Concepts as Action-Oriented as 'Search'
•Download as PPT, PDF•
0 likes•479 views
The document proposes that concepts can be understood as networks of features that are activated through a search process, similar to how search engines work. It argues that concepts have a distributed representation in the brain and are activated based on their relations to other concepts and features, rather than having strict definitions. This view of concepts as action-oriented networks addressed issues with other theories, and provides a way to understand conceptual combination and other phenomena. However, questions remain about how to account for more abstract concepts.
Report
Share
Report
Share
![Concepts as Action-Oriented as 'Search' ,[object Object],[object Object]](https://image.slidesharecdn.com/concepts-as-actionoriented-as-search3193/85/Concepts-as-Action-Oriented-as-Search-1-320.jpg)
![Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Knowledge Representation in Artificial intelligence
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
Introduction to Ontology Concepts and Terminology
The document introduces an ontology tutorial that will cover basic concepts of the Semantic Web, Linked Data, and the Resource Description Framework data model as well as the ontology languages RDFS and OWL. The tutorial is intended for information professionals who want to gain an introductory understanding of ontologies, ontology concepts, and terminology. The tutorial will explain how to model and structure data as RDF triples and create basic RDFS ontologies.
Schema-agnositc queries over large-schema databases: a distributional semanti...
This document provides an overview and summary of André Freitas' PhD thesis defense presentation on schema-agnostic queries for large schema databases using distributional semantics. The presentation motivates the need for schema-agnostic queries due to the rise of very large and dynamic database schemas. It proposes using distributional semantics to provide an accurate, comprehensive and low maintenance approach to cope with semantic heterogeneity in schema-agnostic queries. The key aspects of the approach include semantic pivoting to reduce semantic complexity, distributional semantic models to enable semantic matching, and a hybrid distributional-relational semantic model called τ-Space to support the development of a schema-agnostic query mechanism.
Context, Perspective, and Generalities in a Knowledge Ontology
This presentation to the Ontolog Forum in Dec 2016 presents the knowledge graph (ontology) design for KBpedia, a system of six major knowledge bases and 20 minor ones for conducting knowledge-based artificial intelligence (KBAI). The talk emphasizes the roots of the system in the triadic logic of Charles Sanders Peirce. It also discusses the use of KBpedia for the more-or-less automatic ways it can help create training corpuses, training sets, and reference standards for supervised, unsupervised and deep machine learning. Uses of the system include entity and relation extraction and tagging, classification, clustering, sentiment analysis, and other AI tasks.
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...
The growing size, heterogeneity and complexity of databases demand the creation of strategies to facilitate users and systems to consume data. Ideally, query mechanisms should be schema-agnostic, i.e. they should be able to match user queries in their own vocabulary and syntax to the data, abstracting data consumers from the representation of the data. This work provides an informationtheoretical framework to evaluate the semantic complexity involved in the query-database communication, under a schema-agnostic query scenario. Different entropy measures are introduced to quantify the semantic phenomena involved in the user-database communication, including structural complexity, ambiguity, synonymy and vagueness. The entropy measures are validated using natural language queries over Semantic Web databases. The analysis of the semantic complexity is used to improve the understanding of the core semantic dimensions present at the query-data matching process, allowing the improvement of the design of schema-agnostic query mechanisms and defining measures which can be used to assess the semantic uncertainty or difficulty behind a schema-agnostic querying task.
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC
I will claim that Semantic Web Patterns can drive the next technological breakthrough: they can be key for providing intelligent applications with sophisticated ways of interpreting data. I will picture scenarios of a possible not so far future in order to support my claim. I will argue that current Semantic Web Patterns are not sufficient for addressing the envisioned requirements, and I will suggest a research direction for fixing the problem, which includes the hybridisation of existing computer science pattern-based approaches, and human computing.
Open IE tutorial 2018
Here are some science-related events from EventKG that took place in Lyon:
- 1921: "À Lyon, fusion de la Société de médecine et de la Société des sciences médicales" (In Lyon, merger of the Medical Society and the Society of Medical Sciences)
- 1987: "The International Astronomical Union organizes its 24th General Assembly in Lyon"
- 1988: "The International Astronomical Union organizes its 25th General Assembly in Lyon"
- 2009: "The International Astronomical Union organizes its 26th General Assembly in Lyon"
- 2015: "The International Astronomical Union organizes its 29th General Assembly in Lyon"
-
Different Semantic Perspectives for Question Answering Systems
Question Answering systems define one of the most complex tasks in computational semantics. The intrinsic complexity of the QA task allows researchers of QA systems to investigate and explore different perspectives of semantics. However, this complexity also induces a bias towards a systems perspective, where researchers are alienated from a deeper reasoning on the semantic principles that are in place within the different components of the system. In this talk we will explore the semantic challenges, principles and perspectives behind the components of QA systems, aiming at providing a principled map and overview on the contribution of each component within the QA semantic interpretation goal.
Recommended
Knowledge Representation in Artificial intelligence
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
Introduction to Ontology Concepts and Terminology
The document introduces an ontology tutorial that will cover basic concepts of the Semantic Web, Linked Data, and the Resource Description Framework data model as well as the ontology languages RDFS and OWL. The tutorial is intended for information professionals who want to gain an introductory understanding of ontologies, ontology concepts, and terminology. The tutorial will explain how to model and structure data as RDF triples and create basic RDFS ontologies.
Schema-agnositc queries over large-schema databases: a distributional semanti...
This document provides an overview and summary of André Freitas' PhD thesis defense presentation on schema-agnostic queries for large schema databases using distributional semantics. The presentation motivates the need for schema-agnostic queries due to the rise of very large and dynamic database schemas. It proposes using distributional semantics to provide an accurate, comprehensive and low maintenance approach to cope with semantic heterogeneity in schema-agnostic queries. The key aspects of the approach include semantic pivoting to reduce semantic complexity, distributional semantic models to enable semantic matching, and a hybrid distributional-relational semantic model called τ-Space to support the development of a schema-agnostic query mechanism.
Context, Perspective, and Generalities in a Knowledge Ontology
This presentation to the Ontolog Forum in Dec 2016 presents the knowledge graph (ontology) design for KBpedia, a system of six major knowledge bases and 20 minor ones for conducting knowledge-based artificial intelligence (KBAI). The talk emphasizes the roots of the system in the triadic logic of Charles Sanders Peirce. It also discusses the use of KBpedia for the more-or-less automatic ways it can help create training corpuses, training sets, and reference standards for supervised, unsupervised and deep machine learning. Uses of the system include entity and relation extraction and tagging, classification, clustering, sentiment analysis, and other AI tasks.
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...
The growing size, heterogeneity and complexity of databases demand the creation of strategies to facilitate users and systems to consume data. Ideally, query mechanisms should be schema-agnostic, i.e. they should be able to match user queries in their own vocabulary and syntax to the data, abstracting data consumers from the representation of the data. This work provides an informationtheoretical framework to evaluate the semantic complexity involved in the query-database communication, under a schema-agnostic query scenario. Different entropy measures are introduced to quantify the semantic phenomena involved in the user-database communication, including structural complexity, ambiguity, synonymy and vagueness. The entropy measures are validated using natural language queries over Semantic Web databases. The analysis of the semantic complexity is used to improve the understanding of the core semantic dimensions present at the query-data matching process, allowing the improvement of the design of schema-agnostic query mechanisms and defining measures which can be used to assess the semantic uncertainty or difficulty behind a schema-agnostic querying task.
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC
I will claim that Semantic Web Patterns can drive the next technological breakthrough: they can be key for providing intelligent applications with sophisticated ways of interpreting data. I will picture scenarios of a possible not so far future in order to support my claim. I will argue that current Semantic Web Patterns are not sufficient for addressing the envisioned requirements, and I will suggest a research direction for fixing the problem, which includes the hybridisation of existing computer science pattern-based approaches, and human computing.
Open IE tutorial 2018
Here are some science-related events from EventKG that took place in Lyon:
- 1921: "À Lyon, fusion de la Société de médecine et de la Société des sciences médicales" (In Lyon, merger of the Medical Society and the Society of Medical Sciences)
- 1987: "The International Astronomical Union organizes its 24th General Assembly in Lyon"
- 1988: "The International Astronomical Union organizes its 25th General Assembly in Lyon"
- 2009: "The International Astronomical Union organizes its 26th General Assembly in Lyon"
- 2015: "The International Astronomical Union organizes its 29th General Assembly in Lyon"
-
Different Semantic Perspectives for Question Answering Systems
Question Answering systems define one of the most complex tasks in computational semantics. The intrinsic complexity of the QA task allows researchers of QA systems to investigate and explore different perspectives of semantics. However, this complexity also induces a bias towards a systems perspective, where researchers are alienated from a deeper reasoning on the semantic principles that are in place within the different components of the system. In this talk we will explore the semantic challenges, principles and perspectives behind the components of QA systems, aiming at providing a principled map and overview on the contribution of each component within the QA semantic interpretation goal.
AI Beyond Deep Learning
This document summarizes Andre Freitas' talk on AI beyond deep learning. It discusses representing meaning from text at scale using knowledge graphs and embeddings. It also covers using neuro-symbolic models like graph networks on top of knowledge graphs to enable few-shot learning, explainability, and transportability. The document advocates that AI engineers should focus on representation design and evaluating multi-component NLP systems.
Semantics at Scale: A Distributional Approach
1) The document discusses using distributional semantics to build robust semantic models that can handle large amounts of data and enable semantic computing at scale.
2) It describes how distributional semantic models can be used to represent word meanings based on their linguistic contexts, allowing semantic knowledge bases to be automatically constructed from large text corpora.
3) The author proposes a schema-agnostic approach using distributional semantics to enable querying databases without prior knowledge of schemas, addressing problems of vocabulary and structural differences between queries and data.
Effective Semantics for Engineering NLP Systems
Provide a synthesis of the emerging representation trends behind NLP systems.
Shift in perspective:
Effective engineering (task driven, scalable) instead of sound formalism.
Best-effort representation.
Knowledge Graphs (Frege revisited)
Information Extraction & Text Classification
Distributional Semantic Models
Knowledge Graphs & Distributional Semantics
(Distributional-Relational Models)
Applications of DRMs
KG Completion
Semantic Parsing
Natural Language Inference
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.PadmapriyaIJET - International Journal of Engineering and Techniques
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.Semantic Relation Classification: Task Formalisation and Refinement
The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded,
allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.
Building AI Applications using Knowledge Graphs
This document provides an overview of building AI applications using knowledge graphs. It discusses the goals of the tutorial, which are to provide a broad view of multiple perspectives on knowledge graphs and show how knowledge graphs can form the foundation for building AI systems. The tutorial focuses on contemporary and emerging perspectives through exemplar approaches and infrastructures, rather than providing an exhaustive survey. It also notes that the tutorial is not a standard academic tutorial and takes a big picture view rather than being a comprehensive survey.
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
The document describes the Schema-Agnostic Queries (SAQ-2015) challenge which aims to support querying over complex databases without needing to understand the underlying schema. It presents a test collection for evaluating systems on their ability to answer schema-agnostic queries over linked data. The test collection contains schema-agnostic queries mapped to equivalent SPARQL queries along with categorized mappings. An analysis finds the test collection expresses a variety of mapping types, data model mappings, compositional mappings, and query operations.
Ir 01
This document provides an overview of an information retrieval course. The course will cover topics related to information retrieval models, techniques, and systems. Students will complete exams, assignments, and a major project to build a search engine using both text-based and semantic retrieval techniques. The document defines key concepts in information retrieval and discusses different types of information retrieval systems and techniques.
Semantic Perspectives for Contemporary Question Answering Systems
This document summarizes an academic presentation about semantic perspectives for contemporary question answering systems. It discusses multiple perspectives of semantic representation including facts versus definitions and temporality. It also outlines lightweight semantic representation using RDF and distributional semantics. The document then discusses knowledge graph extraction from text including taxonomy extraction, n-ary relation extraction, and argumentation representation. It concludes with an overview of querying knowledge graphs using distributional semantics and semantic approximation as core operations.
Information_Retrieval_Models_Nfaoui_El_Habib
This document provides an overview of information retrieval models, including vector space models, TF-IDF, Doc2Vec, and latent semantic analysis. It begins with basic concepts in information retrieval like document indexing and relevance scoring. Then it discusses vector space models and how documents and queries are represented as vectors. TF-IDF weighting is explained as assigning higher weight to rare terms. Doc2Vec is introduced as an extension of word2vec to learn document embeddings. Latent semantic analysis uses singular value decomposition to project documents to a latent semantic space. Implementation details and examples are provided for several models.
Unit 2(knowledge)
The document discusses knowledge representation in artificial intelligence. It defines knowledge as information used to solve problems, including facts, concepts, procedures, models and examples. Knowledge representation schemes include declarative, procedural, inheritable, inferential, relational and heuristic knowledge. Facts can be represented internally in a computer using logic or rules. Propositional calculus and predicate logic are also discussed as methods for knowledge representation, along with inference rules and predicates. The key aspects of knowledge acquisition and creation are outlined.
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
To optimally interpret most natural language queries, it is necessary to understand the phrases, entities, commands, and relationships represented or implied within the search. Knowledge graphs serve as useful instantiations of ontologies which can help represent this kind of knowledge within a domain.
In this talk, we'll walk through techniques to build knowledge graphs automatically from your own domain-specific content, how you can update and edit the nodes and relationships, and how you can seamlessly integrate them into your search solution for enhanced query interpretation and semantic search. We'll have some fun with some of the more search-centric use cased of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "bbq near haystack" into
{ filter:["doc_type":"restaurant"], "query": { "boost": { "b": "recip(geodist(38.034780,-78.486790),1,1000,1000)", "query": "bbq OR barbeque OR barbecue" } } }
We'll also specifically cover use of the Semantic Knowledge Graph, a particularly interesting knowledge graph implementation available within Apache Solr that can be auto-generated from your own domain-specific content and which provides highly-nuanced, contextual interpretation of all of the terms, phrases and entities within your domain. We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding within your search engine.
How To Make Linked Data More than Data
This document discusses how adding formal semantics to linked open data can make it more useful and powerful. It describes how existing linked data lacks formal semantics, limiting its capabilities. The document proposes two approaches: 1) Enriching linked data schemas using ontology matching techniques to capture relationships between datasets. 2) Developing a system called LOQUS that can perform federated queries across multiple linked datasets by decomposing queries and merging results. This would allow queries without needing intimate knowledge of each dataset's structure.
Continuous Learning Algorithms - a Research Proposal Paper
General software intelligences are still held to be outside our current capacity to build. While the definition of intelligence which we apply to machine learning and artificial intelligence generally has expanded over time as our practical computational scales increase, little exploration has been conducted around the other aspect of intelligence, which is the capacity to constantly learn and improve through interaction with the environment. If we are to define a software intelligence as an algorithm that is capable of interacting with its environment and adapting to it over time, then this exploration is critical to the development of such a system.
This body of research will attempt to make the first step into the area of continual feedback for a machine learning algorithm, evaluating it against an area which has traditionally been difficult for computers to emulate – Name Matching Analysis. If a machine learning algorithm can be used to ‘tune’ a soft-search name matching algorithm based on continual feedback generated from the results of that engine and the feedback provided by human experts, then this technique of constant feedback not only has immediate practical value but could be explored further in more ambitious research projects.
Introducing grounded theory
This document outlines the structure and objectives of a workshop on Grounded Theory. The workshop will introduce participants to the basic principles and procedures of Grounded Theory. It will cover topics such as the origins and theoretical underpinnings of Grounded Theory, how to conduct Grounded Theory research through procedures like open coding, theoretical sampling and memo writing, and putting the analysis together to build a theory. By the end of the workshop, participants will understand and have hands-on experience with Grounded Theory to help inform their own research.
Text Analytics for Semantic Computing
Text Analytics for Semantic Computing - the good, the bad and the ugly, tutorial presentation at ICSC 2008
http://icsc.eecs.uci.edu/tutorial1.html
Ijetcas14 624
This document summarizes a survey on string similarity matching search techniques. It discusses how string similarity matching is used to find relevant information in text collections. The document reviews different algorithms for string matching, including edit distance, NR-grep, n-grams, and approaches based on hashing and locality-sensitive hashing. It analyzes techniques like pattern matching, threshold-based joins, and vector representations. The goal is to present an overview of the field and compare algorithm performance for similarity searches.
Technical Whitepaper: A Knowledge Correlation Search Engine
For the technically oriented reader, this brief paper describes the technical foundation of the Knowledge Correlation Search Engine - patented by Make Sence, Inc.
The impact of standardized terminologies and domain-ontologies in multilingua...
The impact of standardized terminologies and domain-ontologies in multilingua...AIMS (Agricultural Information Management Standards)
The document discusses the impact of standardized terminologies and domain ontologies in multilingual information processing. It outlines how natural language processing (NLP) techniques can be used to semi-automatically populate ontologies by extracting information from text. Integrating knowledge from ontologies, NLP tools, and subject experts allows for more effective information access and management in an organization.020610
The document discusses challenges with digital libraries and semantic support. It notes that human information processing is limited and computers do not understand natural language queries. Semantic support adds metadata to information to help humans and computers work together by giving meaning to data. However, communication between digital libraries remains a challenge. While some projects have created ontologies and cross-concordances, researchers often prefer to add their own tags due to difficulties making comprehensive ontologies for research data content. The literature on these topics is very technical and may not effectively communicate with scholars.
Searching for patterns in crowdsourced information
This document introduces crowdsourcing and discusses discovering patterns in crowdsourced data. It discusses defining the context of volunteered information on the internet in order to understand relationships between data. A network model is proposed where different types of context define nodes and relationships between context determine edges. Properties of small world networks are discussed including how they could be used to model relationships between crowdsourced data and evaluate data quality. Finally, applications to search ranking, privacy and security are briefly mentioned.
Probabilistic Topic Models
Presents Natural Language Processing (NLP) algorithms for for Bay Area NLP reading group. Survey of Probabilistic Topic Modeling such as Latent Dirichlet Allocation (LDA). Includes practical references explaining the algorithm along with software libraries for Python, Spark, and R.
More Related Content
What's hot
AI Beyond Deep Learning
This document summarizes Andre Freitas' talk on AI beyond deep learning. It discusses representing meaning from text at scale using knowledge graphs and embeddings. It also covers using neuro-symbolic models like graph networks on top of knowledge graphs to enable few-shot learning, explainability, and transportability. The document advocates that AI engineers should focus on representation design and evaluating multi-component NLP systems.
Semantics at Scale: A Distributional Approach
1) The document discusses using distributional semantics to build robust semantic models that can handle large amounts of data and enable semantic computing at scale.
2) It describes how distributional semantic models can be used to represent word meanings based on their linguistic contexts, allowing semantic knowledge bases to be automatically constructed from large text corpora.
3) The author proposes a schema-agnostic approach using distributional semantics to enable querying databases without prior knowledge of schemas, addressing problems of vocabulary and structural differences between queries and data.
Effective Semantics for Engineering NLP Systems
Provide a synthesis of the emerging representation trends behind NLP systems.
Shift in perspective:
Effective engineering (task driven, scalable) instead of sound formalism.
Best-effort representation.
Knowledge Graphs (Frege revisited)
Information Extraction & Text Classification
Distributional Semantic Models
Knowledge Graphs & Distributional Semantics
(Distributional-Relational Models)
Applications of DRMs
KG Completion
Semantic Parsing
Natural Language Inference
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.PadmapriyaIJET - International Journal of Engineering and Techniques
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.Semantic Relation Classification: Task Formalisation and Refinement
The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded,
allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.
Building AI Applications using Knowledge Graphs
This document provides an overview of building AI applications using knowledge graphs. It discusses the goals of the tutorial, which are to provide a broad view of multiple perspectives on knowledge graphs and show how knowledge graphs can form the foundation for building AI systems. The tutorial focuses on contemporary and emerging perspectives through exemplar approaches and infrastructures, rather than providing an exhaustive survey. It also notes that the tutorial is not a standard academic tutorial and takes a big picture view rather than being a comprehensive survey.
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
The document describes the Schema-Agnostic Queries (SAQ-2015) challenge which aims to support querying over complex databases without needing to understand the underlying schema. It presents a test collection for evaluating systems on their ability to answer schema-agnostic queries over linked data. The test collection contains schema-agnostic queries mapped to equivalent SPARQL queries along with categorized mappings. An analysis finds the test collection expresses a variety of mapping types, data model mappings, compositional mappings, and query operations.
Ir 01
This document provides an overview of an information retrieval course. The course will cover topics related to information retrieval models, techniques, and systems. Students will complete exams, assignments, and a major project to build a search engine using both text-based and semantic retrieval techniques. The document defines key concepts in information retrieval and discusses different types of information retrieval systems and techniques.
Semantic Perspectives for Contemporary Question Answering Systems
This document summarizes an academic presentation about semantic perspectives for contemporary question answering systems. It discusses multiple perspectives of semantic representation including facts versus definitions and temporality. It also outlines lightweight semantic representation using RDF and distributional semantics. The document then discusses knowledge graph extraction from text including taxonomy extraction, n-ary relation extraction, and argumentation representation. It concludes with an overview of querying knowledge graphs using distributional semantics and semantic approximation as core operations.
Information_Retrieval_Models_Nfaoui_El_Habib
This document provides an overview of information retrieval models, including vector space models, TF-IDF, Doc2Vec, and latent semantic analysis. It begins with basic concepts in information retrieval like document indexing and relevance scoring. Then it discusses vector space models and how documents and queries are represented as vectors. TF-IDF weighting is explained as assigning higher weight to rare terms. Doc2Vec is introduced as an extension of word2vec to learn document embeddings. Latent semantic analysis uses singular value decomposition to project documents to a latent semantic space. Implementation details and examples are provided for several models.
Unit 2(knowledge)
The document discusses knowledge representation in artificial intelligence. It defines knowledge as information used to solve problems, including facts, concepts, procedures, models and examples. Knowledge representation schemes include declarative, procedural, inheritable, inferential, relational and heuristic knowledge. Facts can be represented internally in a computer using logic or rules. Propositional calculus and predicate logic are also discussed as methods for knowledge representation, along with inference rules and predicates. The key aspects of knowledge acquisition and creation are outlined.
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
To optimally interpret most natural language queries, it is necessary to understand the phrases, entities, commands, and relationships represented or implied within the search. Knowledge graphs serve as useful instantiations of ontologies which can help represent this kind of knowledge within a domain.
In this talk, we'll walk through techniques to build knowledge graphs automatically from your own domain-specific content, how you can update and edit the nodes and relationships, and how you can seamlessly integrate them into your search solution for enhanced query interpretation and semantic search. We'll have some fun with some of the more search-centric use cased of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "bbq near haystack" into
{ filter:["doc_type":"restaurant"], "query": { "boost": { "b": "recip(geodist(38.034780,-78.486790),1,1000,1000)", "query": "bbq OR barbeque OR barbecue" } } }
We'll also specifically cover use of the Semantic Knowledge Graph, a particularly interesting knowledge graph implementation available within Apache Solr that can be auto-generated from your own domain-specific content and which provides highly-nuanced, contextual interpretation of all of the terms, phrases and entities within your domain. We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding within your search engine.
How To Make Linked Data More than Data
This document discusses how adding formal semantics to linked open data can make it more useful and powerful. It describes how existing linked data lacks formal semantics, limiting its capabilities. The document proposes two approaches: 1) Enriching linked data schemas using ontology matching techniques to capture relationships between datasets. 2) Developing a system called LOQUS that can perform federated queries across multiple linked datasets by decomposing queries and merging results. This would allow queries without needing intimate knowledge of each dataset's structure.
Continuous Learning Algorithms - a Research Proposal Paper
General software intelligences are still held to be outside our current capacity to build. While the definition of intelligence which we apply to machine learning and artificial intelligence generally has expanded over time as our practical computational scales increase, little exploration has been conducted around the other aspect of intelligence, which is the capacity to constantly learn and improve through interaction with the environment. If we are to define a software intelligence as an algorithm that is capable of interacting with its environment and adapting to it over time, then this exploration is critical to the development of such a system.
This body of research will attempt to make the first step into the area of continual feedback for a machine learning algorithm, evaluating it against an area which has traditionally been difficult for computers to emulate – Name Matching Analysis. If a machine learning algorithm can be used to ‘tune’ a soft-search name matching algorithm based on continual feedback generated from the results of that engine and the feedback provided by human experts, then this technique of constant feedback not only has immediate practical value but could be explored further in more ambitious research projects.
Introducing grounded theory
This document outlines the structure and objectives of a workshop on Grounded Theory. The workshop will introduce participants to the basic principles and procedures of Grounded Theory. It will cover topics such as the origins and theoretical underpinnings of Grounded Theory, how to conduct Grounded Theory research through procedures like open coding, theoretical sampling and memo writing, and putting the analysis together to build a theory. By the end of the workshop, participants will understand and have hands-on experience with Grounded Theory to help inform their own research.
Text Analytics for Semantic Computing
Text Analytics for Semantic Computing - the good, the bad and the ugly, tutorial presentation at ICSC 2008
http://icsc.eecs.uci.edu/tutorial1.html
Ijetcas14 624
This document summarizes a survey on string similarity matching search techniques. It discusses how string similarity matching is used to find relevant information in text collections. The document reviews different algorithms for string matching, including edit distance, NR-grep, n-grams, and approaches based on hashing and locality-sensitive hashing. It analyzes techniques like pattern matching, threshold-based joins, and vector representations. The goal is to present an overview of the field and compare algorithm performance for similarity searches.
Technical Whitepaper: A Knowledge Correlation Search Engine
For the technically oriented reader, this brief paper describes the technical foundation of the Knowledge Correlation Search Engine - patented by Make Sence, Inc.
The impact of standardized terminologies and domain-ontologies in multilingua...
The impact of standardized terminologies and domain-ontologies in multilingua...AIMS (Agricultural Information Management Standards)
The document discusses the impact of standardized terminologies and domain ontologies in multilingual information processing. It outlines how natural language processing (NLP) techniques can be used to semi-automatically populate ontologies by extracting information from text. Integrating knowledge from ontologies, NLP tools, and subject experts allows for more effective information access and management in an organization.What's hot (19)
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
Semantic Relation Classification: Task Formalisation and Refinement
Semantic Relation Classification: Task Formalisation and Refinement
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Semantic Perspectives for Contemporary Question Answering Systems
Semantic Perspectives for Contemporary Question Answering Systems
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
Continuous Learning Algorithms - a Research Proposal Paper
Continuous Learning Algorithms - a Research Proposal Paper
Technical Whitepaper: A Knowledge Correlation Search Engine
Technical Whitepaper: A Knowledge Correlation Search Engine
The impact of standardized terminologies and domain-ontologies in multilingua...
The impact of standardized terminologies and domain-ontologies in multilingua...
Similar to Concepts as Action-Oriented as 'Search'
020610
The document discusses challenges with digital libraries and semantic support. It notes that human information processing is limited and computers do not understand natural language queries. Semantic support adds metadata to information to help humans and computers work together by giving meaning to data. However, communication between digital libraries remains a challenge. While some projects have created ontologies and cross-concordances, researchers often prefer to add their own tags due to difficulties making comprehensive ontologies for research data content. The literature on these topics is very technical and may not effectively communicate with scholars.
Searching for patterns in crowdsourced information
This document introduces crowdsourcing and discusses discovering patterns in crowdsourced data. It discusses defining the context of volunteered information on the internet in order to understand relationships between data. A network model is proposed where different types of context define nodes and relationships between context determine edges. Properties of small world networks are discussed including how they could be used to model relationships between crowdsourced data and evaluate data quality. Finally, applications to search ranking, privacy and security are briefly mentioned.
Probabilistic Topic Models
Presents Natural Language Processing (NLP) algorithms for for Bay Area NLP reading group. Survey of Probabilistic Topic Modeling such as Latent Dirichlet Allocation (LDA). Includes practical references explaining the algorithm along with software libraries for Python, Spark, and R.
The Role Of Ontology In Modern Expert Systems Dallas 2008
The document discusses the role of ontologies in modern expert system development. It provides background on expert systems and ontologies, explaining that ontologies define domains of knowledge and are used to encapsulate domain knowledge for use in expert systems. The document outlines the process of developing ontologies, including identifying concepts and relationships in a domain. It also provides an example of an expert system called SINFERS that uses ontologies to select soil property prediction models.
Contextual Ontology Alignment - ESWC 2011
This document describes a new approach called BLOOMS+ for performing contextual ontology alignment of Linked Open Data datasets with an upper ontology. BLOOMS+ leverages contextual information from Wikipedia category hierarchies to compute similarities between concepts in different ontologies. It computes class similarity, contextual similarity between super classes, and an overall similarity to determine equivalence or subsumption relationships between concepts during alignment. The approach is evaluated on aligning several LOD ontologies to the PROTON upper ontology, outperforming existing solutions. Future work involves extending this approach to utilize more contextual sources and enable seamless querying across aligned datasets.
Equation 2.doc
The document provides an overview of two approaches to enabling computers to understand the content of the World Wide Web: (1) the structuring solution, which aims to impose structure on web pages similar to databases, and (2) the learning solution, which aims to make computers smarter through machine learning. It then summarizes three examples of researchers pursuing the learning solution, including using naive Bayes and maximum entropy classifiers to categorize web pages. The researchers were able to achieve 60% accuracy for page classification using naive Bayes but saw improved performance from maximum entropy and adding additional heuristics.
Ontology
study or concern about what kinds of things exist
what entities there are in the universe.
the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.
Summary Of Defending Against The Indefensible Essay
Postman suggests that language can be manipulated, and provides seven principles of critical thinking to help avoid manipulation: definitions can be biased; questions help understand purpose and bias; simplicity obscures; metaphors shape thought; words can conceal ideas; style influences perception; and media are non-neutral. Understanding these principles helps recognize manipulation in language used by media, politicians, and others.
KMD 1001 Design Brief and Ontology Task
The document outlines the assignments for a course on knowledge media design, including an ontology task defining key concepts and their relationships, presentations analyzing frameworks and design challenges, and a final report proposing a solution to a design challenge. It provides examples of framework visualizations and discussions of concepts like design, knowledge, and media to guide the assignments. Students are asked to analyze relationships between pairs of concepts and propose innovative approaches to addressing knowledge media design problems.
4KN Editted 2012.ppt
This document discusses knowledge representation techniques. It begins by defining different types of knowledge like procedural, declarative, meta, and heuristic knowledge. It then discusses different knowledge representation approaches like pictures, graphs, networks, frames, rules, and semantic networks. Pictures represent structural knowledge but are difficult for computers to interpret. Graphs and networks represent relationships well. Rules relate known information to conclusions. Semantic networks use nodes for objects and arcs for relationships. Frames represent stereotypical knowledge using slots. The document also discusses formal knowledge representation techniques like facts represented as propositions, rules using antecedents and consequents, and predicate logic.
Politics and Pragmatism in Scientific Ontology Construction
My talk at the Inconsistency Robustness 2011 Workshop...as usual, trying to fit the entire universe into one paper.
Word Embedding In IR
This document is a thesis that proposes using word embeddings to improve information retrieval by addressing term mismatch issues. It discusses word2vec, a technique for learning word embeddings from large text corpora that capture semantic relationships between words. The thesis proposes two approaches: 1) incorporating word embedding similarities into a probabilistic language model for retrieval and 2) a vector space model. Due to time constraints, only the first approach is implemented, which integrates word embeddings into ALMasri and Chevallet's probabilistic language model. Experiments are conducted to evaluate the impact of using semantic features from word embeddings on retrieval effectiveness.
SMalL - Semantic Malware Log Based Reporter
In this paper we present the SMalL Ontology for malicious software classification, SMalL Java Application for antivirus systems comparison and the SMalL knowledge based file format for malware related attacks. We believe that our ontology is able to aid the development of malware prevention software by offering a common knowledge base and a clear classification of the existing malicious software. The application is a prototype regarding how this ontology might be used in conjunction with known antivirus capabilities to offer a comprehensive comparison.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
340
This document presents a cognitive framework for understanding systems thinking in engineering. It discusses previous work that has identified behaviors and competencies of systems thinkers but lacked a rigorous cognitive understanding. The authors map the identified competencies to relevant cognitive psychology concepts to establish systems thinking as an exercise in cognition. This mapping forms the basis to develop a deeper understanding using insights from fields like cognitive science and education. It suggests future research directions could apply the theories of cognitive psychologist Reuven Feuerstein to create educational methods to develop the cognitive skills and structures required for effective systems thinking. Overall, the summary establishes a connection between engineering systems thinking and cognitive psychology to facilitate a more formal and prescriptive methodology.
The basics of ontologies
The document discusses the basics of ontologies, including their origin in philosophy, definitions, types, benefits and application areas. Some key points are:
- An ontology is a formal specification of a conceptualization used to help humans and programs share knowledge. It establishes a shared vocabulary for exchanging information.
- Ontologies describe domain knowledge and provide an agreed-upon understanding of a domain through concepts and relations. They help solve problems of ambiguity and enable knowledge sharing.
- Ontologies benefit applications like information retrieval, digital libraries, knowledge engineering and natural language processing by facilitating semantic search and integration of data.
Grounded Theory
Grounded theory is a qualitative research method that uses a systematic set of procedures to develop an inductively derived grounded theory about a phenomenon. The key aspects of grounded theory include:
1) Beginning analysis by making distinctions and categorizing data to develop dimensions, categories, and their relationships.
2) Creating codes to analyze categories and relationships and discovering connections through constant comparison across data.
3) Furthering analysis through iterative coding, memo-writing to refine categories and hypotheses, and theoretical sampling to collect more data to develop the emerging theory.
Representation and organization of knowledge in memory
This document summarizes different theories of how knowledge is organized in memory. It discusses declarative versus procedural knowledge, with declarative being "knowing that" facts and procedural being "knowing how" to perform skills. Concepts, categories, networks and schemas are reviewed as ways to organize declarative knowledge. Prototype and exemplar theories are described as alternatives to defining categories solely based on necessary features. The ACT-R model integrates propositional networks to represent declarative knowledge and production systems for procedural knowledge.
8 princípios de arquitetura da informação
This document discusses eight principles of information architecture that guide the design of website structures. The principles are: 1) Treat content as objects with attributes and behaviors; 2) Create pages that offer meaningful choices focused on a task; 3) Only show enough information to understand what information is available deeper in the site; 4) Describe categories by showing exemplar content; 5) Assume half of visitors come through non-home pages so those pages must provide context; 6) Offer multiple ways to browse content; 7) Keep navigation schemes focused rather than mixing unrelated content; and 8) Assume content will exponentially grow so structures must accommodate growth. The principles are general guidelines informed by research that help design structures while allowing for interpretation specific to individual projects
Temple University Digital Scholarship Center: Model of the Month Club: Septem...
Overview of a digital humanities quantitative modeling project. This meeting discussed Underwood et al.'s genre classifier for HathiTrust.
Similar to Concepts as Action-Oriented as 'Search' (20)
Searching for patterns in crowdsourced information
Searching for patterns in crowdsourced information
The Role Of Ontology In Modern Expert Systems Dallas 2008
The Role Of Ontology In Modern Expert Systems Dallas 2008
Summary Of Defending Against The Indefensible Essay
Summary Of Defending Against The Indefensible Essay
Politics and Pragmatism in Scientific Ontology Construction
Politics and Pragmatism in Scientific Ontology Construction
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
Representation and organization of knowledge in memory
Representation and organization of knowledge in memory
Temple University Digital Scholarship Center: Model of the Month Club: Septem...
Temple University Digital Scholarship Center: Model of the Month Club: Septem...
Recently uploaded
Finale of the Year: Apply for Next One!
Presentation for the event called "Finale of the Year: Apply for Next One!" organized by GDSC PJATK
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
dbms calicut university B. sc Cs 4th sem.pdf
Its a seminar ppt on database management system using sql
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Recently uploaded (20)
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Concepts as Action-Oriented as 'Search'
- 20. Thank You ! Questions