Semantics S.Anusuya, K.Muthukumaran K.S.R College of Engineering Abstract Ontology specifies the concepts of a domain and their semantic relationships.
Introduction to Ontology
Lecture Notes, University of Birzeit Palestine Introduction to Ontology Dr. Mustafa Jarrar University of Birzeit mjarrar@birzeit.edu www.jarrar.info Jarrar 2011 1 Reading Material 0) Everything in these
International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 4, Jul-Aug 2015
RESEARCH ARTICLE OPEN ACCESS Multi-Lingual Ontology Server (MOS) For Discovering Web Services Abdelrahman Abbas Ibrahim [1], Dr. Nael Salman [2] Department of Software Engineering [1] Sudan University
COOPERATIVE EDITING APPROACH FOR BUILDING WORDNET DATABASE
Key words Wordnet, TouchGraph, Graph-based semantic editing Konrad DUSZA, Łukasz BYCZKOWSKI, Julian SZYMANSKI COOPERATIVE EDITING APPROACH FOR BUILDING WORDNET DATABASE The paper presents a approach for
CPS331 Lecture: Fuzzy Logic last revised October 11, Objectives: 1. To introduce fuzzy logic as a way of handling imprecise information
CPS331 Lecture: Fuzzy Logic last revised October 11, 2016 Objectives: 1. To introduce fuzzy logic as a way of handling imprecise information Materials: 1. Projectable of young membership function 2. Projectable
WordNet-based User Profiles for Semantic Personalization
PIA 2005 Workshop on New Technologies for Personalized Information Access WordNet-based User Profiles for Semantic Personalization Giovanni Semeraro, Marco Degemmis, Pasquale Lops, Ignazio Palmisano LACAM
Adaptive Hypermedia Systems Analysis Approach by Means of the GAF Framework
Adaptive Hypermedia Systems Analysis Approach by Means of the GAF Framework Evgeny Knutov, Paul De Bra, and Mykola Pechenizkiy Department of Computer Science, Eindhoven University of Technology, P.O. Box
Information technology - Metadata registries (MDR) - Part 5: Naming principles
1 2 3 ISO/IEC JTC1 SC32 N Date: 2013-12-18 ISO/IEC DIS 11179-5 4 5 ISO/IEC JTC1/SC32/WG2 6 7 Secretariat: ANSI 8 9 10 11 Information technology - Metadata registries (MDR) - Part 5: Naming principles Technologies
Lightweight Transformation of Tabular Open Data to RDF
Proceedings of the I-SEMANTICS 2012 Posters & Demonstrations Track, pp. 38-42, 2012. Copyright 2012 for the individual papers by the papers' authors. Copying permitted only for private and academic purposes.
SEARCH TECHNIQUES: BASIC AND ADVANCED
17 SEARCH TECHNIQUES: BASIC AND ADVANCED 17.1 INTRODUCTION Searching is the activity of looking thoroughly in order to find something. In library and information science, searching refers to looking through
ROUGH SETS THEORY AND UNCERTAINTY INTO INFORMATION SYSTEM
ROUGH SETS THEORY AND UNCERTAINTY INTO INFORMATION SYSTEM Pavel Jirava Institute of System Engineering and Informatics Faculty of Economics and Administration, University of Pardubice Abstract: This article
A Method for Semi-Automatic Ontology Acquisition from a Corporate Intranet
A Method for Semi-Automatic Ontology Acquisition from a Corporate Intranet Joerg-Uwe Kietz, Alexander Maedche, Raphael Volz Swisslife Information Systems Research Lab, Zuerich, Switzerland fkietz, volzg@swisslife.ch
The Data Web and Linked Data.
Mustafa Jarrar Lecture Notes, Knowledge Engineering (SCOM7348) University of Birzeit 1 st Semester, 2011 Knowledge Engineering (SCOM7348) The Data Web and Linked Data. Dr. Mustafa Jarrar University of
0.1 Upper ontologies and ontology matching
0.1 Upper ontologies and ontology matching 0.1.1 Upper ontologies Basics What are upper ontologies? 0.1 Upper ontologies and ontology matching Upper ontologies (sometimes also called top-level or foundational
Debugging the missing is-a structure within taxonomies networked by partial reference alignments
Debugging the missing is-a structure within taxonomies networked by partial reference alignments Patrick Lambrix and Qiang Liu Linköping University Post Print N.B.: When citing this work, cite the original
A service based on Linked Data to classify Web resources using a Knowledge Organisation System
A service based on Linked Data to classify Web resources using a Knowledge Organisation System A proof of concept in the Open Educational Resources domain Abstract One of the reasons why Web resources
Metadata Common Vocabulary: a journey from a glossary to an ontology of statistical metadata, and back
Joint UNECE/Eurostat/OECD Work Session on Statistical Metadata (METIS) Lisbon, 11 13 March, 2009 Metadata Common Vocabulary: a journey from a glossary to an ontology of statistical metadata, and back Sérgio
COMP90042 LECTURE 3 LEXICAL SEMANTICS COPYRIGHT 2018, THE UNIVERSITY OF MELBOURNE
COMP90042 LECTURE 3 LEXICAL SEMANTICS SENTIMENT ANALYSIS REVISITED 2 Bag of words, knn classifier. Training data: This is a good movie.! This is a great movie.! This is a terrible film. " This is a wonderful
Semantic Retrieval of the TIB AV-Portal. Dr. Sven Strobel IATUL 2015 July 9, 2015; Hannover
Semantic Retrieval of the TIB AV-Portal Dr. Sven Strobel IATUL 2015 July 9, 2015; Hannover Semantic Retrieval of the TIB AV-Portal Contents 1. TIB AV-Portal 2. Automatic Video Analysis 3. Named-Entity
Information mining and information retrieval : methods and applications
Information mining and information retrieval : methods and applications J. Mothe, C. Chrisment Institut de Recherche en Informatique de Toulouse Université Paul Sabatier, 118 Route de Narbonne, 31062 Toulouse
Automatic Wordnet Mapping: from CoreNet to Princeton WordNet
Automatic Wordnet Mapping: from CoreNet to Princeton WordNet Jiseong Kim, Younggyun Hahm, Sunggoo Kwon, Key-Sun Choi Semantic Web Research Center, School of Computing, KAIST 291 Daehak-ro, Yuseong-gu,
Conceptual Data Modeling Concepts & Principles
Mustafa Jarrar: Lecture Notes on Conceptual Modeling Concepts. Birzeit University, Palestine. 2011 Conceptual Data Modeling Concepts & Principles (Chapter 1&2) Mustafa Jarrar Birzeit University mjarrar@birzeit.edu
Effect of log-based Query Term Expansion on Retrieval Effectiveness in Patent Searching
Effect of log-based Query Term Expansion on Retrieval Effectiveness in Patent Searching Wolfgang Tannebaum, Parvaz Madabi and Andreas Rauber Institute of Software Technology and Interactive Systems, Vienna
Information management - Topic Maps visualization
Information management - Topic Maps visualization Benedicte Le Grand Laboratoire d Informatique de Paris 6, Universite Pierre et Marie Curie, Paris, France Benedicte.Le-Grand@lip6.fr http://www-rp.lip6.fr/~blegrand
A Semantic Web-Based Approach for Harvesting Multilingual Textual. definitions from Wikipedia to support ICD-11 revision
A Semantic Web-Based Approach for Harvesting Multilingual Textual Definitions from Wikipedia to Support ICD-11 Revision Guoqian Jiang 1,* Harold R. Solbrig 1 and Christopher G. Chute 1 1 Department of
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network Roberto Navigli, Simone Paolo Ponzetto What is BabelNet a very large, wide-coverage multilingual
A Novel Architecture of Ontology based Semantic Search Engine
International Journal of Science and Technology Volume 1 No. 12, December, 2012 A Novel Architecture of Ontology based Semantic Search Engine Paras Nath Gupta 1, Pawan Singh 2, Pankaj P Singh 3, Punit
CHAPTER 5 SEARCH ENGINE USING SEMANTIC CONCEPTS
82 CHAPTER 5 SEARCH ENGINE USING SEMANTIC CONCEPTS In recent years, everybody is in thirst of getting information from the internet. Search engines are used to fulfill the need of them. Even though the
WEIGHTING QUERY TERMS USING WORDNET ONTOLOGY
IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.4, April 2009 349 WEIGHTING QUERY TERMS USING WORDNET ONTOLOGY Mohammed M. Sakre Mohammed M. Kouta Ali M. N. Allam Al Shorouk
State of the Art: Patterns in Ontology Engineering
: Patterns in Ontology Engineering Eva Blomqvist ISSN 1404-0018 Research Report 04:8 : Patterns in Ontology Engineering Eva Blomqvist Information Engineering Research Group Department of Electronic and
Ontology Matching with CIDER: Evaluation Report for the OAEI 2008
Ontology Matching with CIDER: Evaluation Report for the OAEI 2008 Jorge Gracia, Eduardo Mena IIS Department, University of Zaragoza, Spain {jogracia,emena}@unizar.es Abstract. Ontology matching, the task
Ch t 8 Chapter 8. System Models
Ch t 8 Chapter 8. System Models Objectives To explain why the context t of a system should be modelled d as a part of requirements engineering process To describe behavioural modelling, data modelling
An e-infrastructure for Language Documentation on the Web
An e-infrastructure for Language Documentation on the Web Gary F. Simons, SIL International William D. Lewis, University of Washington Scott Farrar, University of Arizona D. Terence Langendoen, National
COMP 250 Fall inheritance Nov. 17, 2017
Inheritance In our daily lives, we classify the many things around us. The world has objects like dogs and cars and food and we are familiar with talking about these objects as classes Dogs are animals
Putting ontologies to work in NLP
Putting ontologies to work in NLP The lemon model and its future John P. McCrae National University of Ireland, Galway Introduction In natural language processing we are doing three main things Understanding
Data-Mining Algorithms with Semantic Knowledge
Data-Mining Algorithms with Semantic Knowledge Ontology-based information extraction Carlos Vicient Monllaó Universitat Rovira i Virgili December, 14th 2010. Poznan A Project funded by the Ministerio de
Einführung in die Programmierung Introduction to Programming
Chair of Software Engineering Einführung in die Programmierung Introduction to Programming Prof. Dr. Bertrand Meyer Lecture 3: Dealing with Objects II Programming languages The programming language is
Assessing the Quality of Natural Language Text
Assessing the Quality of Natural Language Text DC Research Ulm (RIC/AM) daniel.sonntag@dfki.de GI 2004 Agenda Introduction and Background to Text Quality Text Quality Dimensions Intrinsic Text Quality,
Evaluating a Conceptual Indexing Method by Utilizing WordNet
Evaluating a Conceptual Indexing Method by Utilizing WordNet Mustapha Baziz, Mohand Boughanem, Nathalie Aussenac-Gilles IRIT/SIG Campus Univ. Toulouse III 118 Route de Narbonne F-31062 Toulouse Cedex 4
Enhanced retrieval using semantic technologies:
Enhanced retrieval using semantic technologies: Ontology based retrieval as a new search paradigm? - Considerations based on new projects at the Bavarian State Library Dr. Berthold Gillitzer 28. Mai 2008
A Survey Of Different Text Mining Techniques Varsha C. Pande 1 and Dr. A.S. Khandelwal 2
A Survey Of Different Text Mining Techniques Varsha C. Pande 1 and Dr. A.S. Khandelwal 2 1 Department of Electronics & Comp. Sc, RTMNU, Nagpur, India 2 Department of Computer Science, Hislop College, Nagpur,
SEMANTIC MATCHING APPROACHES
CHAPTER 4 SEMANTIC MATCHING APPROACHES 4.1 INTRODUCTION Semantic matching is a technique used in computer science to identify information which is semantically related. In order to broaden recall, a matching
Historical Text Mining:
Historical Text Mining Historical Text Mining, and Historical Text Mining: Challenges and Opportunities Dr. Robert Sanderson Dept. of Computer Science University of Liverpool azaroth@liv.ac.uk http://www.csc.liv.ac.uk/~azaroth/
The Conference Review System with WSDM
The Conference Review System with WSDM Olga De Troyer, Sven Casteleyn Vrije Universiteit Brussel WISE Research group Pleinlaan 2, B-1050 Brussel, Belgium Olga.DeTroyer@vub.ac.be, svcastel@vub.ac.be 1 Introduction
Semantics Isn t Easy Thoughts on the Way Forward
Semantics Isn t Easy Thoughts on the Way Forward NANCY IDE, VASSAR COLLEGE REBECCA PASSONNEAU, COLUMBIA UNIVERSITY COLLIN BAKER, ICSI/UC BERKELEY CHRISTIANE FELLBAUM, PRINCETON UNIVERSITY New York University
Text Mining. Munawar, PhD. Text Mining - Munawar, PhD
10 Text Mining Munawar, PhD Definition Text mining also is known as Text Data Mining (TDM) and Knowledge Discovery in Textual Database (KDT).[1] A process of identifying novel information from a collection
I&R SYSTEMS ON THE INTERNET/INTRANET CITES AS THE TOOL FOR DISTANCE LEARNING. Andrii Donchenko
International Journal "Information Technologies and Knowledge" Vol.1 / 2007 293 I&R SYSTEMS ON THE INTERNET/INTRANET CITES AS THE TOOL FOR DISTANCE LEARNING Andrii Donchenko Abstract: This article considers
The Topic Specific Search Engine
The Topic Specific Search Engine Benjamin Stopford 1 st Jan 2006 Version 0.1 Overview This paper presents a model for creating an accurate topic specific search engine through a focussed (vertical)