Soumettre la recherche
Mettre en ligne
Concepts as Action-Oriented as 'Search'
•
Télécharger en tant que PPT, PDF
•
0 j'aime
•
479 vues
mahmad
Suivre
Technologie
Formation
Signaler
Partager
Signaler
Partager
1 sur 20
Télécharger maintenant
Recommandé
Knowledge Representation in Artificial intelligence
Knowledge Representation in Artificial intelligence
Knowledge Representation in Artificial intelligence
Yasir Khan
DC-2013 Tutorial, September 2, 2013
Introduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and Terminology
Steven Miller
The evolution of data environments towards the growth in the size, complexity, dy- namicity and decentralisation (SCoDD) of schemas drastically impacts contemporary data management. The SCoDD trend emerges as a central data management concern in Big Data scenarios, where users and applications have a demand for more complete data, produced by independent data sources, under different semantic assumptions and contexts of use. Most Database Management Systems (DBMSs) today target a closed communication scenario, where the symbolic schema of the database is known a priori by the database user, which is able to interpret it in an unambiguous way. The context in which the data is consumed and produced is well-defined and it is typically the same context in which the data was created. In contrast, data management under the SCoDD conditions target an open communication scenario where the symbolic system of the database is unknown by the user and multiple interpretation contexts are possible. In this case the database can be created under a different context from the database user. The emergence of this new data environment demands the revisit of the semantic assumptions behind databases and the design of data access mechanisms which can support semantically heterogeneous (open communication) data environments. This work aims at filling this gap by proposing a complementary semantic model for databases, based on distributional semantic models. Distributional semantics provides a complementary perspective to the formal perspective of database semantics, which supports semantic approximation as a first-class database operation. Differently from models which describe uncertain and incomplete data or probabilistic databases, distributional- relational models focuses on the construction of conceptual approximation approaches for databases, supported by a comprehensive semantic model automatically built from large-scale unstructured data external to the database, which serves as a semantic/com- monsense knowledge base. The semantic model can be used to support schema-agnosticqueries, i.e. abstracting the data consumer from a specific conceptualization behind the data. The proposed distributional-relational semantic model is supported by a distributional structured vector space model, named τ −Space, which represents structured data under a distributional semantic model representation which, in coordination with a query plan- ning approach, supports a schema-agnostic query mechanism for large-schema databases. The query mechanism is materialized in the Treo query engine and is evaluated using schema-agnostic natural language queries. The evaluation of the query mechanism confirms that distributional semantics provides a high-recall, medium-high precision, and low maintainability solution to cope with the abstraction and conceptual-level differences in schema-agnostic queries over largeschema/ schema-less open domain dataset
Schema-agnositc queries over large-schema databases: a distributional semanti...
Schema-agnositc queries over large-schema databases: a distributional semanti...
Andre Freitas
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.
Context, Perspective, and Generalities in a Knowledge Ontology
Context, Perspective, and Generalities in a Knowledge Ontology
Mike Bergman
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.
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...
Andre Freitas
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.
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC
Valentina Presutti
Knowledge Graphs
Open IE tutorial 2018
Open IE tutorial 2018
Andre Freitas
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.
Different Semantic Perspectives for Question Answering Systems
Different Semantic Perspectives for Question Answering Systems
Andre Freitas
Recommandé
Knowledge Representation in Artificial intelligence
Knowledge Representation in Artificial intelligence
Knowledge Representation in Artificial intelligence
Yasir Khan
DC-2013 Tutorial, September 2, 2013
Introduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and Terminology
Steven Miller
The evolution of data environments towards the growth in the size, complexity, dy- namicity and decentralisation (SCoDD) of schemas drastically impacts contemporary data management. The SCoDD trend emerges as a central data management concern in Big Data scenarios, where users and applications have a demand for more complete data, produced by independent data sources, under different semantic assumptions and contexts of use. Most Database Management Systems (DBMSs) today target a closed communication scenario, where the symbolic schema of the database is known a priori by the database user, which is able to interpret it in an unambiguous way. The context in which the data is consumed and produced is well-defined and it is typically the same context in which the data was created. In contrast, data management under the SCoDD conditions target an open communication scenario where the symbolic system of the database is unknown by the user and multiple interpretation contexts are possible. In this case the database can be created under a different context from the database user. The emergence of this new data environment demands the revisit of the semantic assumptions behind databases and the design of data access mechanisms which can support semantically heterogeneous (open communication) data environments. This work aims at filling this gap by proposing a complementary semantic model for databases, based on distributional semantic models. Distributional semantics provides a complementary perspective to the formal perspective of database semantics, which supports semantic approximation as a first-class database operation. Differently from models which describe uncertain and incomplete data or probabilistic databases, distributional- relational models focuses on the construction of conceptual approximation approaches for databases, supported by a comprehensive semantic model automatically built from large-scale unstructured data external to the database, which serves as a semantic/com- monsense knowledge base. The semantic model can be used to support schema-agnosticqueries, i.e. abstracting the data consumer from a specific conceptualization behind the data. The proposed distributional-relational semantic model is supported by a distributional structured vector space model, named τ −Space, which represents structured data under a distributional semantic model representation which, in coordination with a query plan- ning approach, supports a schema-agnostic query mechanism for large-schema databases. The query mechanism is materialized in the Treo query engine and is evaluated using schema-agnostic natural language queries. The evaluation of the query mechanism confirms that distributional semantics provides a high-recall, medium-high precision, and low maintainability solution to cope with the abstraction and conceptual-level differences in schema-agnostic queries over largeschema/ schema-less open domain dataset
Schema-agnositc queries over large-schema databases: a distributional semanti...
Schema-agnositc queries over large-schema databases: a distributional semanti...
Andre Freitas
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.
Context, Perspective, and Generalities in a Knowledge Ontology
Context, Perspective, and Generalities in a Knowledge Ontology
Mike Bergman
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.
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...
Andre Freitas
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.
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC
Valentina Presutti
Knowledge Graphs
Open IE tutorial 2018
Open IE tutorial 2018
Andre Freitas
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.
Different Semantic Perspectives for Question Answering Systems
Different Semantic Perspectives for Question Answering Systems
Andre Freitas
In this talk we will summarise some of the detectable trends on AI beyond deep learning. We will focus on the current transition from deep learning to deep semantics, describing the enabling infrastructures, challenges and opportunities in the construction of the next generation AI systems. The talk will focus on Natural Language Processing (NLP) as an AI sub-domain and will link to the research at the AI Systems Lab at the University of Manchester.
AI Beyond Deep Learning
AI Beyond Deep Learning
Andre Freitas
Semantics at Scale: A Distributional Approach
Semantics at Scale: A Distributional Approach
Semantics at Scale: A Distributional Approach
Andre Freitas
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
Effective Semantics for Engineering NLP Systems
Effective Semantics for Engineering NLP Systems
Andre Freitas
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.
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
IJET - International Journal of Engineering and Techniques
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.
Semantic Relation Classification: Task Formalisation and Refinement
Semantic Relation Classification: Task Formalisation and Refinement
Andre Freitas
Goals of this Tutorial: Provide a broad view of the multiple perspectives underlying knowledge graphs. Show knowledge graphs as a foundation for building AI systems. Method: Focus on the contemporary and emerging perspectives. Sampling exemplar approaches and infrastructures on each of these emerging perspectives (not an exhaustive survey).
Building AI Applications using Knowledge Graphs
Building AI Applications using Knowledge Graphs
Andre Freitas
The Challenge in a Nutshell To create a query mechanism that semantically matches schema-agnostic user queries to knowledge base elements The Goal To support easy querying over complex databases with large schemata, relieving users from the need to understand the formal representation of the data Relevance The increase in the size and in the semantic heterogeneity of database schemas are bringing new requirements for users querying and searching structured data. At this scale it can become unfeasible for data consumers to be familiar with the representation of the data in order to query it. At the center of this discussion is the semantic gap between users and databases, which becomes more central as the scale and complexity of the data grows. Addressing this gap is a fundamental part of the Semantic Web vision. Schema-agnostic query mechanisms aim at allowing users to be abstracted from the representation of the data, supporting the automatic matching between queries and databases. This challenge aims at emphasizing the role of schema-agnosticism as a key requirement for contemporary database management, by providing a test collection for evaluating flexible query and search systems over structured data in terms of their level of schema-agnosticism (i.e. their ability to map a query issued with the user terminology and structure, mapping it to the dataset vocabulary). The challenge is instantiated in the context of Semantic Web datasets.
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Andre Freitas
ir system
Ir 01
Ir 01
Mohammed Romi
Talk at JAIST (December 2016)
Semantic Perspectives for Contemporary Question Answering Systems
Semantic Perspectives for Contemporary Question Answering Systems
Andre Freitas
These lecture slides describes the information retrieval models.
Information_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_Habib
El Habib NFAOUI
A.I topic
Unit 2(knowledge)
Unit 2(knowledge)
Ashish Nayak
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.
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
OpenSource Connections
How To Make Linked Data More than Data
How To Make Linked Data More than Data
Amit Sheth
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.
Continuous Learning Algorithms - a Research Proposal Paper
Continuous Learning Algorithms - a Research Proposal Paper
tjb910
Slides for a workshop conducted at the University of Graz (April 2016).
Introducing grounded theory
Introducing grounded theory
Achilleas Kostoulas
Text Analytics for Semantic Computing - the good, the bad and the ugly, tutorial presentation at ICSC 2008 http://icsc.eecs.uci.edu/tutorial1.html
Text Analytics for Semantic Computing
Text Analytics for Semantic Computing
Meena Nagarajan
A Survey on String Similarity Matching Search Techniques
Ijetcas14 624
Ijetcas14 624
Iasir Journals
For the technically oriented reader, this brief paper describes the technical foundation of the Knowledge Correlation Search Engine - patented by Make Sence, Inc.
Technical Whitepaper: A Knowledge Correlation Search Engine
Technical Whitepaper: A Knowledge Correlation Search Engine
s0P5a41b
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)
Presentation 020610 New media
020610
020610
judithgulpers
Searching for patterns in crowdsourced information
Searching for patterns in crowdsourced information
Silvia Puglisi
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.
Probabilistic Topic Models
Probabilistic Topic Models
Steve Follmer
Contenu connexe
Tendances
In this talk we will summarise some of the detectable trends on AI beyond deep learning. We will focus on the current transition from deep learning to deep semantics, describing the enabling infrastructures, challenges and opportunities in the construction of the next generation AI systems. The talk will focus on Natural Language Processing (NLP) as an AI sub-domain and will link to the research at the AI Systems Lab at the University of Manchester.
AI Beyond Deep Learning
AI Beyond Deep Learning
Andre Freitas
Semantics at Scale: A Distributional Approach
Semantics at Scale: A Distributional Approach
Semantics at Scale: A Distributional Approach
Andre Freitas
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
Effective Semantics for Engineering NLP Systems
Effective Semantics for Engineering NLP Systems
Andre Freitas
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.
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
IJET - International Journal of Engineering and Techniques
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.
Semantic Relation Classification: Task Formalisation and Refinement
Semantic Relation Classification: Task Formalisation and Refinement
Andre Freitas
Goals of this Tutorial: Provide a broad view of the multiple perspectives underlying knowledge graphs. Show knowledge graphs as a foundation for building AI systems. Method: Focus on the contemporary and emerging perspectives. Sampling exemplar approaches and infrastructures on each of these emerging perspectives (not an exhaustive survey).
Building AI Applications using Knowledge Graphs
Building AI Applications using Knowledge Graphs
Andre Freitas
The Challenge in a Nutshell To create a query mechanism that semantically matches schema-agnostic user queries to knowledge base elements The Goal To support easy querying over complex databases with large schemata, relieving users from the need to understand the formal representation of the data Relevance The increase in the size and in the semantic heterogeneity of database schemas are bringing new requirements for users querying and searching structured data. At this scale it can become unfeasible for data consumers to be familiar with the representation of the data in order to query it. At the center of this discussion is the semantic gap between users and databases, which becomes more central as the scale and complexity of the data grows. Addressing this gap is a fundamental part of the Semantic Web vision. Schema-agnostic query mechanisms aim at allowing users to be abstracted from the representation of the data, supporting the automatic matching between queries and databases. This challenge aims at emphasizing the role of schema-agnosticism as a key requirement for contemporary database management, by providing a test collection for evaluating flexible query and search systems over structured data in terms of their level of schema-agnosticism (i.e. their ability to map a query issued with the user terminology and structure, mapping it to the dataset vocabulary). The challenge is instantiated in the context of Semantic Web datasets.
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Andre Freitas
ir system
Ir 01
Ir 01
Mohammed Romi
Talk at JAIST (December 2016)
Semantic Perspectives for Contemporary Question Answering Systems
Semantic Perspectives for Contemporary Question Answering Systems
Andre Freitas
These lecture slides describes the information retrieval models.
Information_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_Habib
El Habib NFAOUI
A.I topic
Unit 2(knowledge)
Unit 2(knowledge)
Ashish Nayak
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.
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
OpenSource Connections
How To Make Linked Data More than Data
How To Make Linked Data More than Data
Amit Sheth
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.
Continuous Learning Algorithms - a Research Proposal Paper
Continuous Learning Algorithms - a Research Proposal Paper
tjb910
Slides for a workshop conducted at the University of Graz (April 2016).
Introducing grounded theory
Introducing grounded theory
Achilleas Kostoulas
Text Analytics for Semantic Computing - the good, the bad and the ugly, tutorial presentation at ICSC 2008 http://icsc.eecs.uci.edu/tutorial1.html
Text Analytics for Semantic Computing
Text Analytics for Semantic Computing
Meena Nagarajan
A Survey on String Similarity Matching Search Techniques
Ijetcas14 624
Ijetcas14 624
Iasir Journals
For the technically oriented reader, this brief paper describes the technical foundation of the Knowledge Correlation Search Engine - patented by Make Sence, Inc.
Technical Whitepaper: A Knowledge Correlation Search Engine
Technical Whitepaper: A Knowledge Correlation Search Engine
s0P5a41b
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)
Tendances
(19)
AI Beyond Deep Learning
AI Beyond Deep Learning
Semantics at Scale: A Distributional Approach
Semantics at Scale: A Distributional Approach
Effective Semantics for Engineering NLP Systems
Effective Semantics for Engineering NLP Systems
[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
Building AI Applications using Knowledge Graphs
Building AI Applications using Knowledge Graphs
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Ir 01
Ir 01
Semantic Perspectives for Contemporary Question Answering Systems
Semantic Perspectives for Contemporary Question Answering Systems
Information_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_Habib
Unit 2(knowledge)
Unit 2(knowledge)
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey Grainger
How To Make Linked Data More than Data
How To Make Linked Data More than Data
Continuous Learning Algorithms - a Research Proposal Paper
Continuous Learning Algorithms - a Research Proposal Paper
Introducing grounded theory
Introducing grounded theory
Text Analytics for Semantic Computing
Text Analytics for Semantic Computing
Ijetcas14 624
Ijetcas14 624
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...
Similaire à Concepts as Action-Oriented as 'Search'
Presentation 020610 New media
020610
020610
judithgulpers
Searching for patterns in crowdsourced information
Searching for patterns in crowdsourced information
Silvia Puglisi
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.
Probabilistic Topic Models
Probabilistic Topic Models
Steve Follmer
This is the corrected set of slides from my October 2008 ORF presentation of the same title. - JM
The Role Of Ontology In Modern Expert Systems Dallas 2008
The Role Of Ontology In Modern Expert Systems Dallas 2008
Jason Morris
A method of automated ontology alignment using contextual information and taxonomies.
Contextual Ontology Alignment - ESWC 2011
Contextual Ontology Alignment - ESWC 2011
Mariana Damova, Ph.D
Equation 2.doc
Equation 2.doc
butest
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.
Ontology
Ontology
Ahmed Tememe
✍️
Summary Of Defending Against The Indefensible Essay
Summary Of Defending Against The Indefensible Essay
Brenda Zerr
KMD 1001 Design Brief and Ontology Task
KMD 1001 Design Brief and Ontology Task
Stian Håklev
prolog lab material
4KN Editted 2012.ppt
4KN Editted 2012.ppt
HenokGetachew15
My talk at the Inconsistency Robustness 2011 Workshop...as usual, trying to fit the entire universe into one paper.
Politics and Pragmatism in Scientific Ontology Construction
Politics and Pragmatism in Scientific Ontology Construction
Mike Travers
Word Embedding In IR
Word Embedding In IR
Bhaskar Chatterjee
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.
SMalL - Semantic Malware Log Based Reporter
SMalL - Semantic Malware Log Based Reporter
Stefan Prutianu
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
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
Mauro Dragoni
Systems Framework
340
340
Baffajo Beita
The basics of ontologies
The basics of ontologies
AIMS (Agricultural Information Management Standards)
Grounded Theory
Grounded Theory
litdoc1999
Representation and organization of knowledge in memory
Representation and organization of knowledge in memory
Maria Angela Leabres-Diopol
8 princípios de arquitetura da informação
8 princípios de arquitetura da informação
Jonathan Prateat
Overview of a digital humanities quantitative modeling project. This meeting discussed Underwood et al.'s genre classifier for HathiTrust.
Temple University Digital Scholarship Center: Model of the Month Club: Septem...
Temple University Digital Scholarship Center: Model of the Month Club: Septem...
Liz Rodrigues
Similaire à Concepts as Action-Oriented as 'Search'
(20)
020610
020610
Searching for patterns in crowdsourced information
Searching for patterns in crowdsourced information
Probabilistic Topic Models
Probabilistic Topic Models
The Role Of Ontology In Modern Expert Systems Dallas 2008
The Role Of Ontology In Modern Expert Systems Dallas 2008
Contextual Ontology Alignment - ESWC 2011
Contextual Ontology Alignment - ESWC 2011
Equation 2.doc
Equation 2.doc
Ontology
Ontology
Summary Of Defending Against The Indefensible Essay
Summary Of Defending Against The Indefensible Essay
KMD 1001 Design Brief and Ontology Task
KMD 1001 Design Brief and Ontology Task
4KN Editted 2012.ppt
4KN Editted 2012.ppt
Politics and Pragmatism in Scientific Ontology Construction
Politics and Pragmatism in Scientific Ontology Construction
Word Embedding In IR
Word Embedding In IR
SMalL - Semantic Malware Log Based Reporter
SMalL - Semantic Malware Log Based Reporter
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
340
340
The basics of ontologies
The basics of ontologies
Grounded Theory
Grounded Theory
Representation and organization of knowledge in memory
Representation and organization of knowledge in memory
8 princípios de arquitetura da informação
8 princípios de arquitetura da informação
Temple University Digital Scholarship Center: Model of the Month Club: Septem...
Temple University Digital Scholarship Center: Model of the Month Club: Septem...
Dernier
Read about the journey the Adobe Experience Manager team has gone through in order to become and scale API-first throughout the organisation.
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Radu Cotescu
As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc
MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Miguel Araújo
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
The Digital Insurer
In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Drew Madelung
MINDCTI Revenue Release Quarter 1 2024
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
MIND CTI
With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
Stay safe, grab a drink and join us virtually for our upcoming "GenAI Risks & Security" Meetup to hear about how to uncover critical GenAI risks and vulnerabilities, AI security considerations in every company, and how a CISO should navigate through GenAI Risks.
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
lior mazor
Uncertainty, Acting under uncertainty, Basic probability notation, Bayes’ Rule,
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
Khushali Kathiriya
I've been in the field of "Cyber Security" in its many incarnations for about 25 years. In that time I've learned some lessons, some the hard way. Here are my slides presented at BSides New Orleans in April 2024.
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Rafal Los
Increase engagement and revenue with Muvi Live Paywall! In this presentation, we will explore the five key benefits of using Muvi Live Paywall to monetize your live streams. You'll learn how Muvi Live Paywall can help you: Monetize your live content easily: Set up pay-per-view access to your live streams and start generating revenue from your content. Increase audience engagement: Provide exclusive, premium content behind the paywall to keep your viewers engaged. Gain valuable viewer insights: Track viewer data and analytics to better understand your audience and tailor your content accordingly. Reduce content piracy: Muvi Live Paywall's security features help protect your content from unauthorized distribution. Streamline your workflow: The all-in-one platform simplifies the process of managing and monetizing your live streams. With Muvi Live Paywall, you can take control of your live stream monetization and create a sustainable business model for your content. Learn more about Muvi Live Paywall and start generating revenue from your live streams today!
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Roshan Dwivedi
ICT role in 21 century education. How to ICT help in education
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
jfdjdjcjdnsjd
The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
apidays
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
The Digital Insurer
This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
Boston Institute of Analytics
Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
DianaGray10
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
The Digital Insurer
Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc
Breathing New Life into MySQL Apps With Advanced Postgres Capabilities
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
RTylerCroy
This project focuses on implementing real-time object detection using Raspberry Pi and OpenCV. Real-time object detection is a critical aspect of computer vision applications, allowing systems to identify and locate objects within a live video stream instantly.
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Khem
Dernier
(20)
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Concepts as Action-Oriented as 'Search'
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
Thank You !
Questions
Télécharger maintenant