This document discusses the background of OntoLex research. It covers several topics:
1. The need for shared semantics on the Semantic Web and in ontology-based applications. Precise ontological representations are needed.
2. The role of linguists in ontology, as ambiguity, polysemy and underspecification must be addressed. Meaning has different levels and dimensions that require analysis.
3. The relationship between language, meaning, concepts and reality. Word meanings are examined in relation to concepts stored in the mental lexicon. Exercises explore how meanings relate to concepts.
This document summarizes several demonstrations presented at a linguistics conference. It describes projects integrating historical lexical databases through linked open data, allowing tracing of word meanings and concepts over time. It also summarizes demonstrations of tools for searching treebanks and researching morphosyntactic dialects in historical Dutch texts. Finally, it provides brief updates on the status of treebank search applications GrETEL and PaQU, and plans to upgrade the morphological research tool MIMORE.
This document outlines a roadmap for making BioPortal, an ontology repository, more multilingual. It proposes representing the natural language of ontologies, distinguishing between monolingual and multilingual ontologies, and representing relations between translated ontologies. It also discusses reconciling multilingual mappings and representing multilingual content overall. Making BioPortal multilingual could enable new discoveries by connecting data in different languages and addressing cultural differences reflected in language. Some challenges include dealing with partial multilingual ontologies and multiple mappings between terms. The proposals aim to better support multilingual ontologies, translations, and mappings within BioPortal.
This document discusses lexical resources like WordNet and FrameNet, and how they can be used to build lexicalized ontologies. It describes WordNet as a freely available lexical database that groups words into synsets within a semantic network. FrameNet is presented as defining words through the semantic frames and roles they evoke. The document also discusses building multilingual ontologies through projects like EuroWordNet and how WordNet can function as a lexicalized ontology through its use of semantic relations to structure word hierarchies.
This document discusses ontological categories and word classes from a linguistic perspective. It covers topics like nouns and things, countability of nouns, and how word classes are not universal across all languages. For example, some languages like Mandarin Chinese and Yurok do not have an adjective class. The document also notes that while linguists often define word classes based on morphosyntactic properties, ontological categories provide an alternative semantic perspective. The lab session will focus on applying ontological categories like things, situations, and properties to lexical semantics and analyzing word classes like nouns, verbs, and adjectives.
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
Petr Simon - Procedural Lexical Semantics (PhD Thesis)Petr Šimon
The document discusses the development of a theory of semantic well-formedness that takes a procedural approach to lexical semantics. It evaluates proposals from Generative Lexicon theory and extends Transparent Intensional Logic to provide a richer analysis of meaning at the lexical and compositional levels. The goal is to describe a flexible formal system for analyzing meaning variation, change, and what makes expressions meaningful.
This document summarizes several demonstrations presented at a linguistics conference. It describes projects integrating historical lexical databases through linked open data, allowing tracing of word meanings and concepts over time. It also summarizes demonstrations of tools for searching treebanks and researching morphosyntactic dialects in historical Dutch texts. Finally, it provides brief updates on the status of treebank search applications GrETEL and PaQU, and plans to upgrade the morphological research tool MIMORE.
This document outlines a roadmap for making BioPortal, an ontology repository, more multilingual. It proposes representing the natural language of ontologies, distinguishing between monolingual and multilingual ontologies, and representing relations between translated ontologies. It also discusses reconciling multilingual mappings and representing multilingual content overall. Making BioPortal multilingual could enable new discoveries by connecting data in different languages and addressing cultural differences reflected in language. Some challenges include dealing with partial multilingual ontologies and multiple mappings between terms. The proposals aim to better support multilingual ontologies, translations, and mappings within BioPortal.
This document discusses lexical resources like WordNet and FrameNet, and how they can be used to build lexicalized ontologies. It describes WordNet as a freely available lexical database that groups words into synsets within a semantic network. FrameNet is presented as defining words through the semantic frames and roles they evoke. The document also discusses building multilingual ontologies through projects like EuroWordNet and how WordNet can function as a lexicalized ontology through its use of semantic relations to structure word hierarchies.
This document discusses ontological categories and word classes from a linguistic perspective. It covers topics like nouns and things, countability of nouns, and how word classes are not universal across all languages. For example, some languages like Mandarin Chinese and Yurok do not have an adjective class. The document also notes that while linguists often define word classes based on morphosyntactic properties, ontological categories provide an alternative semantic perspective. The lab session will focus on applying ontological categories like things, situations, and properties to lexical semantics and analyzing word classes like nouns, verbs, and adjectives.
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
Petr Simon - Procedural Lexical Semantics (PhD Thesis)Petr Šimon
The document discusses the development of a theory of semantic well-formedness that takes a procedural approach to lexical semantics. It evaluates proposals from Generative Lexicon theory and extends Transparent Intensional Logic to provide a richer analysis of meaning at the lexical and compositional levels. The goal is to describe a flexible formal system for analyzing meaning variation, change, and what makes expressions meaningful.
This document discusses ontologies in computer science from the perspective of description logics. It begins by defining ontologies in philosophy and computer science. Description logics are introduced as a family of knowledge representation languages used to formally specify ontologies. Description logics allow concepts and roles to be built using constructors like conjunction, disjunction, and quantification. Description logic knowledge bases consist of a TBox containing terminology and an ABox containing facts about individuals. Reasoning tasks like satisfiability can be used to check a knowledge base for internal consistency.
My Journey into Data Science and ML - Library of Concepts and useful stuff.pdfJoseLuisOssioBejaran
This document provides a comprehensive overview of concepts and techniques related to data science, machine learning, and deep learning. It includes sections on fundamentals like ETL, dimensionality reduction, and data preprocessing. Machine learning topics covered include supervised and unsupervised learning algorithms, ensemble methods, and evaluation metrics. Deep learning concepts such as neural networks, optimization, and transfer learning are also discussed. The document aims to be a useful reference for those looking to learn about these fields.
The document provides details of an information audit conducted for the Niels Bohr Library and Archives. It includes business process maps for key information assets like collections, photographs, oral histories, and books. The maps show current workflows, systems used, and gaps. There are multiple systems for different assets, work is organized by silos, and search functions are limited. Recommendations include adopting common standards, using open source technology, and customizing systems to meet organizational needs.
This document is the introduction to a master's thesis written in Polish that analyzes the translation of functional texts from the perspective of Skopos theory. The introduction provides background on Skopos theory and its focus on the purpose and function of translation. It also discusses the complexity of translation due to differences in language and culture. The purpose of the thesis is to analyze how Skopos theory concepts can help address culture-specific issues that arise in translating various types of functional texts in practice. The introduction outlines the chapters to follow on Skopos theory, the concept of culture in translation theory, and example studies applying Skopos to resolve culture-specific challenges in domains like manuals, advertising, tourism and legal translation.
Keynote presented at the International Association of University Libraries Conference (IATUL), 20 June 2017 in Bolzano, Italy.
Library metadata was created to describe objects and enable a reader to understand when they had the same or a different object in hand. Now linked data concepts and techniques are allowing us to recreate, merge, and link our metadata assets in new ways that better support discovery - both in our local systems and on the wider web. Tennant described this migration and the potential it has for solving key discovery problems.
This document discusses text and data mining (TDM) and provides definitions from 1982, 1999, and 2008 that describe mining as automatically generating logical representations of text passages, the (semi)automated discovery of trends and patterns across large datasets, and the use of automated methods to exploit knowledge in biomedical literature. It also lists different types of content that can be mined, such as images, graphs, tables, datasets, and text, and provides 101 potential uses for content mining, such as finding papers about chemistry in German or papers acknowledging support from the Wellcome Trust.
Taxonomies and Ontologies – The Yin and Yang of Knowledge ModellingSemantic Web Company
See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
This document provides an overview of library resources for research related to biomedical microdevices. It recommends several key disciplines (nanoscience, nanotechnology, biomedical engineering), databases (Science Citation Index, Compendex, Inspec, SciFinder Scholar, PubMed, IEEE Xplore), and search terms. The document highlights interdisciplinary nature of field and importance of using controlled vocabularies to maximize search results. Library staff are available to assist with any questions.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
A System For Citations Retrieval On The WebBrittany Brown
The document describes a system called CiteSeeker that searches the web for citations to specified publications and authors. CiteSeeker crawls the web starting from user-provided seed URLs and follows links to search documents in common formats like HTML, PDF and compressed files for citations. It uses fuzzy searching to account for inaccuracies in search strings. The system is designed to avoid getting stuck in loops during crawling and to minimize memory usage. It is implemented in C# using .NET technologies and external text extraction tools. Results are returned as a list of URLs containing the citations.
This document discusses shared library services that support arts and humanities research with limited resources. It summarizes two national services, Copac and Archives Hub, which provide resource discovery for over 50 million library records and 22,000 archival descriptions respectively. These services create efficiencies of scale and save libraries time and money. The services benefit users by providing comprehensive, accurate information and supporting quality research. The document suggests aggregating academic activity data from these services could further support discovery of hidden collections, new relationships between concepts, and speed up early-stage research. Challenges include effective coordination, developing business cases, engaging researchers, and driving innovation.
247th ACS Meeting: Experiment Markup Language (ExptML)Stuart Chalk
To integrate science into the semantic web it is important to capture the context of research as it is done. ExptML is designed to store information and workflows from the scientific process.
Ontology selection in repositories like AgroPortal and BioPortal can be done through browsing ontologies by category or metadata search. Users can define metadata for their own ontologies to aid discovery. The ontology recommender system can also suggest related ontologies. Evaluation of ontologies is challenging due to the large number and variety of ontologies in different formats and complexity levels for various user needs. Careful selection is important to avoid issues with missing relevant information or connections between data.
The document discusses how bio-ontologies and natural language processing can enable open science by facilitating structured knowledge representation and collaborative curation. It describes services provided by the National Center for Biomedical Ontology (NCBO) that allow use of ontologies for annotation, data aggregation, and accelerating the curation process. Several groups are highlighted that utilize NCBO services for applications such as clinical trial matching, specimen banking, and data summarization.
This document provides an introduction to the textbook "Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence" by Sandro Skansi. The textbook aims to provide an overview of deep learning for undergraduates, covering topics from foundational concepts to applications. It uses Python code examples with Keras to demonstrate deep learning techniques. The preface discusses the author's goal for the book to serve as an introductory resource for readers new to deep learning and how it relates to fields like artificial intelligence, machine learning, and statistics.
Towards Open Methods: Using Scientific Workflows in LinguisticsRichard Littauer
The document discusses how scientific workflows can be used in linguistics research to automate processing, analysis, and management of linguistic data. Workflows make research more reproducible by documenting methods. They could allow accessing and downloading open linguistic databases. Hypothetical examples show workflows linking text characters to dictionary definitions. Workflows may help standardize part-of-speech tags. Tracking workflows early can help share methods and ensure reproducibility.
Earth Sciences 4490: Getting Started on your Literature Reviewdansich
This document provides an overview and instructions for an Earth Sciences literature review course. It introduces students to library databases like GeoRef, Web of Science, and SciFinder Scholar to search for articles, and to RefWorks for organizing citations and creating bibliographies. The document schedules two sessions to help students learn database searching, get full text articles, and import references to RefWorks. It provides tips for effective database searching and instructions for exercises using the different resources.
Wf4Ever: Scientific Workflows and Research Objects as tools for scientific in...Joint ALMA Observatory
The document outlines current challenges in radio astronomy and potential solutions using scientific workflows and research objects. It introduces the speaker and their background and interests in bringing computational tools and the virtual observatory to radio astronomy. Specific challenges discussed include an overabundance of data that is difficult to find, document, share and reproduce. The talk proposes that workflows and research objects could help address these issues by defining computations and dependencies, enabling distributed and interactive computing, and providing tools for workflow storage, discovery and provenance.
This document discusses ontologies in computer science from the perspective of description logics. It begins by defining ontologies in philosophy and computer science. Description logics are introduced as a family of knowledge representation languages used to formally specify ontologies. Description logics allow concepts and roles to be built using constructors like conjunction, disjunction, and quantification. Description logic knowledge bases consist of a TBox containing terminology and an ABox containing facts about individuals. Reasoning tasks like satisfiability can be used to check a knowledge base for internal consistency.
My Journey into Data Science and ML - Library of Concepts and useful stuff.pdfJoseLuisOssioBejaran
This document provides a comprehensive overview of concepts and techniques related to data science, machine learning, and deep learning. It includes sections on fundamentals like ETL, dimensionality reduction, and data preprocessing. Machine learning topics covered include supervised and unsupervised learning algorithms, ensemble methods, and evaluation metrics. Deep learning concepts such as neural networks, optimization, and transfer learning are also discussed. The document aims to be a useful reference for those looking to learn about these fields.
The document provides details of an information audit conducted for the Niels Bohr Library and Archives. It includes business process maps for key information assets like collections, photographs, oral histories, and books. The maps show current workflows, systems used, and gaps. There are multiple systems for different assets, work is organized by silos, and search functions are limited. Recommendations include adopting common standards, using open source technology, and customizing systems to meet organizational needs.
This document is the introduction to a master's thesis written in Polish that analyzes the translation of functional texts from the perspective of Skopos theory. The introduction provides background on Skopos theory and its focus on the purpose and function of translation. It also discusses the complexity of translation due to differences in language and culture. The purpose of the thesis is to analyze how Skopos theory concepts can help address culture-specific issues that arise in translating various types of functional texts in practice. The introduction outlines the chapters to follow on Skopos theory, the concept of culture in translation theory, and example studies applying Skopos to resolve culture-specific challenges in domains like manuals, advertising, tourism and legal translation.
Keynote presented at the International Association of University Libraries Conference (IATUL), 20 June 2017 in Bolzano, Italy.
Library metadata was created to describe objects and enable a reader to understand when they had the same or a different object in hand. Now linked data concepts and techniques are allowing us to recreate, merge, and link our metadata assets in new ways that better support discovery - both in our local systems and on the wider web. Tennant described this migration and the potential it has for solving key discovery problems.
This document discusses text and data mining (TDM) and provides definitions from 1982, 1999, and 2008 that describe mining as automatically generating logical representations of text passages, the (semi)automated discovery of trends and patterns across large datasets, and the use of automated methods to exploit knowledge in biomedical literature. It also lists different types of content that can be mined, such as images, graphs, tables, datasets, and text, and provides 101 potential uses for content mining, such as finding papers about chemistry in German or papers acknowledging support from the Wellcome Trust.
Taxonomies and Ontologies – The Yin and Yang of Knowledge ModellingSemantic Web Company
See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
This document provides an overview of library resources for research related to biomedical microdevices. It recommends several key disciplines (nanoscience, nanotechnology, biomedical engineering), databases (Science Citation Index, Compendex, Inspec, SciFinder Scholar, PubMed, IEEE Xplore), and search terms. The document highlights interdisciplinary nature of field and importance of using controlled vocabularies to maximize search results. Library staff are available to assist with any questions.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
A System For Citations Retrieval On The WebBrittany Brown
The document describes a system called CiteSeeker that searches the web for citations to specified publications and authors. CiteSeeker crawls the web starting from user-provided seed URLs and follows links to search documents in common formats like HTML, PDF and compressed files for citations. It uses fuzzy searching to account for inaccuracies in search strings. The system is designed to avoid getting stuck in loops during crawling and to minimize memory usage. It is implemented in C# using .NET technologies and external text extraction tools. Results are returned as a list of URLs containing the citations.
This document discusses shared library services that support arts and humanities research with limited resources. It summarizes two national services, Copac and Archives Hub, which provide resource discovery for over 50 million library records and 22,000 archival descriptions respectively. These services create efficiencies of scale and save libraries time and money. The services benefit users by providing comprehensive, accurate information and supporting quality research. The document suggests aggregating academic activity data from these services could further support discovery of hidden collections, new relationships between concepts, and speed up early-stage research. Challenges include effective coordination, developing business cases, engaging researchers, and driving innovation.
247th ACS Meeting: Experiment Markup Language (ExptML)Stuart Chalk
To integrate science into the semantic web it is important to capture the context of research as it is done. ExptML is designed to store information and workflows from the scientific process.
Ontology selection in repositories like AgroPortal and BioPortal can be done through browsing ontologies by category or metadata search. Users can define metadata for their own ontologies to aid discovery. The ontology recommender system can also suggest related ontologies. Evaluation of ontologies is challenging due to the large number and variety of ontologies in different formats and complexity levels for various user needs. Careful selection is important to avoid issues with missing relevant information or connections between data.
The document discusses how bio-ontologies and natural language processing can enable open science by facilitating structured knowledge representation and collaborative curation. It describes services provided by the National Center for Biomedical Ontology (NCBO) that allow use of ontologies for annotation, data aggregation, and accelerating the curation process. Several groups are highlighted that utilize NCBO services for applications such as clinical trial matching, specimen banking, and data summarization.
This document provides an introduction to the textbook "Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence" by Sandro Skansi. The textbook aims to provide an overview of deep learning for undergraduates, covering topics from foundational concepts to applications. It uses Python code examples with Keras to demonstrate deep learning techniques. The preface discusses the author's goal for the book to serve as an introductory resource for readers new to deep learning and how it relates to fields like artificial intelligence, machine learning, and statistics.
Towards Open Methods: Using Scientific Workflows in LinguisticsRichard Littauer
The document discusses how scientific workflows can be used in linguistics research to automate processing, analysis, and management of linguistic data. Workflows make research more reproducible by documenting methods. They could allow accessing and downloading open linguistic databases. Hypothetical examples show workflows linking text characters to dictionary definitions. Workflows may help standardize part-of-speech tags. Tracking workflows early can help share methods and ensure reproducibility.
Earth Sciences 4490: Getting Started on your Literature Reviewdansich
This document provides an overview and instructions for an Earth Sciences literature review course. It introduces students to library databases like GeoRef, Web of Science, and SciFinder Scholar to search for articles, and to RefWorks for organizing citations and creating bibliographies. The document schedules two sessions to help students learn database searching, get full text articles, and import references to RefWorks. It provides tips for effective database searching and instructions for exercises using the different resources.
Wf4Ever: Scientific Workflows and Research Objects as tools for scientific in...Joint ALMA Observatory
The document outlines current challenges in radio astronomy and potential solutions using scientific workflows and research objects. It introduces the speaker and their background and interests in bringing computational tools and the virtual observatory to radio astronomy. Specific challenges discussed include an overabundance of data that is difficult to find, document, share and reproduce. The talk proposes that workflows and research objects could help address these issues by defining computations and dependencies, enabling distributed and interactive computing, and providing tools for workflow storage, discovery and provenance.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
1. Background of the OntoLex Research
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Ontology and the Lexicon
Week 2: An Introduction
Shu-Kai Hsieh
Lab of Ontologies, Language Processing and e-Humanities
GIL, National Taiwan University
February 26, 2014
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The Need and Trend of Shared Semantics
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Your Agent or Enemy needs this
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Ontology-based Applications
We need a shared and common understanding (of a domain) that
can be communicated across people and machines (applications),
and it will play a major role in supporting information exchange
and discovery, we well as many applications:
• e-Government, e-Health, e-Commerce,..
• open data integration and interoperability.
• ontology-backboned search engine
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Languages representing Ontologies
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Languages representing Ontologies
Can we use Glossaries, Terminologies, Meta data or
other Controlled Vocabulary instead? No: they are just not
precise enough to capture linguistic and cognitive complexity.
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Why do we need Linguists for Ontology?
• ambiguity, polysemy, underspecification,...OH, 沒有語言學的
介入,知識整理工作好無趣也無頭緒。
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1 Background of the OntoLex Research
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What is Ontology
• In Philosophy (dating back to Artistotle), → Ontos (that
which exists) + logos (knowledge of).
• In Computer Science, → ”an explicit specification of a
conceptualization (Gruber (1995))”, ”an engineering artefact
that represents the shared conceptualisation of objects in a
domain of interest”.
explicit specification : Written in logic, as a set of axioms.
conceptualization : the set of objects and relations in a
domain. <Objects,Relations,Functions>
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What is Ontological Representation
• Not what it is (ontology) but how it is represented
(ontological representation).
• There are many ways to represent ontologies (recall that it is
an engineering artefact) → that at best approximate our real
concepts and conceptualisations, even we don’t quite
understand what we are approximating.
• The evaluation of an engineering artefact turns out to be
whether it is fit for purpose or not.
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1 Background of the OntoLex Research
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Semantic Web
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Cognitive-Functional Take
Linguistic meaning is ultimately a matter of
conceptualizing the things we talk about: when we put
things into words, we are not just giving a one-to-one
mapping of what the world is like - we make a choice by
putting things in the particular way we do. [2].
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Empirical Epistimological Stance
• OntoLex (Lexicalized Ontology) is exclusively concerned with
the ”conceptual meanings” of lexical units (that can be
empirically represented), NOT with the those of actions or
phenomena.
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Language, Meaning and Reality
• [單位-意義] 可以切割出來觀察嗎?
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Language, Meaning and Reality
跨語言觀察可能更接近
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Meaning(s)
• Meaning has many readings!
• 他的意思應該是如果不給他意思一下就很沒有意思
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Meaning(s): levels
Linguistic approach to meaning requires us to break down the
notion of meaning into different levels at which we interprete
linguistic expressions [2].
expression level the meaning of a simple or complex expression
taken in isolation.
utterance level the meaning of an expression when used in a given
context of utterance resulting from fixing reference.
communication level the meaning of an utterance as a
communicative act in a given social setting.
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Meaning(s): levels
Example
I don’t need your bicyle.
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Meaning(s): dimensions
[2] provides three dimensions of meaning:
• descriptive dimension: propositional meaning; related to
reference and truth.
• social dimension: related to honorifics.
• expressive dimension: conventionally serves the expression
of subjective sensations, emotions, affections, evaluations or
attitudes.
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A clear boundary in the Study of Meaning?
• Lexical Semantics: the investigation of expression meanings
stored in the mental lexicon.
• Semantics vs. Pragmatics: Semantics ends where
contextual knowledge comes in? (i.e., utterance meaning and
communicative meaning are beyond semantics?)
• Meanings are Concepts?
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For the sake of communication, we perceive the world (entities,
events, relations) linguistically via sound and/or orthographic form,
which trigger our MENTAL SOMETHING for further processing
and production.
• A Concept is a general mental description of something that is
stored in our mental lexicon.
• Word Sense is the instantiation and differentiation of Concept
in varied contexts.
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From Meanings to Concepts and Back
usage-based exercise
我小時候養了 13 條狗; 狗是人類的好朋友;她看到狗就
怕;狗仔隊毀了這個社會的善良風俗
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From Semantics to Cognitive Semantics, and Back?
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Exercise
What can be opened? (Searle, 1983)
Example
a. John opened the window.
b. John opened his mouth.
c. John opened his book.
d. John opened his briefcase.
e. John opened the curtains.
f. The carpenter opened the wall.
g. The surgeon opened the wound.
h. The sapper opened the dam.
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Underspecified Meanings: Exercise
What can be opened? more figurative ... (Evans, 2009)
Example
a. The discussant opened the conversation.
b. John opened a bank account.
c. John opened the meeting.
d. John opened a dialogue.
e. John opened the curtains.
f. The Germans opened hostilities against the Allies in 1940.
g. The skies opened.
h. He opened his mind to a new way of thinking.
g. He finally opened up to her.
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Underspecified Meanings: Exercise
What can be opened again, in Chinese?
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Underspecified Meanings: Exercise
My two cents ..
Example
車,穴,店,窗,會,視野,燈,口,頭,花,槍,心,源,天,門,箱,戶,工,瓶,國,同學
會,隧道,玩笑...
Each relates to distinct sorts of actions, events, and situations.
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In sum, open appears to be a function of (1) (sentential) context
which guides the (2) encyclopedic and conceptual knowledge
(ontology) to which open relates in a given instance of use. (word
meaning)
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In the Context of Cognitive Science ...
Cognitive linguistics practice can be divided approximately into two
areas: (Evans, 2009)
1
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Cognitive semantics: the study of semantic representation,
the human conceptual system,and meaning construction
processes as revealed by language.
2
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Cognitive (approaches) to grammar: the study of the
symbolic linguistic units that comprise language, and their
principles of organization.
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Cognitive Lexical Semantics
To develop a cognitive linguistic account of
1
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lexical concept: the linguistic knowledge that word encode.
2
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cognitive model: the conceptual system, i.e., the
non-linguistic knowledge that words facilitate access to. →
This provides a level of non-linguistic knowledge which is
specialized for being accessed via lexical concepts.
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Ontology-based Linguistic Studies [1]
A variety of polysemy that gets a fair amount of linguistic
attention is regular (or systematic) polysemy. This refers to word
senses that are distinct, but which follow a general pattern or rule
in the language.
• container/contents:
I put some sand into a box/bottle/tin/canister.
I dumped the whole box/bottle/tin/canister onto the floor.
• location/government/inhabitants:city
• physical object/information: book
• .......
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Ontology-based Linguistic Studies [2]
Example (組合的概念痕跡)
schoolboy (小中学校の)男子生徒 a boy attending school.
schoolmate 学校の友達 a person who attends or attended the
same school as oneself.
schoolhouse 校舍 a building used as a school, especially in a small
community or village.
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Non-hierarchical Ontologies: Qualia Structure
Pustejovsky (1995,p76,85-86): qualia as information about ”four
essential aspects of a word’s meaning”, should enter into individual
items’ meaning representations. These are:
• Formal: the basic category which distinguishes it within a
larger domain;
• Constitutive: the relation between an object and its
constituent parts;
• Telic: its purpose and function;
• Agentive: factors involved in its origin or ”bringing it about”.
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1 Background of the OntoLex Research
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Semantic Web
What is Ontology
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OntoLex Resources
• Wiki Family (today)
• Lexical Resources
• Ontological Resources
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Wiki: its role
• A good review of current state-of-arts can be found in [1].
• Resolving the Knowledge acquisition bottleneck: The
creation of very large knowledge bases has been made possible
by the availability of collaboratively-curated online resources
such as Wikipedia and Wiktionary.
• structured, semi-structured, unstructured resources.
• what are the advantages and disadvantages, respectively?
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Wiki as semi-structured content for Ontologies
• Transforming Wikipedia into machine-readable knowledge
• Acquiring related terms: thesaurus extraction
• Relation extraction
• Leitmotif: generating semantics by exploiting the shallow
structure found in Wikipedia.
• Building and enriching ontologies from Wikipedia: YAGO,
WikiNet and BabelNet.
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Multilingual case of collaboratively-generated
semi-structured resources
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• WikiTaxonomy (Ponzetto and Strube, 2007; Ponzetto and
Strube, 2011)(100k is-a relations)
• WikiNet: (Nas- tase et al., 2010; Nastase and Strube, 2013)
is a project which heuristically exploits different aspects of
Wikipedia to obtain a multilingual concept network by deriving
not only is-a relations, but also other types of relations.
• MENTA (de Melo and Weikum, 2010), creates one of the
largest multilingual lexical knowledge bases by interconnecting
more than 13M articles in 271 languages.
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1 Background of the OntoLex Research
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Semantic Web
What is Ontology
Ontology for Linguistics
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Homework
• Install Protégé http://protege.stanford.edu/, and
• Read the tutorials
Next class: Creating your first (Pizza) ontology following http://
protegewiki.stanford.edu/wiki/Protege4Pizzas10Minutes
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Eduard Hovy, Roberto Navigli, and Simone Paolo Ponzetto.
Collaboratively built semi-structured content and artificial
intelligence: The story so far.
Artificial Intelligence, 194:2–27, 2013.
Sebastian Löbner.
Understanding semantics.
Routledge, 2013.
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Shu-Kai Hsieh
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