The document discusses methods of automated knowledge extraction from web documents. It describes how knowledge extraction systems work by defining output structures, preprocessing input text, applying extraction methods, and generating output. The key steps involve identifying named entities, their relationships, and addressing issues like ambiguity and accuracy challenges. While automated extraction helps analyze vast online information, limitations regarding accuracy, efficiency and dependency on external resources exist. Combining automated techniques with human oversight may help improve knowledge extraction.
An Introduction to Information Retrieval and Applicationssathish sak
An Introduction to Information Retrieval and Applications The score you get depends on the functions, difficulty and quality of your project
For system development:
System functions and correctness
For academic paper presentation
Quality and your presentation of the paper
Major methods/experimental results *must* be presented
Papers from top conferences are strongly suggested
E.g. SIGIR, WWW, CIKM, WSDM, JCDL, ICMR, …
Proposals are *required* for each team, and will be counted in the score
September 2021: Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Human Being Character Analysis from Their Social Networking ProfilesBiswaranjan Samal
In this paper, characteristics of human beings obtained from profile statement present in their social
networking profile status are analyzed in terms of introvert, extrovert or ambivert. Recently, Machine learning
plays a vital role in classifying the human characteristics. The user profile status is collected from LinkedIn, a
popular professional social networking application. Oauth2.0 protocol is used for login into the LinkedIn and
web scrapping using JavaScript is used for information extraction of the registered users. Then, Word Net: a
lexical database is used for forming the word clusters such as: extrovert and introvert using semi-supervised
learning techniques. K-nearest neighbor classification algorithm is finally considered for classifying the profiles
into various available categories. The results obtained in the proposed method are encouraging with good
accuracy
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Natural Language Processing reveals the structure and meaning of text by offering powerful machine learning models. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage.
• What is Natural Language Processing?
• How & where to use NLP
• NLP for information retrieval
This talk features the basics behind the science of Information Retrieval with a story-mode on information and its various aspects. It then takes you through a quick journey into the process behind building of the search engine.
An Introduction to Information Retrieval and Applicationssathish sak
An Introduction to Information Retrieval and Applications The score you get depends on the functions, difficulty and quality of your project
For system development:
System functions and correctness
For academic paper presentation
Quality and your presentation of the paper
Major methods/experimental results *must* be presented
Papers from top conferences are strongly suggested
E.g. SIGIR, WWW, CIKM, WSDM, JCDL, ICMR, …
Proposals are *required* for each team, and will be counted in the score
September 2021: Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Human Being Character Analysis from Their Social Networking ProfilesBiswaranjan Samal
In this paper, characteristics of human beings obtained from profile statement present in their social
networking profile status are analyzed in terms of introvert, extrovert or ambivert. Recently, Machine learning
plays a vital role in classifying the human characteristics. The user profile status is collected from LinkedIn, a
popular professional social networking application. Oauth2.0 protocol is used for login into the LinkedIn and
web scrapping using JavaScript is used for information extraction of the registered users. Then, Word Net: a
lexical database is used for forming the word clusters such as: extrovert and introvert using semi-supervised
learning techniques. K-nearest neighbor classification algorithm is finally considered for classifying the profiles
into various available categories. The results obtained in the proposed method are encouraging with good
accuracy
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Natural Language Processing reveals the structure and meaning of text by offering powerful machine learning models. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage.
• What is Natural Language Processing?
• How & where to use NLP
• NLP for information retrieval
This talk features the basics behind the science of Information Retrieval with a story-mode on information and its various aspects. It then takes you through a quick journey into the process behind building of the search engine.
The Process of Information extraction through Natural Language ProcessingWaqas Tariq
Information Retrieval (IR) is the discipline that deals with retrieval of unstructured data, especially textual documents, in response to a query or topic statement, which may itself be unstructured, e.g., a sentence or even another document, or which may be structured, e.g., a boolean expression. The need for effective methods of automated IR has grown in importance because of the tremendous explosion in the amount of unstructured data, both internal, corporate document collections, and the immense and growing number of document sources on the Internet.. The topics covered include: formulation of structured and unstructured queries and topic statements, indexing (including term weighting) of document collections, methods for computing the similarity of queries and documents, classification and routing of documents in an incoming stream to users on the basis of topic or need statements, clustering of document collections on the basis of language or topic, and statistical, probabilistic, and semantic methods of analyzing and retrieving documents. Information extraction from text has therefore been pursued actively as an attempt to present knowledge from published material in a computer readable format. An automated extraction tool would not only save time and efforts, but also pave way to discover hitherto unknown information implicitly conveyed in this paper. Work in this area has focused on extracting a wide range of information such as chromosomal location of genes, protein functional information, associating genes by functional relevance and relationships between entities of interest. While clinical records provide a semi-structured, technically rich data source for mining information, the publications, in their unstructured format pose a greater challenge, addressed by many approaches.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
Presentation of the paper titled "Leveraging Semantic Parsing for Relation Linking over Knowledge Bases" at the ISWC 2020 - Research Track.
@inproceedings{mihindu-sling-2020,
title = "Leveraging Semantic Parsing for Relation Linking over Knowledge Bases",
author = "Mihindukulasooriya, Nandana and Rossiello, Gaetano and Kapanipathi, Pavan and Abdelaziz, Ibrahim and Ravishankar, Srinivas and Yu, Mo and Gliozzo, Alfio and Roukos, Salim and Gray, Alexander",
booktitle="The Semantic Web -- ISWC 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="402--419",
url = "https://link.springer.com/chapter/10.1007/978-3-030-62419-4_23",
doi = "10.1007/978-3-030-62419-4_23"
}
This chapter introduces the notion of Information Retrieval (IR). it discusses after a survey of classification of various IR systems and major components of an IR system, the notion of Boolean Retrieval model and Invertex Index and extended Boolean are presented.
Domain Specific Named Entity Recognition Using Supervised ApproachWaqas Tariq
This paper introduces Named Entity Recognition approach for textual corpus. Supervised Statistical methods are used to develop our system. Our system can be used to categorize NEs belonging to a particular domain for which it is being trained. As Named Entities appears in text surrounded by contexts (words that are left or right of the NE), we will be focusing on extracting NE contexts from text and then perform statistical computing on them. We are using n-gram modeling for extracting contexts from text. Our methodology first extracts left and right tri-grams surrounding NE instances in the training corpus and calculate their probabilities. Then all the extracted tri-grams along with their calculated probabilities are stored in a file. During testing, system detects unrecognized NEs in the testing corpus and categorize them using the tri-gram probabilities calculated during training time. The proposed system consists of two modules namely Knowledge acquisition and NE Recognition. Knowledge acquisition module extracts the tri-grams surrounding NEs in the training corpus and NE Recognition module performs the categorization of Named Entities in the testing corpus.
lectronic-mail is widely used most suitable method of transferring messages electronically from one
person to another, rising from and going to any part of the world. Main features of Electronic mail is its speed,
dependability, well-equipped storage options and a large number of added services make it highly well-liked
among people from all sectors of business and society. But being popular it also has negative side too. Electronics
mails are preferred media for a large number of attacks over the internet.. A number of the most popular attacks over
the internet include spams. Some methods are essentially in detection of spam related mails but they have higher false
positives. A number of filters such as Checksum-based filters, Bayesian filters, machine learning based and
memory-based filters are usually used in order to recognize spams. As spammers constantly try to find a way to
avoid existing filters, a new filters need to be developed to catch spam. This paper proposes to find an
resourceful spam mail filtering method using user profile base ontology. Ontologies permit for machineunderstandable
semantics of data. It is main to interchange information with each other for more efficient spam
filtering. Thus, it is essential to build ontology and a framework for capable email filtering. Using ontology that is
particularly designed to filter spam, bunch of useless bulk email could be filtered out on the system. We propose a
user profile-based spam filter that classifies email based on the likelihood that User profile within it have been
included in spam or valid email.
Phrase Structure Identification and Classification of Sentences using Deep Le...ijtsrd
Phrase structure is the arrangement of words in a specific order based on the constraints of a specified language. This arrangement is based on some phrase structure rules which are according to the productions in context free grammar. The identification of the phrase structure can be done by breaking the specified natural language sentence into its constituents that may be lexical and phrasal categories. These phrase structures can be identified using parsing of the sentences which is nothing but syntactic analysis. The proposed system deals with this problem using Deep Learning strategy. Instead of using Rule Based technique, supervised learning with sequence labelling is done using IOB labelling. This is a sequence classification problem which has been trained and modeled using RNN LSTM. The proposed work has shown a considerable result and can be applied in many applications of NLP. Hashi Haris | Misha Ravi ""Phrase Structure Identification and Classification of Sentences using Deep Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23841.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23841/phrase-structure-identification-and-classification-of-sentences-using-deep-learning/hashi-haris
The Process of Information extraction through Natural Language ProcessingWaqas Tariq
Information Retrieval (IR) is the discipline that deals with retrieval of unstructured data, especially textual documents, in response to a query or topic statement, which may itself be unstructured, e.g., a sentence or even another document, or which may be structured, e.g., a boolean expression. The need for effective methods of automated IR has grown in importance because of the tremendous explosion in the amount of unstructured data, both internal, corporate document collections, and the immense and growing number of document sources on the Internet.. The topics covered include: formulation of structured and unstructured queries and topic statements, indexing (including term weighting) of document collections, methods for computing the similarity of queries and documents, classification and routing of documents in an incoming stream to users on the basis of topic or need statements, clustering of document collections on the basis of language or topic, and statistical, probabilistic, and semantic methods of analyzing and retrieving documents. Information extraction from text has therefore been pursued actively as an attempt to present knowledge from published material in a computer readable format. An automated extraction tool would not only save time and efforts, but also pave way to discover hitherto unknown information implicitly conveyed in this paper. Work in this area has focused on extracting a wide range of information such as chromosomal location of genes, protein functional information, associating genes by functional relevance and relationships between entities of interest. While clinical records provide a semi-structured, technically rich data source for mining information, the publications, in their unstructured format pose a greater challenge, addressed by many approaches.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
Presentation of the paper titled "Leveraging Semantic Parsing for Relation Linking over Knowledge Bases" at the ISWC 2020 - Research Track.
@inproceedings{mihindu-sling-2020,
title = "Leveraging Semantic Parsing for Relation Linking over Knowledge Bases",
author = "Mihindukulasooriya, Nandana and Rossiello, Gaetano and Kapanipathi, Pavan and Abdelaziz, Ibrahim and Ravishankar, Srinivas and Yu, Mo and Gliozzo, Alfio and Roukos, Salim and Gray, Alexander",
booktitle="The Semantic Web -- ISWC 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="402--419",
url = "https://link.springer.com/chapter/10.1007/978-3-030-62419-4_23",
doi = "10.1007/978-3-030-62419-4_23"
}
This chapter introduces the notion of Information Retrieval (IR). it discusses after a survey of classification of various IR systems and major components of an IR system, the notion of Boolean Retrieval model and Invertex Index and extended Boolean are presented.
Domain Specific Named Entity Recognition Using Supervised ApproachWaqas Tariq
This paper introduces Named Entity Recognition approach for textual corpus. Supervised Statistical methods are used to develop our system. Our system can be used to categorize NEs belonging to a particular domain for which it is being trained. As Named Entities appears in text surrounded by contexts (words that are left or right of the NE), we will be focusing on extracting NE contexts from text and then perform statistical computing on them. We are using n-gram modeling for extracting contexts from text. Our methodology first extracts left and right tri-grams surrounding NE instances in the training corpus and calculate their probabilities. Then all the extracted tri-grams along with their calculated probabilities are stored in a file. During testing, system detects unrecognized NEs in the testing corpus and categorize them using the tri-gram probabilities calculated during training time. The proposed system consists of two modules namely Knowledge acquisition and NE Recognition. Knowledge acquisition module extracts the tri-grams surrounding NEs in the training corpus and NE Recognition module performs the categorization of Named Entities in the testing corpus.
lectronic-mail is widely used most suitable method of transferring messages electronically from one
person to another, rising from and going to any part of the world. Main features of Electronic mail is its speed,
dependability, well-equipped storage options and a large number of added services make it highly well-liked
among people from all sectors of business and society. But being popular it also has negative side too. Electronics
mails are preferred media for a large number of attacks over the internet.. A number of the most popular attacks over
the internet include spams. Some methods are essentially in detection of spam related mails but they have higher false
positives. A number of filters such as Checksum-based filters, Bayesian filters, machine learning based and
memory-based filters are usually used in order to recognize spams. As spammers constantly try to find a way to
avoid existing filters, a new filters need to be developed to catch spam. This paper proposes to find an
resourceful spam mail filtering method using user profile base ontology. Ontologies permit for machineunderstandable
semantics of data. It is main to interchange information with each other for more efficient spam
filtering. Thus, it is essential to build ontology and a framework for capable email filtering. Using ontology that is
particularly designed to filter spam, bunch of useless bulk email could be filtered out on the system. We propose a
user profile-based spam filter that classifies email based on the likelihood that User profile within it have been
included in spam or valid email.
Phrase Structure Identification and Classification of Sentences using Deep Le...ijtsrd
Phrase structure is the arrangement of words in a specific order based on the constraints of a specified language. This arrangement is based on some phrase structure rules which are according to the productions in context free grammar. The identification of the phrase structure can be done by breaking the specified natural language sentence into its constituents that may be lexical and phrasal categories. These phrase structures can be identified using parsing of the sentences which is nothing but syntactic analysis. The proposed system deals with this problem using Deep Learning strategy. Instead of using Rule Based technique, supervised learning with sequence labelling is done using IOB labelling. This is a sequence classification problem which has been trained and modeled using RNN LSTM. The proposed work has shown a considerable result and can be applied in many applications of NLP. Hashi Haris | Misha Ravi ""Phrase Structure Identification and Classification of Sentences using Deep Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23841.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23841/phrase-structure-identification-and-classification-of-sentences-using-deep-learning/hashi-haris
Wellness at hand is designed to support smokers in managing their cravings for cigarette during a quit attempt. Wellness-at-hand includes a bracelet that senses the physiological data of the user and the user interaction with the system happens through the palm-based interactions. It provides a holistic approach to help smokers in managing their cravings in the following way: First, it enforces a quit plan attached with money deterrence; secondly, it engages the smoker in short interactions like games during a craving episode; thirdly, it provides a better understanding of emotions and unconscious thoughts leading to smoking, and lastly, it utilizes positive reinforcement to motivate smokers to continue their quit plan.
(Submitted as part of a course project for Interaction Design and Usability)
Presentation took during Computer Linguistics course at UPF (Universitat Pompeu Fabra) covering the following topic:
Information Extraction
Jerry R. Hobbs, University of Southern California Ellen Riloff, University of Utah
Ontology Learning from Text
Ontology construction ‘Layer Cake’
Knowledge representation and knowledge management systems
Subtasks in ontology learning
Most Popular Ontology Learning Tools
key note address delivered on 23rd March 2011 in the Workshop on Data Mining and Computational Biology in Bioinformatics, sponsored by DBT India and organised by Unit of Simulation and Informatics, IARI, New Delhi.
I do not claim any originality either to slides or their content and in fact aknowledge various web sources.
Content Analysis Overview for Persona DevelopmentPamela Rutledge
After developing an Ad Hoc persona as the core of your engagement strategy, it's important to test your assumptions against real people and real data. Content analysis is a methodology for evaluating text-based data that can be gathered from social media tools.
Named entity recognition using web document corpusIJMIT JOURNAL
This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE)
can be a named: location, person, organization, date, time, etc., characterized by instances. A NE is
found in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at identifying contexts inducing the NE’s nature. As such, The occurrence of the word "President" in a text, means that this word or context may be followed by the name of a president as President "Obama". Likewise, a word preceded by the string "footballer" induces that this is the name of a
footballer. NE recognition may be viewed as a classification method, where every word is assigned to
a NE class, regarding the context. The aim of this study is then to identify and classify the contexts that are most relevant to recognize a NE, those which are frequently found with the NE. A learning approach using training corpus: web documents, constructed from learning examples is then suggested. Frequency representations and modified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.
Named Entity Recognition Using Web Document CorpusIJMIT JOURNAL
This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE) can be a named: location, person, organization, date, time, etc., characterized by instances. A NE is found in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at identifying contexts inducing the NE’s nature. As such, The occurrence of the word "President" in a text, means that this word or context may be followed by the name of a president as President "Obama". Likewise, a word preceded by the string "footballer" induces that this is the name of a footballer. NE recognition may be viewed as a classification method, where every word is assigned to a NE class, regarding the context.
The aim of this study is then to identify and classify the contexts that are most relevant to recognize a NE, those which are frequently found with the NE. A learning approach using training corpus: web documents, constructed from learning examples is then suggested. Frequency representations and modified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.
Similar to Knowledge acquisition using automated techniques (20)
Teleconsultation refers to the electronic communication that happens between a clinician and patient for the purpose of diagnostic or therapeutic advice. Teleconsultations are particularly useful to provide healthcare services in situations where face-to-face consultation may not be easy. So far, the teleconsultations sessions are primarily supported by audio and video based communication. Although audio and video based communications are advantageous for teleconsultation, they may not fully support all the diagnostic tasks that are carried out in a face-to-face consultation session. For example, diagnosis of physical injuries may require physical handling through touch, which is not possible over video based communication. To address this, I put forward a novel approach of using tangible interfaces and artifacts to support physical diagnostic tasks in a teleconsultation sessions.
The aim of this thesis is to contribute to the understanding on how to design such tangible interfaces. The research will be carried out in three phases. In the first phase, I will investigate the experience of users with technology involved in a teleconsultation session through observation studies to gather a deep understanding on existing teleconsultation processes. These insights will inform the design for tangible interfaces to support teleconsultation session. The prototyping will be carried out in second phase. Finally, in the third phase I will field deploy the prototype to gather and understand its implication in teleconsultation sessions. This investigation will guide me towards a first conceptual understanding of the design of tangible interfaces for teleconsultation sessions. Ultimately, my aim is to invoke thinking towards natural (tangible) interfaces in supporting teleconsultations to get closer to the experience of face-to-face consultation.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...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 the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Knowledge acquisition using automated techniques
1. Methods
of
Knowledge Extraction
Deepti Aggarwal
SIEL|SERL, IIIT-Hyderabad, India
2. Agenda
Introduction to Web as a knowledge
repository
Automated extraction techniques (Input
sources, extracted structures, input pre-
processing, extraction methods, output
generation)
Issues with automated extraction
3. What is knowledge?
A familiarity with someone or something
with experience
Includes facts, information, descriptions,
skills
4. Types of Knowledge
Explicit Knowledge Implicit Knowledge
Always present Not present explicitly
explicitly in records for analysis
Objective facts having Cultural beliefs with
a definite answer subjective judgments
E.g., Hyderabad is the
capital of A.P. E.g., Hyderabad is the
best city to live in India.
6. How knowledge is
represented over the web?
Millions of documents, blogs, forums,
social networks scattered on web
Diverse topic, different formats, from
diverse people in diverse language,
different point of views
7. Benefits of knowledge
extraction over the Web
Question Answering systems
Search engines Explicit
Validating knowledge knowledge
Tracking a particular information
Predicting market, polls etc. Implicit
Community advertisements knowledge
11. Working of automated
extraction systems
Defining Input
output pre- Extraction Output
structures processing methods processing
Input
sources Database
of all facts,
Extraction system relations
16. 1. Named Entity: Definition
It is an atomic element in a body of
text.
Types: person, organization, location etc.
Different named entities when linked together,
form a relation.
17. 1. Named Entity: An
example
Sachin Tendulkarwas born in Bombay.
NE of type „Person‟ NE of type „Location‟
18. 2. Named Entity
Relationship: Structure
Subject – Relation - Object
NE of any type NE of any type
Verb, Adjective, Adverb
22. NLP libraries:
Splitting each sentence into tokens, words,
digits using Sentence Tokenizer
Recognizing language constructs, nouns,
verbs, pronouns using Part-of-speech
Tagger
Example: Sachin/NNPTendulkar/NNP
was/VBD born/VBN in/IN
Bombay/NNP
23. NLP libraries (contd.):
Linking individual constituents of a
sentence with Parser to form parse
tree
Identify types of named entity using
Named Entity Recognizer
Example: Sachin
Tendulkar/PERSON was born
inBombay/LOCATION
24. NLP libraries (contd.):
Identify all co-references and replace
with actual entity using Co -
reference Resolution tool
Identify specific meaning of a word
Word Sense Disambiguation
External vocabularies: MindNet,
DBpedia, WordNet
E.g., contextual meaning of „crane‟:
noun-bird, verb-lift/move
26. Extracting relationships
among NEs: Standard
process
named entities within a
1. Identify
sentence.
verbor adjective that
2. Find the
connects the identified named
entities.
3. Connect them together to form relation.
27. Extracting relationships
among NEs: Required
process
1. Identifypart-of-speech constructs:
noun, verb, adjective etc.
Co-references,
2. Determine
Acronyms and
abbreviations.
3. Connect them together to form a
relationship.
28. Extraction Methods
Natural Language Processing: rule based.
Based on sentence structure
E.g., for English language, a rule can be “noun-verb-noun”
Machine Learning: supervised and
unsupervised learning.
Features are detected from the training data
E.g., to extract instances of some medical diseases, system
is trained over all the symptoms of each given disease.
29. Extraction Methods (contd.)
Other methods:Vocabulary
based systems,
context based clustering.
Maintaining a mapping file of all countries and their
nationalities helps to determine nationality of a
person when his birth place is known.
Hybrid:
NLP based libraries to pre-process the input data,
applying machine learning approach to extract the
relations by using some external vocabulary as
WordNet.
31. Types of output systems
1. Identifies all mentionsof named entities
and their relations.
E.g., from a given corpus, extract all named entity
relations.
2. Identify missing relations of a database
E.g., Given a database, extract the missing attributes
of given entities from the corpus.
3. Linking various entities within a database.
E.g., Given a database, link two entities together with
some relation extracted from the corpus.
32. Working of automated
extraction systems
Defining Input
output pre- Extraction Output
structures processing methods processing
Input
sources Database
of all facts,
Extraction system relations
33. Issues with
automated
extraction
Accuracy, running time, dependency
34. Issue 1: Challenges of
language structure
Co-reference
resolution
Ambiguous, complex
sentences
Abbreviations
Acronyms
35. See an example…
“Tomcalled his father last night. They talked for
an hour. Hesaid hewould be home the next
day."
What is „He'referring to?
Tomorhis father?
36. “You see sir, I can talk English, I can walk English, I
can laugh English, I can run English, because
English is such a funny language.”
Amitabh in NamakHalal
37. Issue 2: Accuracy
Named entity detection: 90%,
relationship 50-70%.
Introduction of noise at each step.
E.g., disambiguation of acronym
„crane‟ with WordNet, introduces
contextual errors, which then
decreases accuracy of rule based
relationship extraction
38. Issue 3: Efficiency
Feature detection steps are
expensive.
Require days for computation
39. Issue 4: Dependency
on external vocabulary sources, like
Wikipedia, WordNet, MindNetetc.
Maintenance &updationof vocabulary
sources is manual: costly and require
expertise.
Limited size produce context based noise
Domain-dependent: medical domain
Corpus-dependent: Wikipedia, news
corpus
Relation specific: Dateand Place-of-
event
40. Issue 5: Problem with Implicit
knowledge extraction
Community Knowledge is learned and shared
No one can be an expert.
cultural competence and perception of
workers are fed into a system as variables.
Cultural Consensus Theory provides
models to include such variables into the
system.
41. Can we do better?
Can we seek human intelligence to improve
the accuracy of automated techniques?
42. References
[1] I. Tuomi. Data is more than knowledge:
implications of the reversed knowledge hierarchy
for knowledge management and organizational
memory. J. Manage. Inf. Syst. , 16(3):103–117, Dec.
1999.
[2] S. Sekine. Named Entity: History and Future. 2004.
[3] S. Sarawagi. Information extraction. Found. Trends
databases , 1(3):261–377, Mar. 2008.
[4] S. C. Weller. Cultural consensus theory:
Applications and frequently asked questions. Field
Methods,19(4):339–368, 2007.
43. References (contd.)
[5] Z. Syed, E. Viegas, and S. Parastatidis. Automatic
discovery of semantic relations using mindnet.
LREC,2010.
[6] G. A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and
K. Miller. Wordnet: An on-line lexical database.
International Journal of Lexicography , 3:235–244,
1990
[7] T. S. Jayram, R. Krishnamurthy, S. Raghavan, S.
Vaithyanathan, and H. Zhu. Avatar information
extraction system. IEEE Data Eng. Bull. , pages 40–48,
2006.
[8] E. Greengrass. Information retrieval: A survey, 2000.
The definition of knowledge is a matter of on-going debate among philosophersbut for our talk I have taken this definition from wikipedia
Predicting market: to predict whether people likes Lux soap or not.community advertisements. Ex: Advertising Bengalis’ community in Hyderabad for a concert in Bengali.
Scarcity is not the issue but abundance is!Easy for humans to understand the meaning lying in different documents.Becomes difficult for a user to find a document of his interest.
Too much of labour, time consuming, biasedness, For huge data, an intelligent way is to formulate an algo which can perform repetitive computation. with systems instead of manual labour. Less time consuming, Which I will talk about in my ppt.I Consider it to be more appropriate. Combines the advantages of both systems and humans. Systems: scalability and accuracy and intelligence with humans. In my thesis, I have particularly opted for this approach. Today I am not talking about this approach. I will cover this topic in some later ppt.
Systems that are built over some algorithms: the use of methods for controlling industrial processes automatically, esp by electronically controlled systems, often reducing manpower
Broad overview of how system worksAccording to me these are five main components
Broad overview of how system worksAccording to me these are five main components
Type of extraction method depends on the applicationHighly sophisticated system can achieve max. of 70% accuracy. Accuracy of automated techniques can not surpass human intelligence.