Date: June 14, 2016
Venue: Oslo, Norway. Doctoral Seminar at HiOA
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: June 10, 2016
Venue: Stavanger, Norway. Doctoral Seminar at the IAI group for the research visit of Prof. Kalervo Järvelin
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: October 2nd, 2017
Venue: Amsterdam, The Netherlands. The 2017 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR '17)
Corresponding article: https://arxiv.org/abs/1708.08291
Please cite, link to or credit this presentation when using it or part of it in your work.
The document discusses ontology engineering and provides details about:
1. Ontology engineering is the process of developing ontologies for a particular domain by defining concepts, arranging them hierarchically, and defining their properties and relationships.
2. Ontology engineering is analogous to object-oriented database design but ontologies reflect the structure of the world using open world assumptions.
3. Popular ontology engineering tools include Protégé, which supports ontology development and knowledge modeling.
The document discusses ontologies, including:
1) It defines ontologies as formal specifications of concepts and relationships that can exist for an agent or community. Ontologies allow knowledge to be shared and reused.
2) Ontologies can be used to facilitate knowledge management, enable learning about a domain, and enable intelligent search and query expansion.
3) The document provides guidance on developing ontologies, including researching the domain, using existing resources, defining classes and properties, and choosing an ontology language.
Date: March 3rd, 2016
Venue: Trondheim, Norway. Doctoral Seminar at NTNU
Please cite, link to or credit this presentation when using it or part of it in your work.
Lect6-An introduction to ontologies and ontology developmentAntonio Moreno
The document provides an overview of ontologies and ontology development:
1. It defines ontologies as explicit specifications of conceptualizations in a domain that define concepts, properties, attributes, and relationships to enable knowledge sharing.
2. Ontology components include concepts, properties, restrictions, and individuals. Ontologies can range from single large ontologies to several specialized smaller ones.
3. OWL is introduced as the standard language for representing ontologies, with features like classes, properties, restrictions, and logical operators.
4. A general methodology for ontology development is outlined, including determining scope, reusing existing ontologies, enumerating terms, and defining classes, properties, and other components in an iterative
A semantic application is proposed for healthcare to integrate information from various sources in a structured way. The application would allow querying relationships between entities like care takers, patients, medical conditions, locations and professionals. An ontology would be developed to define classes, properties and constraints. RDF and OWL would be used to represent metadata and SPARQL would enable querying the semantic graph. The goals are to more easily find healthcare resources, store institutional knowledge, generate new insights from data, and improve clinical research capabilities.
Date: June 10, 2016
Venue: Stavanger, Norway. Doctoral Seminar at the IAI group for the research visit of Prof. Kalervo Järvelin
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: October 2nd, 2017
Venue: Amsterdam, The Netherlands. The 2017 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR '17)
Corresponding article: https://arxiv.org/abs/1708.08291
Please cite, link to or credit this presentation when using it or part of it in your work.
The document discusses ontology engineering and provides details about:
1. Ontology engineering is the process of developing ontologies for a particular domain by defining concepts, arranging them hierarchically, and defining their properties and relationships.
2. Ontology engineering is analogous to object-oriented database design but ontologies reflect the structure of the world using open world assumptions.
3. Popular ontology engineering tools include Protégé, which supports ontology development and knowledge modeling.
The document discusses ontologies, including:
1) It defines ontologies as formal specifications of concepts and relationships that can exist for an agent or community. Ontologies allow knowledge to be shared and reused.
2) Ontologies can be used to facilitate knowledge management, enable learning about a domain, and enable intelligent search and query expansion.
3) The document provides guidance on developing ontologies, including researching the domain, using existing resources, defining classes and properties, and choosing an ontology language.
Date: March 3rd, 2016
Venue: Trondheim, Norway. Doctoral Seminar at NTNU
Please cite, link to or credit this presentation when using it or part of it in your work.
Lect6-An introduction to ontologies and ontology developmentAntonio Moreno
The document provides an overview of ontologies and ontology development:
1. It defines ontologies as explicit specifications of conceptualizations in a domain that define concepts, properties, attributes, and relationships to enable knowledge sharing.
2. Ontology components include concepts, properties, restrictions, and individuals. Ontologies can range from single large ontologies to several specialized smaller ones.
3. OWL is introduced as the standard language for representing ontologies, with features like classes, properties, restrictions, and logical operators.
4. A general methodology for ontology development is outlined, including determining scope, reusing existing ontologies, enumerating terms, and defining classes, properties, and other components in an iterative
A semantic application is proposed for healthcare to integrate information from various sources in a structured way. The application would allow querying relationships between entities like care takers, patients, medical conditions, locations and professionals. An ontology would be developed to define classes, properties and constraints. RDF and OWL would be used to represent metadata and SPARQL would enable querying the semantic graph. The goals are to more easily find healthcare resources, store institutional knowledge, generate new insights from data, and improve clinical research capabilities.
Date: October 7, 2016
Venue: Stavanger, Norway. Technical talk at UiS TN-IDE
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: March 13, 2017
Venue: Stavanger, Norway. Doctoral Seminar at the IAI group for the research visit of Prof. Maarten de Rijke
Please cite, link to or credit this presentation when using it or part of it in your work.
This is Part II of the tutorial "Entity Linking and Retrieval" given at SIGIR 2013 (together with E. Meij and D. Odijk). For the complete tutorial material (including slides for the other parts) visit http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
Date: August 2016
Venue: Saratov, Russian Federation. The 10th Russian Summer School in Information Retrieval (RuSSIR '16)
Please cite, link to or credit this presentation when using it or part of it in your work.
The document introduces ontology and describes what it is from both philosophical and computer science perspectives. An ontology in computers consists of a vocabulary to describe a domain, specifications of the meaning of terms, and constraints capturing additional knowledge about the domain. It then provides an example ontology and discusses applications of ontologies such as for the semantic web. It also discusses important considerations for building ontologies such as collaboration, versioning, and ease of use.
A set of ideas on the use of artificial intelligence for data curation that has been presented at the Pharma-IT conference (London, 2017), in the artificial intelligence track.
It begins with some broad discussion about semantic web, knowledge representation, machine learning and artificial intelligence. It the focus on how a "data curation" problem can be framed and hints at some possible examples.
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We will show how to plug Linked Open Data in a recommendation engine in order to build a new generation of LOD-enabled applications.
(Lecture given @ the 11th Reasoning Web Summer School - Berlin - August 1, 2015)
Issues and activities in authoring ontologiesrobertstevens65
The document discusses issues in authoring ontologies and describes a study conducted to better understand the ontology authoring process. The study used an instrumented version of Protégé called Protégé4US to collect interaction logs and eye tracking data from ontology authors. Analysis of the data revealed common patterns of exploration, editing, and reasoning activities. Key findings include the repetitive nature of editing tasks and lack of situational awareness after running reasoning. Design recommendations aim to better support activities like bulk editing and anticipating the effects of reasoning.
A Survey Ondecision Tree Learning Algorithms for Knowledge DiscoveryIJERA Editor
Theimmense volumes of data are populated into repositories from various applications. In order to find out desired information and knowledge from large datasets, the data mining techniques are very much helpful. Classification is one of the knowledge discovery techniques. In Classification, Decision trees are very popular in research community due to simplicity and easy comprehensibility. This paper presentsan updated review of recent developments in the field of decision trees.
Franz et al 2015 escjam 2015 logic resolution taxonomic variabletaxonbytes
This document discusses using logic and computational tools to resolve conflicts and ambiguities that arise from taxonomic changes and evolving classifications over time. It presents the Euler/X toolkit, which takes taxonomic classifications as input, identifies relationships between concepts, and outputs aligned representations to help integrate conflicting information in a logically consistent manner. This approach aims to make taxonomic evolution more tractable for computational use, though it requires significant effort and there are tradeoffs to consider regarding complexity versus benefit.
This document summarizes research on building a serendipitous search system based on enriched entity networks extracted from Wikipedia and Yahoo Answers. It describes extracting entities and relationships between them to build entity networks. It then details using a random walk retrieval algorithm and rank aggregation to perform searches across the networks. The researchers analyze the system's precision, MAP, and ability to provide unexpected yet relevant results. User studies found the combined system provided more relevant, interesting, and informative results compared to using Wikipedia or Yahoo Answers individually. Metadata like sentiment, readability and categories was added to entity networks to help promote serendipity.
Pharo: a reflective language A first systematic analysis of reflective APIsESUG
This document analyzes the reflective features and APIs in Pharo, a reflective programming language. It presents a catalog of Pharo's reflective APIs and analyzes how they relate to metaobjects. The analysis highlights areas for potential improvement, such as providing solutions for intercession on state reads/writes and addressing constraints when changing an object's class. The document contributes to understanding Pharo's reflective design and its evolution over time.
Gleaning Types for Literals in RDF with Application to Entity SummarizationKalpa Gunaratna
ESWC 2016 talk about how to compute types (ontology classes) for literals and add semantics to them, making them richer. Then utilize them in an entity summarization usecase.
Global Collection Dashboard – Using data we have to uncover data we don’tAxiell ALM
This document discusses the development of a global collections dashboard to provide summaries of digitized collection data from multiple institutions. It describes the types of data that can be analyzed, such as collection records with geographic, taxonomic, and object data. Methods for analyzing and visualizing the data like completeness rankings, searches, and comparisons between institutions are presented. The goal is to make more collection data accessible and help prioritize digitization efforts.
Improving Semantic Search Using Query Log AnalysisStuart Wrigley
Despite the attention Semantic Search is continuously gaining, several challenges affecting tool performance and user experience remain unsolved. Among these are: matching user terms with the searchspace, adopting view-based interfaces in the Open Web as well as supporting users while building their queries. This paper proposes an approach to move a step forward towards tackling these challenges by creating models of usage of Linked Data concepts and properties extracted from semantic query logs as a source of collaborative knowledge. We use two sets of query logs from the USEWOD workshops to create our models and show the potential of using them in the mentioned areas.
This document discusses key concepts of object-oriented programming including:
- Objects have state (fields) and behavior (methods) like real-world objects.
- Classes are used to group similar objects and define common attributes and behaviors.
- Inheritance allows subclasses to inherit attributes and behaviors from parent classes.
- Polymorphism allows classes to share a common interface while having different implementations.
- Abstract classes define common functionality and interfaces for subclasses to implement.
This document summarizes a research paper that proposes a novel approach for discovering rare and non-present item-sets from transactional databases using an adaptation of the Apriori algorithm. The paper first defines rare and non-present item-sets and provides an example dataset. It then reviews related work in frequent item-set mining and rare item-set mining. The paper proposes a generalized framework for pattern mining and describes how it can be instantiated to develop an Apriori-based algorithm called ARANIM for discovering rare and non-present item-sets.
Digital Object Identifiers (DOIs) in the context of the International TreatyFAO
http://tiny.cc/faowgsworkshop
FAO's activities relevant to genome sequencing- International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA). Presentation from the FAO expert workshop on practical applications of Whole Genome Sequencing (WGS) for food safety management - 7-8 December 2015, Rome, Italy.
Date: March 22, 2019
Venue: Stavanger, Norway. Symposium at the IAI group
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: October 7, 2016
Venue: Stavanger, Norway. Technical talk at UiS TN-IDE
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: March 13, 2017
Venue: Stavanger, Norway. Doctoral Seminar at the IAI group for the research visit of Prof. Maarten de Rijke
Please cite, link to or credit this presentation when using it or part of it in your work.
This is Part II of the tutorial "Entity Linking and Retrieval" given at SIGIR 2013 (together with E. Meij and D. Odijk). For the complete tutorial material (including slides for the other parts) visit http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
Date: August 2016
Venue: Saratov, Russian Federation. The 10th Russian Summer School in Information Retrieval (RuSSIR '16)
Please cite, link to or credit this presentation when using it or part of it in your work.
The document introduces ontology and describes what it is from both philosophical and computer science perspectives. An ontology in computers consists of a vocabulary to describe a domain, specifications of the meaning of terms, and constraints capturing additional knowledge about the domain. It then provides an example ontology and discusses applications of ontologies such as for the semantic web. It also discusses important considerations for building ontologies such as collaboration, versioning, and ease of use.
A set of ideas on the use of artificial intelligence for data curation that has been presented at the Pharma-IT conference (London, 2017), in the artificial intelligence track.
It begins with some broad discussion about semantic web, knowledge representation, machine learning and artificial intelligence. It the focus on how a "data curation" problem can be framed and hints at some possible examples.
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We will show how to plug Linked Open Data in a recommendation engine in order to build a new generation of LOD-enabled applications.
(Lecture given @ the 11th Reasoning Web Summer School - Berlin - August 1, 2015)
Issues and activities in authoring ontologiesrobertstevens65
The document discusses issues in authoring ontologies and describes a study conducted to better understand the ontology authoring process. The study used an instrumented version of Protégé called Protégé4US to collect interaction logs and eye tracking data from ontology authors. Analysis of the data revealed common patterns of exploration, editing, and reasoning activities. Key findings include the repetitive nature of editing tasks and lack of situational awareness after running reasoning. Design recommendations aim to better support activities like bulk editing and anticipating the effects of reasoning.
A Survey Ondecision Tree Learning Algorithms for Knowledge DiscoveryIJERA Editor
Theimmense volumes of data are populated into repositories from various applications. In order to find out desired information and knowledge from large datasets, the data mining techniques are very much helpful. Classification is one of the knowledge discovery techniques. In Classification, Decision trees are very popular in research community due to simplicity and easy comprehensibility. This paper presentsan updated review of recent developments in the field of decision trees.
Franz et al 2015 escjam 2015 logic resolution taxonomic variabletaxonbytes
This document discusses using logic and computational tools to resolve conflicts and ambiguities that arise from taxonomic changes and evolving classifications over time. It presents the Euler/X toolkit, which takes taxonomic classifications as input, identifies relationships between concepts, and outputs aligned representations to help integrate conflicting information in a logically consistent manner. This approach aims to make taxonomic evolution more tractable for computational use, though it requires significant effort and there are tradeoffs to consider regarding complexity versus benefit.
This document summarizes research on building a serendipitous search system based on enriched entity networks extracted from Wikipedia and Yahoo Answers. It describes extracting entities and relationships between them to build entity networks. It then details using a random walk retrieval algorithm and rank aggregation to perform searches across the networks. The researchers analyze the system's precision, MAP, and ability to provide unexpected yet relevant results. User studies found the combined system provided more relevant, interesting, and informative results compared to using Wikipedia or Yahoo Answers individually. Metadata like sentiment, readability and categories was added to entity networks to help promote serendipity.
Pharo: a reflective language A first systematic analysis of reflective APIsESUG
This document analyzes the reflective features and APIs in Pharo, a reflective programming language. It presents a catalog of Pharo's reflective APIs and analyzes how they relate to metaobjects. The analysis highlights areas for potential improvement, such as providing solutions for intercession on state reads/writes and addressing constraints when changing an object's class. The document contributes to understanding Pharo's reflective design and its evolution over time.
Gleaning Types for Literals in RDF with Application to Entity SummarizationKalpa Gunaratna
ESWC 2016 talk about how to compute types (ontology classes) for literals and add semantics to them, making them richer. Then utilize them in an entity summarization usecase.
Global Collection Dashboard – Using data we have to uncover data we don’tAxiell ALM
This document discusses the development of a global collections dashboard to provide summaries of digitized collection data from multiple institutions. It describes the types of data that can be analyzed, such as collection records with geographic, taxonomic, and object data. Methods for analyzing and visualizing the data like completeness rankings, searches, and comparisons between institutions are presented. The goal is to make more collection data accessible and help prioritize digitization efforts.
Improving Semantic Search Using Query Log AnalysisStuart Wrigley
Despite the attention Semantic Search is continuously gaining, several challenges affecting tool performance and user experience remain unsolved. Among these are: matching user terms with the searchspace, adopting view-based interfaces in the Open Web as well as supporting users while building their queries. This paper proposes an approach to move a step forward towards tackling these challenges by creating models of usage of Linked Data concepts and properties extracted from semantic query logs as a source of collaborative knowledge. We use two sets of query logs from the USEWOD workshops to create our models and show the potential of using them in the mentioned areas.
This document discusses key concepts of object-oriented programming including:
- Objects have state (fields) and behavior (methods) like real-world objects.
- Classes are used to group similar objects and define common attributes and behaviors.
- Inheritance allows subclasses to inherit attributes and behaviors from parent classes.
- Polymorphism allows classes to share a common interface while having different implementations.
- Abstract classes define common functionality and interfaces for subclasses to implement.
This document summarizes a research paper that proposes a novel approach for discovering rare and non-present item-sets from transactional databases using an adaptation of the Apriori algorithm. The paper first defines rare and non-present item-sets and provides an example dataset. It then reviews related work in frequent item-set mining and rare item-set mining. The paper proposes a generalized framework for pattern mining and describes how it can be instantiated to develop an Apriori-based algorithm called ARANIM for discovering rare and non-present item-sets.
Digital Object Identifiers (DOIs) in the context of the International TreatyFAO
http://tiny.cc/faowgsworkshop
FAO's activities relevant to genome sequencing- International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA). Presentation from the FAO expert workshop on practical applications of Whole Genome Sequencing (WGS) for food safety management - 7-8 December 2015, Rome, Italy.
Date: March 22, 2019
Venue: Stavanger, Norway. Symposium at the IAI group
Please cite, link to or credit this presentation when using it or part of it in your work.
About "Towards Better Text Understanding and Retrieval through Kernel Entity ...Darío Garigliotti
Summary of the paper "Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling", presented at SIGIR 2018.
Date: October 17, 2018
Venue: London, UK. Reading group
Please cite, link to or credit this presentation when using it or part of it in your work.
A Semantic Search Approach to Task-Completion EnginesDarío Garigliotti
Date: July 8, 2018
Venue: Ann Arbor, MI, USA. Doctoral Consortium at the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '18)
Please cite, link to or credit this presentation when using it or part of it in your work.
Highlights of the 40th European Conference on Information Retrieval (ECIR '18)
Date: April 6, 2018
Venue: Stavanger, Norway. Symposium at the IAI group
Please cite, link to or credit this presentation when using it or part of it in your work.
A Semantic Search Approach to Task-Completion EnginesDarío Garigliotti
Date: February 27, 2018
Venue: Stavanger, Norway. UiS TN910 - Innovation and Project Awareness
Please cite, link to or credit this presentation when using it or part of it in your work.
This document summarizes Darío Garigliotti's work on constructing a knowledge base of entity-oriented search intents. It introduces key concepts like entities, entity types, RDF tuples, and knowledge bases. It then describes a pipeline approach for building the knowledge base, which involves acquiring refiners from queries, categorizing refiners, discovering intents, and constructing the knowledge base with triples linking intents to entities, categories, and expressing refiners. Evaluation is done on the accuracy of the extracted knowledge base facts. The full knowledge base contains 155k triples describing 31k intent profiles across 581 entity types. Potential applications include leveraging the knowledge base to identify intents in new queries and improving entity cards.
Learning-to-Rank Target Types for Entity-Bearing QueriesDarío Garigliotti
Date: October 1st, 2017
Venue: Amsterdam, The Netherlands. LEARNER 2017, co-located with the 2017 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR '17)
Corresponding article: http://ceur-ws.org/Vol-2007/LEARNER2017_short_3.pdf
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: March 4, 2016
Venue: Trondheim, Norway. Doctoral Seminar at NTNU
Please cite, link to or credit this presentation when using it or part of it in your work.
Original title in Spanish: Si ésta es la respuesta, ¿cuál era la pregunta?
Date: November 20, 2013
Venue: Córdoba, Argentina. Project on Question Generation for the MSc Specialization Course "Natural Language Processing" (Faculty of Mathematics, Astronomy, Physics and Computation, National University of Córdoba)
Please cite, link to or credit this presentation when using it or part of it in your work.
Semi-supervised Learning for Word Sense DisambiguationDarío Garigliotti
Original title in Spanish: Desambiguación de Palabras Polisémicas mediante Aprendizaje Semi-supervisado
Date: September 20, 2013
Venue: Córdoba, Argentina. 42nd JAIIO - Argentine Journals of Informatics and Operating Research (JAIIO '13)
Please cite, link to or credit this presentation when using it or part of it in your work.
Semi-supervised Learning for Word Sense DisambiguationDarío Garigliotti
Original title in Spanish: Desambiguación de Palabras Polisémicas mediante Aprendizaje Semi-supervisado
Date: November 19, 2012
Venue: Córdoba, Argentina. Project on Word Sense Disambiguation for the MSc Specialization Course "Artificial Intelligence" at FaMAF, UNC (Faculty of Mathematics, Astronomy, Physics and Computation, National University of Córdoba)
Video: https://www.youtube.com/watch?v=qv9qZaBw-Qw
Semi-supervised Learning for Word Sense DisambiguationDarío Garigliotti
Original title in Spanish: Desambiguación de Palabras Polisémicas mediante Aprendizaje Semi-supervisado
Date: September 2013
Venue: Córdoba, Argentina. 42nd JAIIO - Argentine Journals of Informatics and Operating Research (JAIIO '13)
Corresponding article: https://arxiv.org/abs/1908.09641
Please cite the paper, and link to or credit this presentation when using it or part of it in your work.
Hierarchical clustering builds clusters hierarchically, by either merging or splitting clusters at each step. Agglomerative hierarchical clustering starts with each point as a separate cluster and successively merges the closest clusters based on a defined proximity measure between clusters. This results in a dendrogram showing the nested clustering structure. The basic algorithm computes a proximity matrix, then repeatedly merges the closest pair of clusters and updates the matrix until all points are in one cluster.
The document discusses several alternative classification techniques including rule-based classifiers, nearest neighbors classifiers, and Naive Bayes classifiers. It provides examples of how each technique works and some key aspects to consider, such as how to build rule-based classifiers directly from data or indirectly from other models like decision trees. It also covers concepts like mutual exclusivity of rules, rule coverage and accuracy, and how to order rules.
Date: September 25, 2017
Course: UiS DAT630 - Web Search and Data Mining (fall 2017) (https://github.com/kbalog/uis-dat630-fall2017)
Presentation based on resources from the 2016 edition of the course (https://github.com/kbalog/uis-dat630-fall2016) and the resources shared by the authors of the book used through the course (https://www-users.cs.umn.edu/~kumar001/dmbook/index.php).
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: September 18, 2017
Course: UiS DAT630 - Web Search and Data Mining (fall 2017) (https://github.com/kbalog/uis-dat630-fall2017)
Presentation based on resources from the 2016 edition of the course (https://github.com/kbalog/uis-dat630-fall2016) and the resources shared by the authors of the book used through the course (https://www-users.cs.umn.edu/~kumar001/dmbook/index.php).
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: September 11, 2017
Course: UiS DAT630 - Web Search and Data Mining (fall 2017) (https://github.com/kbalog/uis-dat630-fall2017)
Presentation based on resources from the 2016 edition of the course (https://github.com/kbalog/uis-dat630-fall2016) and the resources shared by the authors of the book used through the course (https://www-users.cs.umn.edu/~kumar001/dmbook/index.php).
Please cite, link to or credit this presentation when using it or part of it in your work.
Date: March 9, 2016
Course: UiS DAT911 - Foundations of Computer Science (fall 2016)
Please cite, link to or credit this presentation when using it or part of it in your work.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
aziz sancar nobel prize winner: from mardin to nobel
Type-Aware Entity Retrieval
1. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type-aware Entity Retrieval
Dar´ıo Garigliotti
University of Stavanger
June 14, 2016
Dar´ıo Garigliotti Type-aware Entity Retrieval
2. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Outline:
1 Types and Entity Retrieval
2 Environment Dimensions
Type taxonomies
Type representations
Retrieval models
3 Type-aware Entity Retrieval
Dar´ıo Garigliotti Type-aware Entity Retrieval
3. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Types and Entity Retrieval
Traditional Information Retrieval recently extended to an
Entity-oriented Search
It revolves around the satisfaction of more complex
information needs
Several entity elements from knowledge bases, naturally
appearing in queries
Countries where one can pay with the euro
Related entities (via a relation or predicate)
Types or categories or classes
Dar´ıo Garigliotti Type-aware Entity Retrieval
4. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Types and Entity Retrieval
Dar´ıo Garigliotti Type-aware Entity Retrieval
5. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Types and Entity Retrieval
Dar´ıo Garigliotti Type-aware Entity Retrieval
6. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Types and Entity Retrieval
Why to think about types?
Entities are typed
Types are useful for retrieval, presentation,
summarization...
Related tasks, e.g.
Entity ranking (given a query and target categories)
List completion (given a query and entity examples, and?
types)
Query target type identification
Our focus is on emergent dimensions to explore
Dar´ıo Garigliotti Type-aware Entity Retrieval
7. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Type taxonomies
There are different type taxonomies from various knowledge
bases
DBpedia Ontology
Freebase Types
Wikipedia Categories
YAGO Taxonomy
These vary a lot in terms of hierarchical structure and in how
entity-type assignments are recorded
Normalisation efforts are needed
Dar´ıo Garigliotti Type-aware Entity Retrieval
8. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
DBpedia Ontology
A well-designed hierarchy
Created manually by
considering the most
frequently used infoboxes
in Wikipedia
Clean and consistent, but
with limited coverage
0
1
2
3
4
5
6
7
|Level 1| = 58 types
|Level 2| = 114 types
|Level 3| = 142 types
|Level 4| = 213 types
|Level 5| = 45 types
|Level 6| = 17 types
|Level 7| = 1 type
Dar´ıo Garigliotti Type-aware Entity Retrieval
9. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
DBpedia Ontology
Dar´ıo Garigliotti Type-aware Entity Retrieval
10. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Freebase Types
A two-layer categorization
system: types and
domains
Entities are only assigned
to types, having most of
them “same as” links to
DBpedia entities
0
1
2
|Level 1| = 92 types
|Level 2| = 1, 626 types
Dar´ıo Garigliotti Type-aware Entity Retrieval
11. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Wikipedia Categories
It consists of textual
labels known as
categories
It’s not a well-defined
“is-a” hierarchy, but a
graph
Category assignments
are neither consistent
nor complete
It requires a major
normalisation strategy
0
1
2-10
11-24
25-
34
|Level 1| = 27 types
|Level 2 ∪ ... ∪ Level 10| =
121, 657 types
|Level 11 ∪ ... ∪ Level 24| =
410, 697 types
|Level 25 ∪ ... ∪ Level 34| =
14, 564 types
Dar´ıo Garigliotti Type-aware Entity Retrieval
12. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
YAGO Taxonomy
A deep subsumption
hierarchy
Its classification schema is
constructed by taking leaf
categories from Wikipedia
categories and then using
WordNet synsets to
establish the hierarchy
0
1
2-5
6-10
11-
19
|Level 1| = 61 types
|Level 2 ∪ ... ∪ Level 5| =
80, 384 types
|Level 6 ∪ ... ∪ Level 10| =
461, 843 types
|Level 11 ∪ ... ∪ Level 19| =
26, 383 types
Dar´ıo Garigliotti Type-aware Entity Retrieval
13. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Type representations
How to represent the hierarchical information?
Dar´ıo Garigliotti Type-aware Entity Retrieval
14. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Type representations
How to represent the hierarchical information?
t3t3
t2t2
t5t5t4t4
t9t9t8t8
e
t6t6
t12t12
t7t7
…
t10t10 t11t11
t0t0
t1t1 …
Type(s) along path
to top
Dar´ıo Garigliotti Type-aware Entity Retrieval
15. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Type representations
How to represent the hierarchical information?
t3t3
t2t2
t5t5t4t4
t9t9t8t8
e
t6t6
t12t12
t7t7
…
t10t10 t11t11
t0t0
t1t1 …
Type(s) along path
to top
t3t3
t2t2
t5t5t4t4
t9t9t8t8
e
t6t6
t12t12
t7t7
…
t10t10 t11t11
t0t0
t1t1 …
Top-level type(s)
Dar´ıo Garigliotti Type-aware Entity Retrieval
16. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Type representations
How to represent the hierarchical information?
t3t3
t2t2
t5t5t4t4
t9t9t8t8
e
t6t6
t12t12
t7t7
…
t10t10 t11t11
t0t0
t1t1 …
Type(s) along path
to top
t3t3
t2t2
t5t5t4t4
t9t9t8t8
e
t6t6
t12t12
t7t7
…
t10t10 t11t11
t0t0
t1t1 …
Top-level type(s)
t3t3
t2t2
t5t5t4t4
t9t9t8t8
e
t6t6
t12t12
t7t7
…
t10t10 t11t11
t0t0
t1t1 …
Most specific type(s)
Dar´ıo Garigliotti Type-aware Entity Retrieval
17. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Retrieval models
Retrieval task
defined in a
generative
probabilistic
framework
P(q | e)
query entity
Olympic games
target types
Rio de Janeiro
term-based
similarity
type-based
similarity
… …
entity types
Both query and entity considered in the term space as well as
in the type space
Dar´ıo Garigliotti Type-aware Entity Retrieval
18. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Retrieval models
(Strict) Filtering model
P(q | e) = P(θT
q | θT
e ) · χ[types(q) ∩ types(e) = ∅]
Types(q)Types(q) Types(e)Types(e)
Dar´ıo Garigliotti Type-aware Entity Retrieval
19. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Retrieval models
(Soft) Filtering model
P(q | e) = P(θT
q | θT
e ) · P(θT
q | θT
e )
Dar´ıo Garigliotti Type-aware Entity Retrieval
20. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
Type taxonomies
Type representations
Retrieval models
Retrieval models
Interpolation model
P(q | e) = (1 − λ) · P(θT
q | θT
e ) + λ · P(θT
q | θT
e )
Dar´ıo Garigliotti Type-aware Entity Retrieval
21. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
What did we do?
Lessons learned
Query target type detection
Future work draft
What did we do?
We systematically identified and compared all combinations of
those dimensions
4 type taxonomies: DBpedia Ontology (3.9), Freebase
Types (2015-03-31), Wikipedia Categories (for DBpedia
3.9) and YAGO Taxonomy (3.0.2)
3 type representations: path-to-top, top-level, most
specific
3 models: strict and soft filtering, interpolation
Environment: from idealized to realistic
query types oracle
entities fully typed in all the taxonomies
Dar´ıo Garigliotti Type-aware Entity Retrieval
22. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
What did we do?
Lessons learned
Query target type detection
Future work draft
What did we do? Results
Dar´ıo Garigliotti Type-aware Entity Retrieval
23. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
What did we do?
Lessons learned
Query target type detection
Future work draft
Lessons learned
Summary of insights:
How to represent hierarchical entity type information?
(RQ1) Using the most specific types appears to be the
best way
What (kind of) type taxonomies to use? (RQ2) Wikipedia,
in combination with most specific types, performs the best
in both the idealized and the more realistic scenarios
What combination model to choose? (RQ3) The
interpolation model appears to be more robust
Dar´ıo Garigliotti Type-aware Entity Retrieval
24. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
What did we do?
Lessons learned
Query target type detection
Future work draft
Further analysis: strict filtering vs interpolation models
Strict filtering treats
target types as a set
Interpolation operates
with a probability
distribution over types
When we drop from
oracle every type
assigned to less than 3
entities, interpolation
adapts quite better
DBpedia Freebase Wikipedia YAGO
Most-specific types
DBpedia Freebase Wikipedia YAGO
Most-specific types
Dar´ıo Garigliotti Type-aware Entity Retrieval
25. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
What did we do?
Lessons learned
Query target type detection
Future work draft
Further analysis: query-level ranking details
E.g. performance for
(Interpolation, Most
specific level,
Wikipedia-3.9)
query = “Which books by
Kerouac were published
by Viking Press?”
Types: 90 (including
Viking Press books)
Types of the hurt relevant
entities: all contain
Viking Press books
Dar´ıo Garigliotti Type-aware Entity Retrieval
26. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
What did we do?
Lessons learned
Query target type detection
Future work draft
Further analysis: query-level ranking details
E.g. performance for
(Interpolation, Most
specific level,
Wikipedia-3.9)
query = “Give me all
actors starring in Batman
Begins”
All 7 relevant entities are
improved
Dar´ıo Garigliotti Type-aware Entity Retrieval
27. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
What did we do?
Lessons learned
Query target type detection
Future work draft
Query target type detection
Automatic query target type detection
Baselines
Entity-centric: first, to rank entities based on their relevance
to the query, then look at what types the top-k ranked
entities have
Type-centric: to build a direct term-based representation for
each type, by aggregating descriptions of entities of that type
Learning-to-rank with several features
Dar´ıo Garigliotti Type-aware Entity Retrieval
28. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
What did we do?
Lessons learned
Query target type detection
Future work draft
Query target type detection
Dar´ıo Garigliotti Type-aware Entity Retrieval
29. Types and Entity Retrieval
Environment Dimensions
Type-aware Entity Retrieval
What did we do?
Lessons learned
Query target type detection
Future work draft
Future work draft
Automatic query target type detection must be further
analysed. Experiments revisited with additional features
and expanded set of candidate types.
Query classification, for deciding about query suitability to
be improved its retrieval by type-aware approach
Its performance by itself, and its impact in the full system
Dar´ıo Garigliotti Type-aware Entity Retrieval