assam a tool for semi automatically annotating semantic
play

ASSAM: A Tool for Semi-Automatically Annotating Semantic Web - PowerPoint PPT Presentation

Jan 15, 2024 •580 likes •1.27k views

Outline Introduction ASSAM Annotator OATS Conclusion ASSAM: A Tool for Semi-Automatically Annotating Semantic Web Services Andreas He, Eddie Johnston and Nicholas Kushmerick University College Dublin, Ireland November 10, 2004 4 ,

  1. Outline Introduction ASSAM Annotator OATS Conclusion ASSAM: A Tool for Semi-Automatically Annotating Semantic Web Services Andreas Heß, Eddie Johnston and Nicholas Kushmerick University College Dublin, Ireland November 10, 2004 4 , Andreas Heß ASSAM

  2. Outline Introduction ASSAM Annotator OATS Conclusion Outline Introduction 1 Motivation Progress made ASSAM Annotator 2 The Annotator Application Relational Learning Evaluation OATS 3 Conclusion 4 Andreas Heß ASSAM

  3. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Outline Introduction 1 Motivation Progress made ASSAM Annotator 2 The Annotator Application Relational Learning Evaluation OATS 3 Conclusion 4 Andreas Heß ASSAM

  4. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Outline Introduction 1 Motivation Progress made ASSAM Annotator 2 The Annotator Application Relational Learning Evaluation OATS 3 Conclusion 4 Andreas Heß ASSAM

  5. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Semantic Web Services Desired Features Automatic discovery Automatic composition Automatic invocation Andreas Heß ASSAM

  6. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Semantic Metadata Assumptions Semantic annotation, e.g. OWL-S Shared ontology Problems Hand-crafting annotations can be tedious Integrate legacy web services Our Solution Learn mappings to ontology! Andreas Heß ASSAM

  7. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Semantic Metadata Assumptions Semantic annotation, e.g. OWL-S Shared ontology Problems Hand-crafting annotations can be tedious Integrate legacy web services Our Solution Learn mappings to ontology! Andreas Heß ASSAM

  8. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Semantic Metadata Assumptions Semantic annotation, e.g. OWL-S Shared ontology Problems Hand-crafting annotations can be tedious Integrate legacy web services Our Solution Learn mappings to ontology! Andreas Heß ASSAM

  9. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion What is learned? Learn from: Web Service as a whole Operations Parameters Features used: Names Operations Parameters Web Service Comments (if available) Andreas Heß ASSAM

  10. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Outline Introduction 1 Motivation Progress made ASSAM Annotator 2 The Annotator Application Relational Learning Evaluation OATS 3 Conclusion 4 Andreas Heß ASSAM

  11. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Progress Earlier Now Classifying Services Classifying Services, Operations, Datatypes Ensemble Learning Relational Learning Application that can export OWL-S New OATS algorithm for output aggregation Andreas Heß ASSAM

  12. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Progress Earlier Now Classifying Services Classifying Services, Operations, Datatypes Ensemble Learning Relational Learning Application that can export OWL-S New OATS algorithm for output aggregation Andreas Heß ASSAM

  13. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Progress Earlier Now Classifying Services Classifying Services, Operations, Datatypes Ensemble Learning Relational Learning Application that can export OWL-S New OATS algorithm for output aggregation Andreas Heß ASSAM

  14. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Progress Earlier Now Classifying Services Classifying Services, Operations, Datatypes Ensemble Learning Relational Learning Application that can export OWL-S New OATS algorithm for output aggregation Andreas Heß ASSAM

  15. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Progress Earlier Now Classifying Services Classifying Services, Operations, Datatypes Ensemble Learning Relational Learning Application that can export OWL-S New OATS algorithm for output aggregation Andreas Heß ASSAM

  16. Outline Introduction Motivation ASSAM Annotator Progress made OATS Conclusion Progress Earlier Now Classifying Services Classifying Services, Operations, Datatypes Ensemble Learning Relational Learning Application that can export OWL-S New OATS algorithm for output aggregation Andreas Heß ASSAM

  17. Outline Introduction The Annotator Application ASSAM Annotator Relational Learning OATS Evaluation Conclusion Outline Introduction 1 Motivation Progress made ASSAM Annotator 2 The Annotator Application Relational Learning Evaluation OATS 3 Conclusion 4 Andreas Heß ASSAM

  18. Outline Introduction The Annotator Application ASSAM Annotator Relational Learning OATS Evaluation Conclusion Outline Introduction 1 Motivation Progress made ASSAM Annotator 2 The Annotator Application Relational Learning Evaluation OATS 3 Conclusion 4 Andreas Heß ASSAM

  19. Outline Introduction The Annotator Application ASSAM Annotator Relational Learning OATS Evaluation Conclusion Our Application Andreas Heß ASSAM

  20. Outline Introduction The Annotator Application ASSAM Annotator Relational Learning OATS Evaluation Conclusion Our Application +⇧⌃⌃ A/⌃= ⌘⇤ ⌃⇧A/✏⌃ (⌥⌧⌃⇢⌘⇧- *⌘6⌥/⇤L*⌥⌧⌥⌧-%⌃ ⌘⇤⌧⌘✓⌘⇢- ⌘⇤⌧⌘✓⌘⇢/⌃ *⌘✏6⌃⇤⌧⌥⌧/⌘⇤ <⌘⇤⌅ /⇤ ?>*⇡ ⇡/⌧ ⌘< =⌃@ ⌃⇧A/✏⌃ 2✓⌥/⇤ ?>*⇡ A/⌃= Andreas Heß ASSAM

  21. Outline Introduction The Annotator Application ASSAM Annotator Relational Learning OATS Evaluation Conclusion ASSAM’s Key Feature 3⌃✏⌘66⌃⇤⌅⌃⌅ ⌥⇤⇤⌘⌧⌥⌧/⌘⇤ Andreas Heß ASSAM

  22. Outline Introduction The Annotator Application ASSAM Annotator Relational Learning OATS Evaluation Conclusion OWL-S Export !=⌦⇥6>↵?>=⇧⇤↵⌅⇠6◆⇢6◆*✓⌥=✓*⌥⇢5 !=⌦⇥6>↵?>=⇧⇤↵⌅*✓⌥=✓#⌥⌦◆5 !-↵⇧⌥>!./
 ⌦↵,⇧0⌥⌅6✓✏⇣1◆✓2⇤↵⌅3"4⇥↵!⇣;5 !;=⌦⇥6>↵?>=⇧⇤↵⌅*✓⌥=✓#⌥⌦◆5 !=⌦⇥6>↵?>=⇧-⌅◆$⌦⌥>,⇥⌦⌧⌥◆?⇥> ⌦↵,⇧⇢⌥⌦✓$!⇢✓✏⇣+?◆✓⌦⌥⌅⇣5 !-⌅⇧◆!⌅✓✓✓◆ 0✓⌦?⇥>✏⇣(@'⇣ -⌧⌅>⇧-⌅✏⇣◆◆⇢⇧;;⇤⇤⇤@⇤%@⇥⌦=;(555;),+;$⌦⌥>,⇥⌦⌧⇣5 !⇥⇤⌅⇧⌃⌅⌥ ⌦↵,⇧
.✏⇣⌘✓⌥◆✓⌦⇣5 !-⌅⇧⇥6◆⇢6◆ ⌧✓◆⇥↵✏⇣-⌧⌅⇣ ?>↵✓>◆✏⇣!✓⇣;5 !⌦↵,⇧6⌫⌃⌅⌥⇠, ⌦↵,⇧⌦✓⇥6⌦⇡✓✏⇣◆◆⇢⇧;;⌧⇥=6>◆?⌥@6⇡↵@?✓;⇥⇤⌅;.⌥◆⌥◆!⇢✓@⇥⇤⌅"⌘✓⌥◆✓⌦⇣;5 !-⌅⇧◆✓⌧⇢⌅⌥◆✓ ⌧⌥◆⇡✏⇣;⇣5 !;⇥⇤⌅⇧⌃⌅⌥5 !⌦↵,⇧/.6 -⌧⌅>⇧⌦↵,✏⇣◆◆⇢⇧;;⇤⇤⇤@⇤%@⇥⌦=;(555;'&;&&7⌦↵,7!>◆⌥-7>⇣ !⇥⇤⌅⇧.⌥◆⌥◆!⇢✓#⌦⇥⇢✓⌦◆! ⌦↵,⇧
.✏⇣$?⌧✓⇣5 -⌧⌅>⇧◆✓2⇡⇥>⇡✓⇢◆✏⇣1◆✓2⇡⇥>⇡✓⇢◆3⇣ !⌦↵,⇧⌦⌥>=✓ ⌦↵,⇧⌦✓⇥6⌦⇡✓✏⇣◆◆⇢⇧;;⇤⇤⇤@⇤%@⇥⌦=;&''(;)*+,⇡✓⌧⌥"◆⌦?>=⇣;5 -⌧⌅>⇧◆✓2⇢⌦⇥⇡✓✏⇣1◆✓2⇢⌦⇥⇡✓3⇣5 !⌦↵,⇧↵⇥⌧⌥?> ⌦↵,⇧⌦✓⇥6⌦⇡✓✏⇣"⌘✓⌥◆✓⌦⇣;5 !◆✓2⇡⇥>⇡✓⇢◆⇧⌘✓⌥◆✓⌦5 !⌦↵,⇧6⌫#⌦⇥⇢✓⌦◆!⇠, ⌦↵,⇧⌦✓⇥6⌦⇡✓✏⇣◆◆⇢⇧;;⌧⇥=6>◆?⌥@6⇡↵@?✓;⇥⇤⌅;.⌥◆⌥◆!⇢✓@⇥⇤⌅"$?⌧✓⇣;5 !◆✓2⇡⇥>⇡✓⇢◆⇧$?⌧✓5 !;⇥⇤⌅⇧.⌥◆⌥◆!⇢✓#⌦⇥⇢✓⌦◆!5 !-⌅⇧0⌥⌅6✓7⇥, ✓⌅✓⇡◆✏⇣4⇥↵!;$?⌧✓⇣;5 !⇥⇤⌅⇧.⌥◆⌥◆!⇢✓#⌦⇥⇢✓⌦◆! ⌦↵,⇧
.✏⇣$✓⌧⇢✓⌦⌥◆6⌦✓⇣5 !;◆✓2⇡⇥>⇡✓⇢◆⇧$?⌧✓5 !⌦↵,⇧⌦⌥>=✓ ⌦↵,⇧⌦✓⇥6⌦⇡✓✏⇣◆◆⇢⇧;;⇤⇤⇤@⇤%@⇥⌦=;&''(;)*+,⇡✓⌧⌥"◆⌦?>=⇣;5 !⌦↵,⇧↵⇥⌧⌥?> ⌦↵,⇧⌦✓⇥6⌦⇡✓✏⇣"⌘✓⌥◆✓⌦⇣;5 !⌦↵,⇧6⌫#⌦⇥⇢✓⌦◆!⇠, ⌦↵,⇧⌦✓⇥6⌦⇡✓✏⇣◆◆⇢⇧;;⌧⇥=6>◆?⌥@6⇡↵@?✓;⇥⇤⌅;.⌥◆⌥◆!⇢✓@⇥⇤⌅"$✓⌧⇢✓⌦⌥◆6⌦✓⇣;5 !;⇥⇤⌅⇧.⌥◆⌥◆!⇢✓#⌦⇥⇢✓⌦◆!5 Andreas Heß ASSAM

  23. Outline Introduction The Annotator Application ASSAM Annotator Relational Learning OATS Evaluation Conclusion OWL-S Export ASSAM generates... Grounding Profile Process Model Concepts (like WSDL2DAML-S by Paolucci, Sycara et al.) Andreas Heß ASSAM

Recommend

Machine Learning for Annotating Semantic Web Services Andreas He, Nicholas Kushmerick

Machine Learning for Annotating Semantic Web Services Andreas He, Nicholas Kushmerick

Machine Learning for Annotating Semantic Web Services Machine Learning for Annotating Semantic Web Services Machine Learning for Annotating Semantic Web Services Andreas He, Nicholas Kushmerick University College Dublin, Ireland

747 views • 56 slides
Automatically Annotating Text with Linked Open Data Delia Rusu , Bla Fortuna, Dunja Mladeni

Automatically Annotating Text with Linked Open Data Delia Rusu , Bla Fortuna, Dunja Mladeni

Automatically Annotating Text with Linked Open Data Delia Rusu , Bla Fortuna, Dunja Mladeni Joef Stefan Institute ailab.ijs.si Motivation: Annotating Text with LOD DBpedia Open Cyc WordNet ailab.ijs.si Overview Related work

429 views • 17 slides
Semantic Taxonomies Semantic Class Learning from the Web Long-term goal: automatically create

Semantic Taxonomies Semantic Class Learning from the Web Long-term goal: automatically create

Semantic Taxonomies Semantic Class Learning from the Web Long-term goal: automatically create and populate a large- The Web can be viewed as an enormous text collection and scale semantic network by mining Web text. source for knowledge

304 views • 9 slides
Automatically Constructing Semantic Web Services from Online Sources Craig A. Knoblock Jos

Automatically Constructing Semantic Web Services from Online Sources Craig A. Knoblock Jos

Automatically Constructing Semantic Web Services from Online Sources Craig A. Knoblock Jos Luis Ambite, Sirish Darbha, Aman Goel, Kristina Lerman, Rahul Parundekar, and Tom Russ University Southern California Goal Automatically build

870 views • 38 slides
Users as Oracles: Semi-automatically Corroborating User Feedback Andy Podgurski (with Vinay

Users as Oracles: Semi-automatically Corroborating User Feedback Andy Podgurski (with Vinay

Users as Oracles: Semi-automatically Corroborating User Feedback Andy Podgurski (with Vinay Augustine) Electrical Eng. &amp; Computer Science Dept. Case Western Reserve University Cleveland, Ohio User Failure Reporting Semi-automatic

773 views • 29 slides
ASSAM, NATURENOMICS TM , CONSERVATION AND ROLE OF LEGISLATORS Ranjit Barthakur Founder Trustee,

ASSAM, NATURENOMICS TM , CONSERVATION AND ROLE OF LEGISLATORS Ranjit Barthakur Founder Trustee,

ASSAM, NATURENOMICS TM , CONSERVATION AND ROLE OF LEGISLATORS Ranjit Barthakur Founder Trustee, Balipara Foundation Assam Legislative Assembly, Guwahati 10 January, 2017 IN THIS PRESENTATION HISTORICAL CONTEXT OF ASSAM &amp; NORTH EAST INDIA

433 views • 29 slides
From Web 2.0 to Semantic Web A Semi-Automated Approach Andreas He, Christian Maa and Francis

From Web 2.0 to Semantic Web A Semi-Automated Approach Andreas He, Christian Maa and Francis

From Web 2.0 to Semantic Web A Semi-Automated Approach Andreas He, Christian Maa and Francis Dierick Lycos Europe 01/06/2008 | 1 Outline Motivation Proposals for better tagging Tag suggestion / semi-automated tagging Tag

478 views • 32 slides
Library S.B.M.S. College, Sualkuchi, Assam Library S.B.M.S. College, Sualkuchi, Assam Dist.:

Library S.B.M.S. College, Sualkuchi, Assam Library S.B.M.S. College, Sualkuchi, Assam Dist.:

0 Library S.B.M.S. College, Sualkuchi, Assam Library S.B.M.S. College, Sualkuchi, Assam Dist.: Kamrup, Pin: 781103 Phone: 0361-2830040. Email: librarian@dlsbmscollege.org Website: http://www.dlsbmscollege.org 1 Contents The Library Page 2

628 views • 14 slides
Annotating and Automatically Tagging Constructions of Causal Language What Google displays for

Annotating and Automatically Tagging Constructions of Causal Language What Google displays for

Annotating and Automatically Tagging Constructions of Causal Language What Google displays for why questions What Google displays for why questions What Google displays for why questions could be a lot more helpful. What

1.67k views • 145 slides
Contents Contents Introduction Tea Oil &amp; Gas Infrastructure Financials Conclusion

Contents Contents Introduction Tea Oil &amp; Gas Infrastructure Financials Conclusion

Contents Contents Introduction Tea Oil &amp; Gas Infrastructure Financials Conclusion Introduction Assam Company India Limited (Assam Co.) - Over the years Assam Co. Assam Co. is currently engaged in Tea, Oil &amp; Gas and

435 views • 16 slides
Automatically identifying changes in the semantic orientation of words Paul Cook and Suzanne

Automatically identifying changes in the semantic orientation of words Paul Cook and Suzanne

Automatically identifying changes in the semantic orientation of words Paul Cook and Suzanne Stevenson University of Toronto Amelioration and pejoration Changes in a word's meaning to have a more positive or negative evaluation

409 views • 20 slides
Semantic Categories using Automatically Acquired Symmetric Patterns Roy Schwartz + , Roi Reichart

Semantic Categories using Automatically Acquired Symmetric Patterns Roy Schwartz + , Roi Reichart

Minimally Supervised Classification to Semantic Categories using Automatically Acquired Symmetric Patterns Roy Schwartz + , Roi Reichart * and Ari Rappoport + + The Hebrew University, * Technion IIT COLING 2014

586 views • 41 slides
Workflow Plus Lines View Tool Features Automatically creates SQL Views from Requests using

Workflow Plus Lines View Tool Features Automatically creates SQL Views from Requests using

Workflow Plus Lines View Tool Features Automatically creates SQL Views from Requests using this functionality An XML file is created when using Lines in a request. This tool makes it easier for reporting and data extraction Generate

406 views • 12 slides
Semi-supervised Semantic Role Labeling Hagen Frstenau Department of Computational Linguistics

Semi-supervised Semantic Role Labeling Hagen Frstenau Department of Computational Linguistics

Frame Semantics A Semi-supervised approach to role labeling Summary Semi-supervised Semantic Role Labeling Hagen Frstenau Department of Computational Linguistics Saarland University (joint work with Mirella Lapata) FEAST meeting

594 views • 37 slides
Space Traveling across VM Automatically Bridging the Semantic-Gap in Virtual Machine

Space Traveling across VM Automatically Bridging the Semantic-Gap in Virtual Machine

Background and The Problem State-of-the-Art Our Approach: Data Space Traveling Conclusion Space Traveling across VM Automatically Bridging the Semantic-Gap in Virtual Machine Introspection via Online Kernel Data Redirection Yangchun Fu, and

926 views • 66 slides
Budget-aware Semi-Supervised Semantic and Instance Segmentation Miriam Bellver, Amaia Salvador,

Budget-aware Semi-Supervised Semantic and Instance Segmentation Miriam Bellver, Amaia Salvador,

Budget-aware Semi-Supervised Semantic and Instance Segmentation Miriam Bellver, Amaia Salvador, Jordi Torres, Xavier Giro-i-Nieto Women In Computer Vision - CVPR 2019 Motivation Semantic segmentation Instance segmentation Pixel-level

282 views • 15 slides
(Semi-)Automatic Normalization of Historical Texts using Distance Measures and the Norma tool

(Semi-)Automatic Normalization of Historical Texts using Distance Measures and the Norma tool

Corpora Methods Norma Tool Evaluation (Semi-)Automatic Normalization of Historical Texts using Distance Measures and the Norma tool Marcel Bollmann Department of Linguistics Ruhr-University Bochum, Germany Second Workshop on Annotation of

626 views • 35 slides
Structure History of Semantic Roles 1. Contemporary Frameworks 2. Difficult Phenomena (from an

Structure History of Semantic Roles 1. Contemporary Frameworks 2. Difficult Phenomena (from an

Annotating semantic roles: phenomena Katrin Erk Sebastian Pado ESSLLI 2006 Structure History of Semantic Roles 1. Contemporary Frameworks 2. Difficult Phenomena (from an 3. empirical perspective) Role Semantics vs. Formal Semantics 4.

637 views • 18 slides
Logic as a Tool Chapter 4: Deductive Reasoning in First-Order Logic 4.2 Semantic Tableaux for

Logic as a Tool Chapter 4: Deductive Reasoning in First-Order Logic 4.2 Semantic Tableaux for

Logic as a Tool Chapter 4: Deductive Reasoning in First-Order Logic 4.2 Semantic Tableaux for First-Order Logic Valentin Goranko Stockholm University December 2020 Goranko First-order Semantic Tableau: introduction The system of semantic

542 views • 19 slides
A&quot;semi'automatic&quot;web&quot;based&quot;tool

A&quot;semi'automatic&quot;web&quot;based&quot;tool

A&quot;semi'automatic&quot;web&quot;based&quot;tool for&quot;the&quot;selection&quot;of&quot;research ! projects&quot;reviewers Valeria!PUPELLA!a,!Maria!Eugenia!MONTEVERDE!a,!Claudio!LOMBARDO!b, Filippo!BELARDELLI!c!and!Mauro!GIACOMINI!a

422 views • 19 slides
CDE: A tool for automatically creating reproducible experimental software packages Philip Guo 6

CDE: A tool for automatically creating reproducible experimental software packages Philip Guo 6

CDE: A tool for automatically creating reproducible experimental software packages Philip Guo 6 th -year Ph.D. student Department of Computer Science Stanford University pg@cs.stanford.edu http://www.stanford.edu/~pgbovine/cde.html Barriers

452 views • 23 slides
Annotating Expressions of Opinion and Emotion in the Italian Content Annotation Bank (I-CAB)

Annotating Expressions of Opinion and Emotion in the Italian Content Annotation Bank (I-CAB)

About I-CAB Annotating private states Inter-annotator agreement (IAA) Conclusion: problems with the markup language Annotating Expressions of Opinion and Emotion in the Italian Content Annotation Bank (I-CAB) Andrea Esuli, Fabrizio Sebastiani

608 views • 21 slides
Compositional Semantic Parsing on Semi-Structured Tables Ice Pasupat and Percy Liang Stanford

Compositional Semantic Parsing on Semi-Structured Tables Ice Pasupat and Percy Liang Stanford

Compositional Semantic Parsing on Semi-Structured Tables Ice Pasupat and Percy Liang Stanford University ACL 2015 Tuesday, July 28, 2015 Task Question answering given a knowledge source In which city was Ada Lovelace born? Database 2

1.17k views • 84 slides
SEMANTIC SEARCH 3000 LSA-BASED RESEARCH TOOL FOR INFORMATION AND DOCUMENT RETRIEVAL TERRANCE

SEMANTIC SEARCH 3000 LSA-BASED RESEARCH TOOL FOR INFORMATION AND DOCUMENT RETRIEVAL TERRANCE

SEMANTIC SEARCH 3000 LSA-BASED RESEARCH TOOL FOR INFORMATION AND DOCUMENT RETRIEVAL TERRANCE TAUBES (T14) PROFESSOR BENAJIBA CSCI 3907/6907 INTRO TO STATISTICAL NLP GOAL: To design a research tool that allows students to find the relevant

620 views • 28 slides

More recommend