This document summarizes Valentina Presutti's presentation on using frames for knowledge extraction and linked data. It discusses how frames can be used as units of meaning to reconcile knowledge from different sources. It provides background on frames and examples of how they can represent situations and relationships described in text. The document then outlines several projects from STLab that use a frame-based approach for tasks like knowledge extraction, relation extraction, and sentiment analysis. It discusses tools like FRED and Framester that perform frame-based knowledge extraction and integrate linguistic and factual knowledge through linked data.
This tutorial tries to answer the following questions:
What is the best practice for ontology reuse?
Is it fine to use external ontology entities to model my local entities?
Should I import the ontologies that I reuse?
What if I only need a part of an ontology?
What if an external ontology that I reused, changes?
This paper introduces a new human computation game called Jinx to address the knowledge acquisition bottleneck in word sense disambiguation (WSD). Jinx is an online cooperative word game where two anonymous players see the same word in context and must independently type the same replacement word to receive points. The game aims to efficiently generate high quality labeled data for training WSD algorithms by making the labeling process an enjoyable game. An evaluation using data from another project found that tags collected from Jinx players accurately captured the true sense of words in context.
Syntax is the study of how sentences are constructed in languages according to certain principles and processes. The goal of syntax is to construct a grammar that acts as a device for generating the sentences of the language being analyzed. For example, the sentence "the boy likes a girl" can be broken down into a noun phrase (NP) and verb phrase (VP) and further into constituent parts including a determiner (Det), noun (N), and verb (V).
Phrase Identification is one of the most critical and widely studied in Natural Language processing (NLP) tasks. Verb Phrase Identification within a sentence is very useful for a variety of application on NLP. One of the core enabling technologies required in NLP applications is a Morphological Analysis. This paper presents the Myanmar Verb Phrase Identification and Translation Algorithm and develops a Markov Model with Morphological Analysis. The system is based on Rule-Based Maximum Matching Approach. In Machine Translation, Large amount of information is needed to guide the translation process. Myanmar Language is inflected language and there are very few creations and researches of Lexicon in Myanmar, comparing to other language such as English, French and Czech etc. Therefore, this system is proposed Myanmar Verb Phrase identification and translation model based on Syntactic Structure and Morphology of Myanmar Language by using Myanmar- English bilingual lexicon. Markov Model is also used to reformulate the translation probability of Phrase pairs. Experiment results showed that proposed system can improve translation quality by applying morphological analysis on Myanmar Language.
1) A phrase is a group of words that functions as a noun, verb, adjective, or preposition within a sentence.
2) There are different types of phrases including noun phrases, verb phrases, adjective phrases, prepositional phrases, gerund phrases, and infinitive phrases.
3) Phrases are building blocks within the structure of a sentence, along with clauses.
The document discusses the K-nearest neighbor (K-NN) classifier, a machine learning algorithm where data is classified based on its similarity to its nearest neighbors. K-NN is a lazy learning algorithm that assigns data points to the most common class among its K nearest neighbors. The value of K impacts the classification, with larger K values reducing noise but possibly oversmoothing boundaries. K-NN is simple, intuitive, and can handle non-linear decision boundaries, but has disadvantages such as computational expense and sensitivity to K value selection.
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...James Hendler
IJCAI 16 keynote on the need to bring modern AI accomplishments of recent years into connection with the more traditional goals of symbolic AI (and vice versa).
grammaticality, deep & surface structure, and ambiguityDedew Deviarini
This document discusses English morphology and syntax. It covers several key topics:
1. What is syntax and syntactic structure, including parts of speech and phrase structure.
2. The difference between deep and surface structure, where deep structure is the underlying form and surface structure is the actual form after transformations.
3. Grammaticality, which refers to sentences that follow syntactic rules rather than other factors like meaning or truth.
4. Types of ambiguities, including lexical ambiguities due to ambiguous words, and structural ambiguities due to multiple possible syntactic trees.
This tutorial tries to answer the following questions:
What is the best practice for ontology reuse?
Is it fine to use external ontology entities to model my local entities?
Should I import the ontologies that I reuse?
What if I only need a part of an ontology?
What if an external ontology that I reused, changes?
This paper introduces a new human computation game called Jinx to address the knowledge acquisition bottleneck in word sense disambiguation (WSD). Jinx is an online cooperative word game where two anonymous players see the same word in context and must independently type the same replacement word to receive points. The game aims to efficiently generate high quality labeled data for training WSD algorithms by making the labeling process an enjoyable game. An evaluation using data from another project found that tags collected from Jinx players accurately captured the true sense of words in context.
Syntax is the study of how sentences are constructed in languages according to certain principles and processes. The goal of syntax is to construct a grammar that acts as a device for generating the sentences of the language being analyzed. For example, the sentence "the boy likes a girl" can be broken down into a noun phrase (NP) and verb phrase (VP) and further into constituent parts including a determiner (Det), noun (N), and verb (V).
Phrase Identification is one of the most critical and widely studied in Natural Language processing (NLP) tasks. Verb Phrase Identification within a sentence is very useful for a variety of application on NLP. One of the core enabling technologies required in NLP applications is a Morphological Analysis. This paper presents the Myanmar Verb Phrase Identification and Translation Algorithm and develops a Markov Model with Morphological Analysis. The system is based on Rule-Based Maximum Matching Approach. In Machine Translation, Large amount of information is needed to guide the translation process. Myanmar Language is inflected language and there are very few creations and researches of Lexicon in Myanmar, comparing to other language such as English, French and Czech etc. Therefore, this system is proposed Myanmar Verb Phrase identification and translation model based on Syntactic Structure and Morphology of Myanmar Language by using Myanmar- English bilingual lexicon. Markov Model is also used to reformulate the translation probability of Phrase pairs. Experiment results showed that proposed system can improve translation quality by applying morphological analysis on Myanmar Language.
1) A phrase is a group of words that functions as a noun, verb, adjective, or preposition within a sentence.
2) There are different types of phrases including noun phrases, verb phrases, adjective phrases, prepositional phrases, gerund phrases, and infinitive phrases.
3) Phrases are building blocks within the structure of a sentence, along with clauses.
The document discusses the K-nearest neighbor (K-NN) classifier, a machine learning algorithm where data is classified based on its similarity to its nearest neighbors. K-NN is a lazy learning algorithm that assigns data points to the most common class among its K nearest neighbors. The value of K impacts the classification, with larger K values reducing noise but possibly oversmoothing boundaries. K-NN is simple, intuitive, and can handle non-linear decision boundaries, but has disadvantages such as computational expense and sensitivity to K value selection.
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...James Hendler
IJCAI 16 keynote on the need to bring modern AI accomplishments of recent years into connection with the more traditional goals of symbolic AI (and vice versa).
grammaticality, deep & surface structure, and ambiguityDedew Deviarini
This document discusses English morphology and syntax. It covers several key topics:
1. What is syntax and syntactic structure, including parts of speech and phrase structure.
2. The difference between deep and surface structure, where deep structure is the underlying form and surface structure is the actual form after transformations.
3. Grammaticality, which refers to sentences that follow syntactic rules rather than other factors like meaning or truth.
4. Types of ambiguities, including lexical ambiguities due to ambiguous words, and structural ambiguities due to multiple possible syntactic trees.
Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A compreh...Amit Sheth
Amit Sheth, "Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness", Keynote at
From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Falls Church, VA, February 17, 2009. http://semanticommunity.wik.is/
Searching for patterns in crowdsourced informationSilvia Puglisi
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.
Where are all the Semantic Web agents? There are billions of "machine readable" open facts on the Semantic Web, i.e. Linked Open Data (LOD), isn't that enough? It looks like it's not. We're still far from seeing Lucy's and Pete's agents brilliantly solving their tasks with the help of other Semantic Web agents they can trust (Tim Berners Lee et al., The Semantic Web, Scientific American (2001) ). Despite its technological impact on many applications and areas, the Semantic Web promised to cause a breakthrough that we didn't yet experience. One issue is that LOD ontologies are not as linked as they should be. Another issue is that formalising only semi-structured Web pages or databases is not enough for making them able to operate. They also need to reason with commonsense knowledge, the encoding of which is a long-standing challenge in Artificial Intelligence. A third consideration is that most existing commonsense knowledge bases lack formal semantics and situational constraints. In this talk I will advocate the role of the Semantic Web as a provider of a knowledge graph of commonsense to Artificial Intelligence, and discuss ways and obstacles towards the achievement of this goal.
Introductory Talk on Social Network Analysis at Facebook Developer Circle Me...Premsankar Chakkingal
This document provides an overview of social network analysis (SNA). It defines SNA as mapping and analyzing connections between individuals, groups, and institutions. The document outlines key concepts in SNA including actors (nodes), relations (links between actors), types of relations, centrality measures to identify influential individuals, and different software tools to conduct SNA. It also discusses real-world examples like Facebook data that demonstrate concepts like degrees of separation and diffusion of information through social networks.
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWCValentina Presutti
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.
The Unreasonable Effectiveness of MetadataJames Hendler
Invited talk at VIVO 2017 conference - explores the view of the semantic web as enriched metadata, and how that kind of information can be used in new and interesting ways.
"Objective fiction: the semantic construction of web reality" talks about current challenges for semantic technologies, and the Semantic Web in particular, focusing on cognitive and social dimensions of human semantics.
This is the talk given at the Faculty of Information Technology, Monash University on 19/08/2020. It covers our recent research on the topics of learning to reason, including dual-process theory, visual reasoning and neural memories.
This dissertation defense summarizes Sarasi Lalithsena's Ph.D. dissertation on domain-specific knowledge extraction from the Web of Data. The thesis statement is that applications serving specific domains can benefit by identifying relevant knowledge from structured data on the Web by (a) leveraging existing crowdsourced knowledge bases as a reference schema to automatically determine the domains of knowledge graphs, and (b) exploiting the semantics and structure of entities and relationships with statistical techniques to extract relevant portions of knowledge graphs. The outline covers identifying relevant knowledge graphs for a given domain and identifying the relevant portion of knowledge graphs through domain-specific subgraph extraction from hierarchical and non-hierarchical knowledge graphs.
This talk is about PLEA, the virtual being and the robot. It is about the vision how PLEA is made and what is her story. She samples its environment to determine how a person feels, and then demonstrates the affection back. She analyses and interprets different sources of social signals from those who interact with to generate hypotheses. Then she produces non-verbal expressions using information visualization techniques. PLEA is a proof-of-concept, and she was presented at many festivals including British Science Festival and Art & AI Festival in Leicester, the UK. At the end of this talk if we are lucky, PLEA would visit the audience from the screen.
Roman Prokofyev's PhD thesis focuses on entity-centric knowledge discovery for idiosyncratic domains. The thesis outlines contributions in four areas: named entity recognition, co-reference resolution, entity disambiguation, and tag recommendation. Evaluation of the approaches demonstrates improved performance over state-of-the-art methods, with gains of over 10% precision in entity disambiguation. The work extracts structured knowledge from unstructured text in specialized domains to enable automated processing and targeted question answering systems.
This document provides an overview of key topics in artificial intelligence including problem representation, search techniques, knowledge representation, uncertainty handling, and learning. It discusses representing problems as state spaces and using production systems. It also describes uninformed and informed search algorithms as well as constraint satisfaction and best-first search. Regarding knowledge representation, it mentions first-order logic, semantic nets, frames, scripts, and ontologies. It also covers Bayesian networks, fuzzy logic, learning from examples, and formal learning theory.
From Hyperlinks to Semantic Web Properties using Open Knowledge ExtractionSTLab
The vision of the Semantic Web is to populate the web with machine understandable information so that artificial intelligences (AI) can use it as background knowledge for assisting humans in performing a significant number of their daily tasks. Research in this field produced a standardised knowledge representation format (namely, linked data) and huge amount of machine-readable data available on the web (namely, the web of data), mostly derived from structured data (typically databases) or semi-structured data (e.g. Wikipedia infoboxes). However, most of the web consists of natural language text containing valuable knowledge for enriching the web of data. Hence, a main challenge is to extract as much relevant knowledge as possible from this content, and publish them in the form of linked data. Open Information Extraction (OIE) has been developed recently as an approach to extract information from unstructured data, mostly of a textual nature.
However, the information extracted is typically in the form of triples of strings (subject, relational phrase, object). OIE approaches are useful but insufficient alone for populating the web with machine readable information as their results are not directly linkable to, and immediately reusable from, other linked data sources. In this seminar, after giving a brief introduction to background concepts and notions, I will describe a work that proposes a novel Open Knowledge Extraction approach that performs unsupervised, open domain, and abstractive knowledge extraction from text for producing directly usable machine readable information. In particular I will discuss an approach based on the hypothesis that hyperlinks (either created by humans or knowledge extraction tools) provide a pragmatic trace of such semantic relations between two entities, and that such semantic relations, their subjects and objects, can be revealed by processing their linguistic traces (i.e. the sentences that embed the hyperlinks) and formalised as linked data and ontology axioms. Experimental evaluations conducted with the help of crowdsourcing confirm this hypothesis showing very high performances. A demo of Open Knowledge Extraction at http://wit.istc.cnr.it/stlab-tools/legalo.
The Semantic Web: What IAs Need to Know About Web 3.0Chiara Fox Ogan
The document discusses the Semantic Web and Web 3.0. It defines the Semantic Web as an extension of the current web that makes data on the web more accessible to machines. It explains key concepts needed to realize the Semantic Web like identifying resources with URIs, linking data using RDF triples, using ontologies to define relationships between concepts, and sharing structured data and ontologies. The document provides examples of how semantics are already being used in applications today and how semantics can improve search and allow new types of questions to be asked of linked data.
The document discusses the topics of knowledge representation in artificial intelligence. It covers problem representation paradigms, search techniques for problem solving including uninformed and informed searches, knowledge representation methods such as logic-based representations, semantic networks, frames and scripts. It also discusses handling uncertain knowledge using Bayesian networks and fuzzy logic, as well as machine learning techniques.
1. A virtual world is a computer-simulated 3D environment where users interact through avatars. Second Life is a popular virtual world launched in 2003 with over 18 million users.
2. Second Life allows for a high degree of customization and collaboration. Users can design their own avatars and virtual objects. Many universities and companies use Second Life for teaching, meetings, and social networking.
3. Educational uses of Second Life include language learning, architecture tours, and role-playing simulations. However, its use should be intentional and technologies chosen based on their ability to meet instructional goals.
Choices, modelling and Frankenstein Ontologiesbenosteen
This document discusses an ontology project at the University of Bristol. It addresses issues with representing research information, which changes frequently. The project uses a combination of ontologies like FOAF, Bio, and Dcterms to model "Things" like people and publications. Context about these Things, like time periods of validity, is represented using named graphs. The current implementation stores this information in a Fedora object store with RDF serialization. The project aims to gather relevant domain taxonomies and provide APIs for researchers to maintain them, taking a "Frankenstein" approach of combining relevant standards. It notes some design flaws of the CERIF interchange format compared to the linked data approach taken.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
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.
More Related Content
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Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A compreh...Amit Sheth
Amit Sheth, "Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness", Keynote at
From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Falls Church, VA, February 17, 2009. http://semanticommunity.wik.is/
Searching for patterns in crowdsourced informationSilvia Puglisi
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.
Where are all the Semantic Web agents? There are billions of "machine readable" open facts on the Semantic Web, i.e. Linked Open Data (LOD), isn't that enough? It looks like it's not. We're still far from seeing Lucy's and Pete's agents brilliantly solving their tasks with the help of other Semantic Web agents they can trust (Tim Berners Lee et al., The Semantic Web, Scientific American (2001) ). Despite its technological impact on many applications and areas, the Semantic Web promised to cause a breakthrough that we didn't yet experience. One issue is that LOD ontologies are not as linked as they should be. Another issue is that formalising only semi-structured Web pages or databases is not enough for making them able to operate. They also need to reason with commonsense knowledge, the encoding of which is a long-standing challenge in Artificial Intelligence. A third consideration is that most existing commonsense knowledge bases lack formal semantics and situational constraints. In this talk I will advocate the role of the Semantic Web as a provider of a knowledge graph of commonsense to Artificial Intelligence, and discuss ways and obstacles towards the achievement of this goal.
Introductory Talk on Social Network Analysis at Facebook Developer Circle Me...Premsankar Chakkingal
This document provides an overview of social network analysis (SNA). It defines SNA as mapping and analyzing connections between individuals, groups, and institutions. The document outlines key concepts in SNA including actors (nodes), relations (links between actors), types of relations, centrality measures to identify influential individuals, and different software tools to conduct SNA. It also discusses real-world examples like Facebook data that demonstrate concepts like degrees of separation and diffusion of information through social networks.
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWCValentina Presutti
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.
The Unreasonable Effectiveness of MetadataJames Hendler
Invited talk at VIVO 2017 conference - explores the view of the semantic web as enriched metadata, and how that kind of information can be used in new and interesting ways.
"Objective fiction: the semantic construction of web reality" talks about current challenges for semantic technologies, and the Semantic Web in particular, focusing on cognitive and social dimensions of human semantics.
This is the talk given at the Faculty of Information Technology, Monash University on 19/08/2020. It covers our recent research on the topics of learning to reason, including dual-process theory, visual reasoning and neural memories.
This dissertation defense summarizes Sarasi Lalithsena's Ph.D. dissertation on domain-specific knowledge extraction from the Web of Data. The thesis statement is that applications serving specific domains can benefit by identifying relevant knowledge from structured data on the Web by (a) leveraging existing crowdsourced knowledge bases as a reference schema to automatically determine the domains of knowledge graphs, and (b) exploiting the semantics and structure of entities and relationships with statistical techniques to extract relevant portions of knowledge graphs. The outline covers identifying relevant knowledge graphs for a given domain and identifying the relevant portion of knowledge graphs through domain-specific subgraph extraction from hierarchical and non-hierarchical knowledge graphs.
This talk is about PLEA, the virtual being and the robot. It is about the vision how PLEA is made and what is her story. She samples its environment to determine how a person feels, and then demonstrates the affection back. She analyses and interprets different sources of social signals from those who interact with to generate hypotheses. Then she produces non-verbal expressions using information visualization techniques. PLEA is a proof-of-concept, and she was presented at many festivals including British Science Festival and Art & AI Festival in Leicester, the UK. At the end of this talk if we are lucky, PLEA would visit the audience from the screen.
Roman Prokofyev's PhD thesis focuses on entity-centric knowledge discovery for idiosyncratic domains. The thesis outlines contributions in four areas: named entity recognition, co-reference resolution, entity disambiguation, and tag recommendation. Evaluation of the approaches demonstrates improved performance over state-of-the-art methods, with gains of over 10% precision in entity disambiguation. The work extracts structured knowledge from unstructured text in specialized domains to enable automated processing and targeted question answering systems.
This document provides an overview of key topics in artificial intelligence including problem representation, search techniques, knowledge representation, uncertainty handling, and learning. It discusses representing problems as state spaces and using production systems. It also describes uninformed and informed search algorithms as well as constraint satisfaction and best-first search. Regarding knowledge representation, it mentions first-order logic, semantic nets, frames, scripts, and ontologies. It also covers Bayesian networks, fuzzy logic, learning from examples, and formal learning theory.
From Hyperlinks to Semantic Web Properties using Open Knowledge ExtractionSTLab
The vision of the Semantic Web is to populate the web with machine understandable information so that artificial intelligences (AI) can use it as background knowledge for assisting humans in performing a significant number of their daily tasks. Research in this field produced a standardised knowledge representation format (namely, linked data) and huge amount of machine-readable data available on the web (namely, the web of data), mostly derived from structured data (typically databases) or semi-structured data (e.g. Wikipedia infoboxes). However, most of the web consists of natural language text containing valuable knowledge for enriching the web of data. Hence, a main challenge is to extract as much relevant knowledge as possible from this content, and publish them in the form of linked data. Open Information Extraction (OIE) has been developed recently as an approach to extract information from unstructured data, mostly of a textual nature.
However, the information extracted is typically in the form of triples of strings (subject, relational phrase, object). OIE approaches are useful but insufficient alone for populating the web with machine readable information as their results are not directly linkable to, and immediately reusable from, other linked data sources. In this seminar, after giving a brief introduction to background concepts and notions, I will describe a work that proposes a novel Open Knowledge Extraction approach that performs unsupervised, open domain, and abstractive knowledge extraction from text for producing directly usable machine readable information. In particular I will discuss an approach based on the hypothesis that hyperlinks (either created by humans or knowledge extraction tools) provide a pragmatic trace of such semantic relations between two entities, and that such semantic relations, their subjects and objects, can be revealed by processing their linguistic traces (i.e. the sentences that embed the hyperlinks) and formalised as linked data and ontology axioms. Experimental evaluations conducted with the help of crowdsourcing confirm this hypothesis showing very high performances. A demo of Open Knowledge Extraction at http://wit.istc.cnr.it/stlab-tools/legalo.
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The document discusses the Semantic Web and Web 3.0. It defines the Semantic Web as an extension of the current web that makes data on the web more accessible to machines. It explains key concepts needed to realize the Semantic Web like identifying resources with URIs, linking data using RDF triples, using ontologies to define relationships between concepts, and sharing structured data and ontologies. The document provides examples of how semantics are already being used in applications today and how semantics can improve search and allow new types of questions to be asked of linked data.
The document discusses the topics of knowledge representation in artificial intelligence. It covers problem representation paradigms, search techniques for problem solving including uninformed and informed searches, knowledge representation methods such as logic-based representations, semantic networks, frames and scripts. It also discusses handling uncertain knowledge using Bayesian networks and fuzzy logic, as well as machine learning techniques.
1. A virtual world is a computer-simulated 3D environment where users interact through avatars. Second Life is a popular virtual world launched in 2003 with over 18 million users.
2. Second Life allows for a high degree of customization and collaboration. Users can design their own avatars and virtual objects. Many universities and companies use Second Life for teaching, meetings, and social networking.
3. Educational uses of Second Life include language learning, architecture tours, and role-playing simulations. However, its use should be intentional and technologies chosen based on their ability to meet instructional goals.
Choices, modelling and Frankenstein Ontologiesbenosteen
This document discusses an ontology project at the University of Bristol. It addresses issues with representing research information, which changes frequently. The project uses a combination of ontologies like FOAF, Bio, and Dcterms to model "Things" like people and publications. Context about these Things, like time periods of validity, is represented using named graphs. The current implementation stores this information in a Fedora object store with RDF serialization. The project aims to gather relevant domain taxonomies and provide APIs for researchers to maintain them, taking a "Frankenstein" approach of combining relevant standards. It notes some design flaws of the CERIF interchange format compared to the linked data approach taken.
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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?
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9. What is Camel K?
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10. Configuring Camel K Integrations for Data Pipelines
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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
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Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
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.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
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.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
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-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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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.
Knowledge Extraction and Linked Data: Playing with Frames
1. Knowledge Extraction and Linked Data:
Playing with Frames
Valentina Presutti
STLab, ISTC-CNR
Linked Data For Information Extraction @ ISWC 2016
Tuesday, October 18th 2016
2. STLab team
Valentina Presutti Aldo Gangemi
Andrea Nuzzolese
Diego Reforgiato
Martina Sangiovanni Mario Caruso
Giorgia Lodi
Alessandro Russo
Luigi Asprino
Piero Conca
2
3. 3
• Frames as units of meaning (claim andintuition)
• Background on frames
• From entity-centric to frame-centric knowledge extraction
• Some STLab research projects and results
• Next and open issues
Outline
5. 5
Frames naturally support knowledge reconciliation,
regardless the logical, conceptual or syntactic
representation of knowledge sources
6. To understand who speaks to us or a text we read
We identify the main entities and how they relate to
each other within a schema (frame)
Frame occurrences + context-dependent reasoning
The intuition
6
7. 7
I went to the disco and I met a friend, who had lost her keys.
We spent the night looking for them.
8. 8
I went to the disco and I met a friend, who had lost her keys.
We spent the night looking for them.
9. 9
I went to the disco and I met a friend, who had lost her keys.
We spent the night looking for them.
10. 10
I went to the disco and I met a friend, who had lost her keys.
We spent the night looking for them.
11. I went to the disco and I met a friend, who had lost her keys.
We spent the night looking for them.
11
14. 14
Minsky [1]
“When one encounters a new situation
[…] one selects from memory a
structure called a Frame. This is a
remembered framework to be adapted
to fit reality by changing details as
necessary.”
“A frame is a data-structure for
representing a stereotyped situation,
like being in a certain kind of living room,
or going to a child's birthday party”
“We can think of a frame as a network
of nodes and relations.”
“Collections of related frames are
linked together into frame-systems”
Fillmore [2]
“[…] in characterising a language system
we must add to the description of grammar
and lexicon a description of the
cognitive and interactional
“frames” […]”
“The evolution toward language must have
consisted in part in the gradual acquisition
of a reportory of frames and of
mental processes for operating with them,
and eventually the capacity to create new
frames and to transmit them.”
“[…] in order to perceive something or to
attain a concept, what is […] necessary is
to have in memory a repertoire of
prototypes. The act of perception or
conception being that of recognizing in
what ways an object can be seens as an
instance of one or another of these
prototypes.”
17. 17
N-ary relation f(e, e1,…en)
f is a first order logic relation
e is a variable for any event or situation
described by f
ei is a variable for any of the entity arguments
of f
An OWL n-ary relation pattern
the n-ary relation is the reification of f, i.e. e
the n objects represent the arguments of f
the n argument relations are binary
projections of f including e
co-participation relations are binary
projections of f not including e
Representing frames
“Hagrid rolled up a note
for Harry in Hogwarts”
18. 18
From entity-centric to
frame-centric design and
extraction
Before:
Key terms à
classes/properties
After:
Key situationsà
frames/patterns
Frames as units of meaning [3]
21. This requires at least three ingredients:
Knowledge representation
Knowledge extraction
Automated reasoning and learning
21
22. The Semantic Web and Linked Data
Knowledge representation
Knowledge extraction
Automated reasoning
22
23. Mary
marriedWith
John Mary
weddingDate
October 12th, 2016 John
weddingDate
October 12th, 2016
Mary
weddingPlace
Kobe John
weddingPlace
Kobe Mary
weddingPlace
Rome
The Semantic Web and Linked Data
Knowledge representation
Knowledge extraction
Automated reasoning
24. 24
OL & KE tools main focus:
Named Entity extraction
Taxonomy induction,
Relation extraction
Axiom extraction, …
The Semantic Web and Linked Data
Knowledge representation
Knowledge extraction
Automated reasoning
25. 25
This is useful, but it’s not enough
Semantic heterogeneity
Lack of knowledge boundaries
(context) [3]
marriedWith
firstMarriageWith
spousemarriage
spousedate
26. 26
The role of frames in knowledge representation, extraction and
interaction
Performingempirical observations on the web (in line with van
Harmelen’s [4])
Using frames for driving the design of solutions to research
problems andtest their performance
Frames as units of meaning
29. 29
Frame-based Linked Data
“Rico Lebrun taught visual arts at the Chouinard Art Institute and at the Disney
Studios. He was influenced by Michelangelo and maintained a lifelong affinity for
Goya and Picasso.”
30. 30
FRED
“The Black Hand might not have decided to barbarously assassinate Franz Ferdinand
after he arrived in Sarajevo on June 28th, 1914”
31. 31
Automatic selection of relevant binary projections of frames
Usable label generation
Formal alignmentbetween frames and binary properties
Binary relations [6]
32. 32
Binary relation assessment
“Rico Lebrun taught visual arts at the Chouinard Art Institute and at the Disney Studios. He
was influenced by Michelangelo and maintained a lifelong affinity for Goya and Picasso.”
Subject
ObjectSubject Object
http://wit.istc.cnr.it/stlab-tools/legalo
33. 33
Binary property generation
vn.role:Actor1 -> “with”
vn.role:Actor2 -> “with”
vn.role:Beneficiary -> “for”
vn.role:Instrument -> “with”
vn.role:Destination -> “to”
vn.role:Topic -> “about”
vn.role:Source -> “from”
Subject
Object
legalo:teachArtAt
teach art at
“Rico Lebrun taught visual arts at the Chouinard Art Institute and at the Disney Studios. He
was influenced by Michelangelo and maintained a lifelong affinity for Goya and Picasso.”
http://wit.istc.cnr.it/stlab-tools/legalo
35. 35
Semantic Web triples and
properties generation
“Rico Lebrun taught visual arts at the
Chouinard Art Institute and at the Disney
Studios. He was influenced by Michelangelo
and maintained a lifelong affinity for Goya
and Picasso.”
dbpedia:Rico_Lebrun s:teachAbout dbpedia:Visual_arts .
s:teachAbout a owl:ObjectProperty ;
rdfs:subPropertyOf fred:Teach;
rdfs:domain wibi:Artist ;
rdfs:range wibi:Art ;
grounding:definedFromFormalRepresentation
fred-graph:a6705cedbf9b53d10bbcdedaa3be9791da0a9e94 ;
grounding:derivedFromLinguisticEvidence s:linguisticEvidence ;
owl:propertyChainAxiom([ owl:inverseOf s:AgentTeach ] s:TopicTeach) .
_:b2 a alignment:Cell ;
alignment:entity1 s:teachAbout ;
alignment:entity2 <http://purl.org/vocab/aiiso/schema#teaches> ;
alignment:measure "0.846"^xsd:float ;
alignment:relation "equivalence" .
domain, range, subsumption
linguistic and formal scope
alignment to existing LOD vocabularies
37. 37
Topic detection and Opinion holder detection [8]
Sentiment propagation through frames and roles [9]
Sentiment analysis
“People hope that the President will be
condemned by the judges”
38. 38
50 sentencesfrom MPQA opinion corpus1 and Europarl corpus2
100 Sentence sentiment polarity of open rated hotel reviews
(positive and negative)
Evaluation
Task Measure Value
Holder detection F1 0.95
Topic detection F1 0.68
Sub-topic
detection
F1 0.77
Review sentiment
vs. user scores
Avg. correlation 0.81
2 http://www.statmt.org/europarl/
1 http://mpqa.cs.pitt.edu/corpora/mpqacorpus/
3 http://www.stlab.istc.cnr.it/documents/sentilo/reviewsposneg.zip
39. 39
Frame-basedlinked data shows an effective representation of
discourse
Our ultimate goal is machine understanding, hence
an important issue is the limited coverage of existing resources
and their integration with factual world knowledge
FrameBase [10] partially addresses this problem, starting from
similar principles and intuitions
STLab has develop Framester [11,12]: a general web-scale
integrated resource which integrateslinguistic and world factual
knowledge
(see Aldo’s presentation later)
Coverage and integration of
linguistic and world knowledge
40. 40
Abstract, formalised frame model
generalised model of roles
Represents all resources’ entitiesin terms of its frame semantics
Links linguistic data with ontologies and facts (~43M triples)
Includes FrameBase’s ReDer rules
Framester
41. 41
Word-Frame-Disambiguation (frame detection)
any word, e.g. Shakespeare, write, alone, nicely, etc.
frames evoked by word senses
Outperforms Semafor and FrameBase
details to come in few minutes J
!!!Spoiler Warning!!!
http://lipn.univ-paris13.fr/framester/en/wfd/
42. 42
Helping people with Dementia and their carers
Natural language understanding
questionnaire for cognitive ability
assessment
speech to tag (pictures, music, events, etc.)
reminiscence games and suggestions
suggesting missing words
understanding with partial information
Current project and challenge
http://www.mario-project.eu
Blah blah blah blah
Blah blah blah blah
Blah blah blah blah
Blah blah blah blah
Blah blah blah blah
Blah blah blah blah
Blah blah blah blah
Blah blah blah blah
Blah blah blah blah
User-Robot KB
43. 43
Current work:
To integrate FRED and Framester for normalising results
Framester-driven Ontology Alignment (part of a PhD thesis under dev)
MARIO understanding component and evaluation (with datasets and
PwD)
Open challenge:
How to combine statistical learning with our approaches?
we want FRED to learn from interaction experiences
we want to learn new rules and procedures, not only data
(algorithm learning), and get their formalisation, explicitly
Next and open issues
45. 45
References
[1] Marvin Minsky: A Framework for Representing Knowledge. MIT-AI
Laboratory Memo 306, June, 1974.
[2] Charles J Fillmore. Frame Semantics and the Nature of Language. Annals of
the New York Academy of Sciences, 280(1):20-32, 1976.
[3] Aldo Gangemi, Valentina Presutti: Towards a pattern science for the Semantic
Web. Semantic Web 1(1-2): 61-68 (2010)
[4] Frank van Harmelen: The Web of Data: do we understand what we build?
https://sssw.org/2016/?page_id=386
[5] Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero, Andrea
Giovanni Nuzzolese, Francesco Draicchio, Misael Mongiovì: Semantic Web
Machine Reading with FRED. Semantic Web (To appear)
[6] Valentina Presutti, Andrea Giovanni Nuzzolese, Sergio Consoli, Aldo
Gangemi, Diego Regorgiato Recupero: From hyperlinks to Semantic Web
properties using Open Knowledge Extraction pp. 351-378, Semantic Web,
Volume 7, Number 4 / 2016.
46. 46
[7] Aldo Gangemi: A Comparison of Knowledge Extraction Tools for the
Semantic Web. ESWC 2013: 351-366
[8] Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero:
Frame-Based Detection of Opinion Holders and Topics: A Model and a
Tool. IEEE Comp. Int. Mag. 9(1): 20-30 (2014)
[9] Diego Reforgiato Recupero, Valentina Presutti, Sergio Consoli, Aldo
Gangemi, Andrea Giovanni Nuzzolese: Sentilo: Frame-Based Sentiment
Analysis. Cognitive Computation 7(2): 211-225 (2015)
[10] Jacobo Rouces, Gerard de Melo, and Katja Hose. Framebase: Representing
n-ary relations using semantic frames. ESWC 2015: 505-521
[11] Aldo Gangemi, Mehwish Alam, Valentina Presutti, Luigi Asprino and Diego
Reforgiato Recupero: Framester: A Wide Coverage Linguistic Linked Data Hub.
In Proceedings of EKAW 2016
[12] Aldo Gangemi, Mehwish Alam, Valentina Presutti: Word Frame
Disambiguation: Evaluating Linguistic Linked Data on Frame Detection.
LD4IE@ISWC 2016: 23-31
References cont.