This document discusses building knowledge graphs using DIG (Distributed Information Graphs) to integrate heterogeneous data sources. It describes the steps involved, including data acquisition, feature extraction, mapping to an ontology, entity resolution, graph construction, and deployment. As a use case, DIG has been used to build a knowledge graph from over 100 million web pages related to human trafficking to help law enforcement identify victims and prosecute traffickers.
Extracting, Aligning, and Linking Data to Build Knowledge GraphsCraig Knoblock
This document discusses building knowledge graphs by extracting, aligning, and linking data from various sources. It describes crawling websites to acquire raw data, using both structured and unstructured extraction to extract features from the data, aligning the extracted features to a common schema, and resolving entities in the data to merge records referring to the same real-world entity. It also discusses techniques for collectively resolving entities in large datasets, summarizing graphs by grouping similar nodes into super-nodes, and using the summarized graph to predict links in the original graph. The overall goal is to clean, organize, and link disconnected data into a knowledge graph that is easier to query, analyze, and visualize.
Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
Measuring Relevance in the Negative SpaceTrey Grainger
The document discusses using negative space, or hidden or missing data, to improve machine learning and algorithmic systems by connecting related concepts that may not be explicitly linked. It provides examples of how analyzing relationships between terms in a semantic knowledge graph can lead to more diverse and less biased recommendations and search results. The talk argues that simulating hypothetical user interactions could help identify potential issues with algorithm changes before exposing real users.
South Big Data Hub: Text Data Analysis PanelTrey Grainger
Slides from Trey's opening presentation for the South Big Data Hub's Text Data Analysis Panel on December 8th, 2016. Trey provided a quick introduction to Apache Solr, described how companies are using Solr to power relevant search in industry, and provided a glimpse on where the industry is heading with regard to implementing more intelligent and relevant semantic search.
"Searching for Meaning: The Hidden Structure in Unstructured Data". Presentation by Trey Grainger at the Southern Data Science Conference (SDSC) 2018. Covers linguistic theory, application in search and information retrieval, and knowledge graph and ontology learning methods for automatically deriving contextualized meaning from unstructured (free text) content.
Test trend analysis: Towards robust reliable and timely testsHugh McCamphill
This document discusses test trend analysis and making tests more robust, reliable, and timely. It proposes collecting test results data and storing it in Elasticsearch. Visualizations would then be created using Kibana to analyze test failures, slow tests, error messages, and step times. This would provide insights and help identify issues to make tests less flaky.
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachAndre Freitas
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
This document discusses building knowledge graphs using DIG (Distributed Information Graphs) to integrate heterogeneous data sources. It describes the steps involved, including data acquisition, feature extraction, mapping to an ontology, entity resolution, graph construction, and deployment. As a use case, DIG has been used to build a knowledge graph from over 100 million web pages related to human trafficking to help law enforcement identify victims and prosecute traffickers.
Extracting, Aligning, and Linking Data to Build Knowledge GraphsCraig Knoblock
This document discusses building knowledge graphs by extracting, aligning, and linking data from various sources. It describes crawling websites to acquire raw data, using both structured and unstructured extraction to extract features from the data, aligning the extracted features to a common schema, and resolving entities in the data to merge records referring to the same real-world entity. It also discusses techniques for collectively resolving entities in large datasets, summarizing graphs by grouping similar nodes into super-nodes, and using the summarized graph to predict links in the original graph. The overall goal is to clean, organize, and link disconnected data into a knowledge graph that is easier to query, analyze, and visualize.
Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
Measuring Relevance in the Negative SpaceTrey Grainger
The document discusses using negative space, or hidden or missing data, to improve machine learning and algorithmic systems by connecting related concepts that may not be explicitly linked. It provides examples of how analyzing relationships between terms in a semantic knowledge graph can lead to more diverse and less biased recommendations and search results. The talk argues that simulating hypothetical user interactions could help identify potential issues with algorithm changes before exposing real users.
South Big Data Hub: Text Data Analysis PanelTrey Grainger
Slides from Trey's opening presentation for the South Big Data Hub's Text Data Analysis Panel on December 8th, 2016. Trey provided a quick introduction to Apache Solr, described how companies are using Solr to power relevant search in industry, and provided a glimpse on where the industry is heading with regard to implementing more intelligent and relevant semantic search.
"Searching for Meaning: The Hidden Structure in Unstructured Data". Presentation by Trey Grainger at the Southern Data Science Conference (SDSC) 2018. Covers linguistic theory, application in search and information retrieval, and knowledge graph and ontology learning methods for automatically deriving contextualized meaning from unstructured (free text) content.
Test trend analysis: Towards robust reliable and timely testsHugh McCamphill
This document discusses test trend analysis and making tests more robust, reliable, and timely. It proposes collecting test results data and storing it in Elasticsearch. Visualizations would then be created using Kibana to analyze test failures, slow tests, error messages, and step times. This would provide insights and help identify issues to make tests less flaky.
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachAndre Freitas
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
Here are some options for completing your query:
- Freddie Mercury was the lead singer of Queen
- Brian May was the guitarist for Queen
- Queen was a British rock band formed in 1970
- Freddie Mercury died in 1991 from complications due to AIDS
Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances.
This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.
La bi, l'informatique décisionnelle et les graphesCédric Fauvet
The document discusses how graph databases and graph technologies can be used for business intelligence, analytics, and decision making. It provides examples of how companies in various industries like communications, logistics, online recruiting, and consumer web have used graph databases from Neo4j to power applications, gain insights, and improve user experiences. Specific use cases discussed include network management, parcel routing, social job search, recommendations, and interactive television programming. The benefits of the graph model over relational databases for complex connected data are also highlighted.
The document describes several final year project ideas including predictive analytics, a soccer playing agent team for Robocup simulation, the game of Hex, intelligent web search using semantic knowledge, plagiarism detection, text compression using LZW method, searching multiple search engines, tracking website access statistics, mobile phone location services, and image analysis tools. The projects involve developing algorithms and applications in areas such as predictive modeling, artificial intelligence, games, search engines, data compression, and computer vision.
A Semantic Web Primer: The History and Vision of Linked Open Data and the Web 3.0
There is a transformational change coming to the world-wide-web that will fundamentally alter how its vast array of data is structured, and as a result greatly enhance the way humans and machines interact with this indispensable resource. Given the inertia of existing infrastructure, this segue will be evolutionary as opposed to revolutionary, and indeed has been envisioned since the inception of the web. Come join us for a layman's look at the nature of the Web 3.0, its historical underpinnings, and the opportunities it presents.
AI to create professional opportunities
Liang Zhang discussed how AI is used at LinkedIn to create opportunities. LinkedIn uses AI throughout its platform including in personalized recommendations, search, and video. It processes vast amounts of data using machine learning models to tailor experiences for each user. Zhang outlined LinkedIn's approach to personalization at scale using global, per-user, and per-item models.
Wholi is a company that aggregates data from public online sources to build knowledge graphs about people and companies. They use machine learning and natural language processing techniques like named entity recognition and topic modeling to extract useful features from text data. They also employ bootstrapped entity and relationship learning to infer additional information. Wholi matches profiles using a deep learning classifier trained on a large dataset of over 500,000 social media profiles to determine which profiles belong to the same individuals. Their goal is to provide a more complete online identity for matching purposes.
Social media monitoring with ML-powered Knowledge GraphGraphAware
Ever wondered how can be ML used to build Knowledge Graph for allowing businesses to successfully differentiate and compete today? We will show how Computer Vision, NLP/U, knowledge enrichment and graph-native algorithms fit together to build powerful insights from various unstructured data sources.
About the speakers:
Vlasta Kus - Lead Data Scientist at GraphAWare - Machine Learning, Deep Learning and Natural Language Processing expert.
Background in particle physics research at CERN. 10+ years of experience in software development (C/C++, Java, Python) and statistical data analysis.
Neo4j certified professional.
Specialised in using Machine Learning for building Knowledge Graphs (Hume @ GraphAware).
Golven Leroy - Student - I am a engineering student who is interested in everything graph. I love travelling and good food, especially when it is cheese-related and accompanied by good wine. Wannabe Gyro Gearloose, early-age spiderman fan, and beatmaker in my free time.
NODES 2019 - Neo4j Online Developer Expo & Summit - 10th October 2019
Deep neural networks for matching online social networking profilesTraian Rebedea
The document presents a study on using deep neural networks to match online social networking profiles that belong to the same individual. It describes extracting features from profiles, including domain-specific and text-based features. A deep neural network model with multiple fully-connected layers is proposed and shown to achieve high precision and recall on a large dataset, outperforming other supervised and unsupervised baseline methods. The study demonstrates applying deep learning techniques to the task of linking profiles from different social networks that refer to the same person.
JIMS IT Flash , a monthly newsletter-An Initiative by the students of IT Department, shares the knowledge to its readers about the latest IT Innovations, Technologies and News.Your suggestions, thoughts and comments about latest in IT are always welcome at itflash@jimsindia.org.
Visit Website : http://jimsindia.org/
Overview of structured search technology. Using the structure of a document to create better search results for document search and retrieval.
How both search precision and recall is improved when the structure of a document is used.
How a keyword match in a title of a document can be used to boost the search score.
Case studies with the eXist native XML database.
Steps to set up a pilot project.
This document provides an overview of foundational research propelled by text analytics. It begins with an outline that discusses text analytics in the big data era, information extraction systems and formalisms, foundational research challenges, and conclusions. It then discusses how text analytics has become important for applications like semantic search, life science mining, e-commerce, CRM/BI, and log analysis. It notes the need for database management systems and general-purpose development and management systems to facilitate value extraction from big data by a wide range of users and skills. Core information extraction tasks like named entity recognition, relation extraction, event extraction, temporal information extraction, and coreference resolution are discussed. Several formalisms for information extraction are presented, including X
FEATURE SELECTION-MODEL-BASED CONTENT ANALYSIS FOR COMBATING WEB SPAM csandit
With the increasing growth of Internet and World Wide Web, information retrieval (IR) has
attracted much attention in recent years. Quick, accurate and quality information mining is the
core concern of successful search companies. Likewise, spammers try to manipulate IR system
to fulfil their stealthy needs. Spamdexing, (also known as web spamming) is one of the
spamming techniques of adversarial IR, allowing users to exploit ranking of specific documents
in search engine result page (SERP). Spammers take advantage of different features of web
indexing system for notorious motives. Suitable machine learning approaches can be useful in
analysis of spam patterns and automated detection of spam. This paper examines content based
features of web documents and discusses the potential of feature selection (FS) in upcoming
studies to combat web spam. The objective of feature selection is to select the salient features to
improve prediction performance and to understand the underlying data generation techniques.
A publically available web data set namely WEBSPAM - UK2007 is used for all evaluations.
This presentation was given in one of the DSATL Mettups in March 2018 in partnership with Southern Data Science Conference 2018 (www.southerndatascience.com)
This document discusses visualizing metadata quality for open government data. It proposes automatically assessing metadata quality by calculating metrics for fields like completeness, accuracy, and availability. Metrics are computed by analyzing metadata records and scoring them based on predefined evaluation criteria. Records are then ranked and displayed to users, with the goal of improving overall metadata quality over time by exposing issues.
Alphago vs Lee Se-Dol: Tweeter Analysis using Hadoop and SparkJongwook Woo
Jongwook Woo analyzed tweets about Alphago vs Lee Se-Dol's Go match using Hadoop and Spark on Azure HDInsights and IBM DashDB. The analysis found that the US and Japan tweeted the most about the match, with over 11,000 and 9,000 tweets respectively. Most tweets from all countries were positive in sentiment. Tweets peaked on days when games were played from March 9-15, 2016.
Nature is the ultimate complex system. Nature 1.0 is seeds & soil. *Evolving.* Nature 2.0 adds silicon & steel. *Evolving.*
Presented to Complex Systems Group, Stanford University, on May 4, 2018.
Kalpa Gunaratna's Ph.D. dissertation defense: April 19 2017
The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top of the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the relationships and classes are defined. Today, there exist large knowledge graphs in the research community (e.g., encyclopedic datasets like DBpedia and Yago) and corporate world (e.g., Google knowledge graph) that encapsulate a large amount of knowledge for human and machine consumption. Typically, they consist of millions of entities and billions of facts describing these entities. While it is good to have this much knowledge available on the Web for consumption, it leads to information overload, and hence proper summarization (and presentation) techniques need to be explored.
In this dissertation, we focus on creating both comprehensive and concise entity summaries at: (i) the single entity level and (ii) the multiple entity level. To summarize a single entity, we propose a novel approach called FACeted Entity Summarization (FACES) that considers importance, which is computed by combining popularity and uniqueness, and diversity of facts getting selected for the summary. We first conceptually group facts using semantic expansion and hierarchical incremental clustering techniques and form facets (i.e., groupings) that go beyond syntactic similarity. Then we rank both the facts and facets using Information Retrieval (IR) ranking techniques to pick the highest ranked facts from these facets for the summary. The important and unique contribution of this approach is that because of its generation of facets, it adds diversity into entity summaries, making them comprehensive. For creating multiple entity summaries, we simultaneously process facts belonging to the given entities using combinatorial optimization techniques. In this process, we maximize diversity and importance of facts within each entity summary and relatedness of facts between the entity summaries. The proposed approach uniquely combines semantic expansion, graph-based relatedness, and combinatorial optimization techniques to generate relatedness-based multi-entity summaries.
Complementing the entity summarization approaches, we introduce a novel approach using light Natural Language Processing (NLP) techniques to enrich knowledge graphs by adding type semantics to literals.
Building a massive biomedical knowledge graph with citizen scienceBenjamin Good
The life sciences are faced with a rapidly growing array of technologies for measuring the molecular states of living things. From sequencing platforms that can assemble the complete genome sequence of a complex organism involving billions of nucleotides in a few days to imaging systems that can just as rapidly churn out millions of snapshots of cells, biology is truly faced with a data deluge. To translate this information into new knowledge that can guide the search for new medicines, biomedical researchers increasingly need to build on the existing knowledge of the broad community. Prior knowledge can help guide searches through the masses of new data. Unfortunately, most biomedical knowledge is represented solely in the text of journal articles. Given that more than a million such articles are published every year, the challenge of using this knowledge effectively is substantial. Ideally, knowledge such as the interrelations between genes, drugs and diseases would be represented in a knowledge graph that enabled queries like: “show me all the genes related to this disease or related to any drugs used to treat this disease”. Systems exist that attempt to extract this information automatically from text, but the quality of their output remains far below what can be obtained by human readers. We are developing a new platform that taps the language comprehension abilities of citizen scientists to help excavate a queryable knowledge graph from the biomedical literature. In proof-of-concept experiments, we have demonstrated that lay-people are capable of extracting meaningful information from complex biological text. The information extracted using this community intelligence framework can surpass the efforts of individual experts in quality while also offering the potential to achieve massive scale. In this presentation we will describe the results of early experiments and introduce our prototype citizen science platform: http://mark2cure.org.
Here are some options for completing your query:
- Freddie Mercury was the lead singer of Queen
- Brian May was the guitarist for Queen
- Queen was a British rock band formed in 1970
- Freddie Mercury died in 1991 from complications due to AIDS
Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances.
This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.
La bi, l'informatique décisionnelle et les graphesCédric Fauvet
The document discusses how graph databases and graph technologies can be used for business intelligence, analytics, and decision making. It provides examples of how companies in various industries like communications, logistics, online recruiting, and consumer web have used graph databases from Neo4j to power applications, gain insights, and improve user experiences. Specific use cases discussed include network management, parcel routing, social job search, recommendations, and interactive television programming. The benefits of the graph model over relational databases for complex connected data are also highlighted.
The document describes several final year project ideas including predictive analytics, a soccer playing agent team for Robocup simulation, the game of Hex, intelligent web search using semantic knowledge, plagiarism detection, text compression using LZW method, searching multiple search engines, tracking website access statistics, mobile phone location services, and image analysis tools. The projects involve developing algorithms and applications in areas such as predictive modeling, artificial intelligence, games, search engines, data compression, and computer vision.
A Semantic Web Primer: The History and Vision of Linked Open Data and the Web 3.0
There is a transformational change coming to the world-wide-web that will fundamentally alter how its vast array of data is structured, and as a result greatly enhance the way humans and machines interact with this indispensable resource. Given the inertia of existing infrastructure, this segue will be evolutionary as opposed to revolutionary, and indeed has been envisioned since the inception of the web. Come join us for a layman's look at the nature of the Web 3.0, its historical underpinnings, and the opportunities it presents.
AI to create professional opportunities
Liang Zhang discussed how AI is used at LinkedIn to create opportunities. LinkedIn uses AI throughout its platform including in personalized recommendations, search, and video. It processes vast amounts of data using machine learning models to tailor experiences for each user. Zhang outlined LinkedIn's approach to personalization at scale using global, per-user, and per-item models.
Wholi is a company that aggregates data from public online sources to build knowledge graphs about people and companies. They use machine learning and natural language processing techniques like named entity recognition and topic modeling to extract useful features from text data. They also employ bootstrapped entity and relationship learning to infer additional information. Wholi matches profiles using a deep learning classifier trained on a large dataset of over 500,000 social media profiles to determine which profiles belong to the same individuals. Their goal is to provide a more complete online identity for matching purposes.
Social media monitoring with ML-powered Knowledge GraphGraphAware
Ever wondered how can be ML used to build Knowledge Graph for allowing businesses to successfully differentiate and compete today? We will show how Computer Vision, NLP/U, knowledge enrichment and graph-native algorithms fit together to build powerful insights from various unstructured data sources.
About the speakers:
Vlasta Kus - Lead Data Scientist at GraphAWare - Machine Learning, Deep Learning and Natural Language Processing expert.
Background in particle physics research at CERN. 10+ years of experience in software development (C/C++, Java, Python) and statistical data analysis.
Neo4j certified professional.
Specialised in using Machine Learning for building Knowledge Graphs (Hume @ GraphAware).
Golven Leroy - Student - I am a engineering student who is interested in everything graph. I love travelling and good food, especially when it is cheese-related and accompanied by good wine. Wannabe Gyro Gearloose, early-age spiderman fan, and beatmaker in my free time.
NODES 2019 - Neo4j Online Developer Expo & Summit - 10th October 2019
Deep neural networks for matching online social networking profilesTraian Rebedea
The document presents a study on using deep neural networks to match online social networking profiles that belong to the same individual. It describes extracting features from profiles, including domain-specific and text-based features. A deep neural network model with multiple fully-connected layers is proposed and shown to achieve high precision and recall on a large dataset, outperforming other supervised and unsupervised baseline methods. The study demonstrates applying deep learning techniques to the task of linking profiles from different social networks that refer to the same person.
JIMS IT Flash , a monthly newsletter-An Initiative by the students of IT Department, shares the knowledge to its readers about the latest IT Innovations, Technologies and News.Your suggestions, thoughts and comments about latest in IT are always welcome at itflash@jimsindia.org.
Visit Website : http://jimsindia.org/
Overview of structured search technology. Using the structure of a document to create better search results for document search and retrieval.
How both search precision and recall is improved when the structure of a document is used.
How a keyword match in a title of a document can be used to boost the search score.
Case studies with the eXist native XML database.
Steps to set up a pilot project.
This document provides an overview of foundational research propelled by text analytics. It begins with an outline that discusses text analytics in the big data era, information extraction systems and formalisms, foundational research challenges, and conclusions. It then discusses how text analytics has become important for applications like semantic search, life science mining, e-commerce, CRM/BI, and log analysis. It notes the need for database management systems and general-purpose development and management systems to facilitate value extraction from big data by a wide range of users and skills. Core information extraction tasks like named entity recognition, relation extraction, event extraction, temporal information extraction, and coreference resolution are discussed. Several formalisms for information extraction are presented, including X
FEATURE SELECTION-MODEL-BASED CONTENT ANALYSIS FOR COMBATING WEB SPAM csandit
With the increasing growth of Internet and World Wide Web, information retrieval (IR) has
attracted much attention in recent years. Quick, accurate and quality information mining is the
core concern of successful search companies. Likewise, spammers try to manipulate IR system
to fulfil their stealthy needs. Spamdexing, (also known as web spamming) is one of the
spamming techniques of adversarial IR, allowing users to exploit ranking of specific documents
in search engine result page (SERP). Spammers take advantage of different features of web
indexing system for notorious motives. Suitable machine learning approaches can be useful in
analysis of spam patterns and automated detection of spam. This paper examines content based
features of web documents and discusses the potential of feature selection (FS) in upcoming
studies to combat web spam. The objective of feature selection is to select the salient features to
improve prediction performance and to understand the underlying data generation techniques.
A publically available web data set namely WEBSPAM - UK2007 is used for all evaluations.
This presentation was given in one of the DSATL Mettups in March 2018 in partnership with Southern Data Science Conference 2018 (www.southerndatascience.com)
This document discusses visualizing metadata quality for open government data. It proposes automatically assessing metadata quality by calculating metrics for fields like completeness, accuracy, and availability. Metrics are computed by analyzing metadata records and scoring them based on predefined evaluation criteria. Records are then ranked and displayed to users, with the goal of improving overall metadata quality over time by exposing issues.
Alphago vs Lee Se-Dol: Tweeter Analysis using Hadoop and SparkJongwook Woo
Jongwook Woo analyzed tweets about Alphago vs Lee Se-Dol's Go match using Hadoop and Spark on Azure HDInsights and IBM DashDB. The analysis found that the US and Japan tweeted the most about the match, with over 11,000 and 9,000 tweets respectively. Most tweets from all countries were positive in sentiment. Tweets peaked on days when games were played from March 9-15, 2016.
Nature is the ultimate complex system. Nature 1.0 is seeds & soil. *Evolving.* Nature 2.0 adds silicon & steel. *Evolving.*
Presented to Complex Systems Group, Stanford University, on May 4, 2018.
Kalpa Gunaratna's Ph.D. dissertation defense: April 19 2017
The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top of the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the relationships and classes are defined. Today, there exist large knowledge graphs in the research community (e.g., encyclopedic datasets like DBpedia and Yago) and corporate world (e.g., Google knowledge graph) that encapsulate a large amount of knowledge for human and machine consumption. Typically, they consist of millions of entities and billions of facts describing these entities. While it is good to have this much knowledge available on the Web for consumption, it leads to information overload, and hence proper summarization (and presentation) techniques need to be explored.
In this dissertation, we focus on creating both comprehensive and concise entity summaries at: (i) the single entity level and (ii) the multiple entity level. To summarize a single entity, we propose a novel approach called FACeted Entity Summarization (FACES) that considers importance, which is computed by combining popularity and uniqueness, and diversity of facts getting selected for the summary. We first conceptually group facts using semantic expansion and hierarchical incremental clustering techniques and form facets (i.e., groupings) that go beyond syntactic similarity. Then we rank both the facts and facets using Information Retrieval (IR) ranking techniques to pick the highest ranked facts from these facets for the summary. The important and unique contribution of this approach is that because of its generation of facets, it adds diversity into entity summaries, making them comprehensive. For creating multiple entity summaries, we simultaneously process facts belonging to the given entities using combinatorial optimization techniques. In this process, we maximize diversity and importance of facts within each entity summary and relatedness of facts between the entity summaries. The proposed approach uniquely combines semantic expansion, graph-based relatedness, and combinatorial optimization techniques to generate relatedness-based multi-entity summaries.
Complementing the entity summarization approaches, we introduce a novel approach using light Natural Language Processing (NLP) techniques to enrich knowledge graphs by adding type semantics to literals.
Building a massive biomedical knowledge graph with citizen scienceBenjamin Good
The life sciences are faced with a rapidly growing array of technologies for measuring the molecular states of living things. From sequencing platforms that can assemble the complete genome sequence of a complex organism involving billions of nucleotides in a few days to imaging systems that can just as rapidly churn out millions of snapshots of cells, biology is truly faced with a data deluge. To translate this information into new knowledge that can guide the search for new medicines, biomedical researchers increasingly need to build on the existing knowledge of the broad community. Prior knowledge can help guide searches through the masses of new data. Unfortunately, most biomedical knowledge is represented solely in the text of journal articles. Given that more than a million such articles are published every year, the challenge of using this knowledge effectively is substantial. Ideally, knowledge such as the interrelations between genes, drugs and diseases would be represented in a knowledge graph that enabled queries like: “show me all the genes related to this disease or related to any drugs used to treat this disease”. Systems exist that attempt to extract this information automatically from text, but the quality of their output remains far below what can be obtained by human readers. We are developing a new platform that taps the language comprehension abilities of citizen scientists to help excavate a queryable knowledge graph from the biomedical literature. In proof-of-concept experiments, we have demonstrated that lay-people are capable of extracting meaningful information from complex biological text. The information extracted using this community intelligence framework can surpass the efforts of individual experts in quality while also offering the potential to achieve massive scale. In this presentation we will describe the results of early experiments and introduce our prototype citizen science platform: http://mark2cure.org.
Trafficking is a crucial violation of human rights and is considered as a form of slavery all over the world. Women and children, particularly, are in great demand in so far as the different sites of trafficking are concerned.
The document outlines plans by the YMCA of Greater Toledo to implement healthier options and provide nutritional education. It discusses replacing current vending options that do not meet "Better Choice" criteria of less than 6g fat, 2g saturated fat, 0g trans fat, 30g carbs and 200 calories per item. It also describes launching a wellness initiative in November across YMCA locations to introduce samples and information on nutrition, exercise and reading food labels. Physical activity and healthy snack guidelines for the YMCA Kids Express summer program are additionally noted.
Infografia habilidades receptivas y productivas de la lenguaLupitaSosa12
Este documento contrasta las habilidades receptivas y productivas en el uso de la lengua. Las habilidades receptivas como la comprensión oral y escrita permiten un dominio más amplio de la lengua con un repertorio más variado de registros y palabras, mientras que las habilidades productivas como la expresión oral y escrita conllevan un dominio más limitado restringido a la variedad dialectal propia. Las microhabilidades de la comprensión difieren de las de la expresión.
Este documento resume los principales aspectos tratados en los informes nacionales presentados a la Conferencia Internacional de Educación de 2008 sobre el tema de la educación inclusiva en América Latina y el Caribe. Los informes destacan la importancia de concebir la educación como un derecho humano y promover la igualdad de oportunidades, y señalan como grupos prioritarios a personas con discapacidad, indígenas y en situación de pobreza. También resaltan los desafíos de implementar currículos flexibles que respondan
Las aplicaciones móviles (apps) pueden ser herramientas útiles para la educación. Google Apps incluye herramientas como Gmail, Google Sites y Google Talk que permiten la comunicación y colaboración en línea entre estudiantes y maestros. El uso de apps en el aula requiere que los maestros sean flexibles e incorporen nuevos recursos didácticos, y que se adapten a formas cambiantes de trabajo colegiado y enseñanza.
El documento describe brevemente a cinco personas o grupos, asignándoles uno o dos adjetivos clave. Michael Jordan se describe como atlético y serio, Hayden Panettiere como bonita y baja, Adam Sandler como cómico y femenino, Florida Georgia Line como buenos cantantes y del país, y Popeye como cómico y fuerte.
El documento presenta 10 aplicaciones educativas para niños de diferentes edades. Algunas de las aplicaciones destacadas son Khan Academy, que ofrece videos educativos sobre diferentes temas; DotToDot, que ayuda a practicar números y letras de forma divertida; y El Restaurante del Dr. Panda, que enseña sobre alimentos y reciclaje mientras se cocinan platos. Otras aplicaciones cubren temas como tangram, juegos de mesa, reconocimiento de formas, mapas mentales e idiomas.
Los campos formativos de la malla curricular se enfocan en cuatro áreas clave: lenguaje, pensamiento matemático, mundo natural y lo social. Estos campos proporcionan las bases para aprendizajes futuros y se alinean con disciplinas académicas. Las competencias y aprendizajes esperados en cada campo guían la planificación educativa y evaluación de los estudiantes.
This document proposes an approach called SemTyper for assigning semantic labels from a domain ontology to data attributes in a source. SemTyper uses text similarity and statistical tests to holistically label textual and numeric data, respectively. It was evaluated on museum, city, weather, and flight data and showed improved accuracy over prior approaches while training 250x faster. SemTyper can also handle noisy data and works with any user-selected ontology.
La didáctica crítica implica una profunda reflexión del actuar docente y permite crear vínculos empáticos entre docentes y alumnos. Paulo Freire argumenta que la educación debe liberar de rasgos alienantes y ser una fuerza de cambio y libertad. El documento presenta un ejemplo de planeación didáctica crítica con intenciones, antecedentes, una consigna y reflexiones finales sobre crear espacios para que los alumnos descubran conocimiento y reflexionen sobre cómo transformar su realidad.
This document lists several place names in the Basque Country region including Bilbao, Bermeo, Balmaseda, Barakaldo, and Galdakao. It also mentions Bizkaia, Aurkibidea, and Amaiera.
Los tres principales símbolos patrios del Perú son la bandera, el escudo y el himno nacional. La bandera peruana consta de tres franjas verticales rojas y blancas y fue izada por primera vez en 1821. El escudo nacional muestra una vicuña, un árbol de quina y una cornucopia derramando monedas de oro. El himno nacional, adoptado en 1821, fue escrito por José de la Torre Ugarte y compuesto musicalmente por José Bernardo Alcedo.
Human Trafficking involves the exploitation and enslavement of victims for forced labor or sexual exploitation. The document provides background information on human trafficking including its history dating back to the 17th century slave trade, current statistics estimating 600-800,000 victims annually, and risks faced by victims such as physical and psychological harm. Key organizations working to combat human trafficking and support victims are also mentioned such as UNICEF, Truckers Against Trafficking, and important figures like Dr. Laura Lederer.
Identity Management for Virtual Organizations: A ModelVon Welch
This document presents a model for identity management (IdM) in virtual organizations developed by researchers at Tech-X. The model is based on the production and consumption of identity data to enable functions like authentication, authorization, and resource allocation. Traditionally, resource providers directly managed all identity data and functions. However, as collaborations grew in scale and complexity, identity management had to be delegated to virtual organizations. The researchers interviewed representatives from various collaborations and resource providers to understand different IdM approaches. Their proposed model describes identity data flows to account for roles, scale, and trust relationships between organizations. The goal is to improve scientific computing by providing guidance on architecting identity management for virtual organizations.
Creating a Data-Driven Government: Big Data With PurposeTyrone Grandison
The U.S. Department of Commerce collects, processes and disseminates data on a range of issues that impact our nation. Whether it's data on the economy, the environment, or technology, data is critical in fulfilling the Department's mission of creating the conditions for economic growth and opportunity. It is this data that provides insight, drives innovation, and transforms our lives. The U.S. Department of Commerce has become known as "America's Data Agency" due to the tens of thousands of datasets including satellite imagery, material standards and demographic surveys.
But having a host of data and ensuring that this data is open and accessible to all are two separate issues. The latter, expanding open data access, is now a key pillar of the Commerce Department's mission. It was this focus on enhancing open data that led to the creation of the Commerce Data Service (CDS).
The mission at the Commerce Data Service is to enable more people to use big data from across the department in innovative ways and across multiple fields. In this talk, I will explore how we are using big data to create a data-driven government.
This talk is a keynote given at the Texas tech University's Big Data Symposium.
The document discusses several concepts and projects from Sandia National Laboratories' Advanced Concepts Group related to analyzing and addressing terrorism as a complex problem. These include developing computational models and simulations to better understand terrorist recruitment and behavior ("Seldon"), creating a network of experts to share knowledge about terrorism issues ("Knownet"), and exploring novel human-machine collaboration systems using physiological sensors ("Mentor/Pal"). The goal is to improve understanding of terrorism as a complex adaptive system and develop new tools to help mitigate related threats.
Challenging Problems for Scalable Mining of Heterogeneous Social and Informat...BigMine
In today’s interconnected real world, social and informational entities are interconnected, forming gigantic, interconnected, integrated social and information networks. By structuring these data objects into multiple types, such networks become semi-structured heterogeneous social and information networks. Most real world applications that handle big data, including interconnected social media and social networks, medical information systems, online e-commerce systems, or database systems, can be structured into typed, heterogeneous social and information networks. For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks. Effective analysis of large-scale heterogeneous social and information networks poses an interesting but critical challenge.
In this talk, we present a set of data mining scenarios in heterogeneous social and information networks and show that mining typed, heterogeneous networks is a new and promising research frontier in data mining research. However, such mining may raise some serious challenging problems on scalability computation. We identify a set of problems on scalable computation and calls for serious studies on such problems. This includes how to efficiently computation for (1) meta path-based similarity search, (2) rank-based clustering, (3) rank-based classification, (4) meta path-based link/relationship prediction, and (5) topical hierarchies from heterogeneous information networks. We introduce some recent efforts, discuss the trade-offs between query-independent pre-computation vs. query-dependent online computation, and point out some promising research directions.
"Designing for Truth, Scale and Sustainability" - WSSSPE2 KeynoteKaitlin Thaney
1) The document discusses designing scientific research practices and tools for truth, scale, and sustainability. It argues current systems are designed for friction rather than collaboration and progress.
2) It notes a perception crisis where up to 70% of research cannot be reproduced, representing wasted money. Shifting practice requires a multi-faceted approach including open tools, standards, incentives and recognition to foster reuse.
3) The document calls for further adoption of "web-enabled science" through access to content, data, code and materials with rewards for openness and collaboration. It discusses rethinking professional development to lower barriers to entry and foster sustainable practitioner communities.
Making the Web Searchable - Keynote ICWE 2015Peter Mika
This document discusses making the web more searchable through semantic technologies. It begins with an overview of how web search currently works and its limitations, and then discusses how the semantic web aims to address these issues by adding explicit meaning and relationships between data on the web. It describes early skepticism of the semantic web from the information retrieval community and how it has become more practical over time. It also outlines research into semantic search done at Yahoo, including developing a knowledge graph and using semantic information to enhance search results. Finally, it discusses how semantic technologies are now being adopted more widely through efforts like schema.org.
Measuring reliability and validity in human coding and machine classificationStuart Shulman
Slides delivered as a part of #CAQDAS14.
In 1989 the Department of Sociology at the University of Surrey convened the world's first conference on qualitative software, which brought together qualitative methodologists and software developers who debated the pros and cons of the use of technology for qualitative data analysis. The result was a book (Fielding & Lee (1991) Using Computers in Qualitative Research, Sage Publications), the setting-up of the CAQDAS Networking Project and many other conferences concerning the topics over the years.
This conference will be another opportunity for methodologists, developers and researchers to come together and debate the issues.There will be keynote papers by leading experts in the field, software support clinics and opportunities to present work in progress.
http://www.surrey.ac.uk/sociology/files/Programme%20.pdf
(Keynote) Peter Mika - “Making the Web Searchable”icwe2015
This document discusses making web search more intelligent through semantic search techniques. It begins by describing how current web search works but has limitations due to not understanding context and meaning. The promise of the semantic web to address this through shared identifiers and structured data is then presented. However, challenges have prevented it from being fully realized. The document outlines research at Yahoo on semantic search, including exploiting semantic models and metadata to enhance search results. This involves techniques such as knowledge graphs, which can provide important entity information to better satisfy user search needs.
Talk straps: Interactivity between Human and Artificial IntelligenceGenoveva Vargas-Solar
The document discusses enabling interactivity between humans and artificial intelligence for subjective information seeking tasks. It proposes a model where the user and AI agent interact in a mixed-initiative system through exploration and feedback. The AI agent guides the user through the data and the user provides feedback through exploration actions. Reinforcement learning can be used to learn an optimal policy for interactions by modeling them as sequential decision making. Features of the data and interactions are used to learn the policy instead of a value function. This enables learning policies for subjective information seeking in open-world environments.
2011 SBS Singapore | Nicholas Gruen, The Coming Revolution in DataDachis Group
The document discusses how data is becoming a revolution and public good. It outlines how various industries like insurance, retail and healthcare are generating vast amounts of data that can provide value if analyzed properly. It discusses how platforms like Google, Wikipedia and others have created public goods by aggregating user data. Competitions like Kaggle are helping find people to analyze different types of data and create predictive models. The document advocates for making more data available as public goods to fuel innovation.
This document summarizes a presentation about graph databases and their use cases. It introduces graph databases and why they are useful, provides an example of using the Neo4j graph database to build a social recommendations system, and describes two case studies analyzing real-world data from Craiova, Romania to provide recommendations and analyze the local talent market.
The document discusses tools for analyzing dark data and dark matter, including DeepDive and Apache Spark. DeepDive is highlighted as a system that helps extract value from dark data by creating structured data from unstructured sources and integrating it into existing databases. It allows for sophisticated relationships and inferences about entities. Apache Spark is also summarized as providing high-level abstractions for stream processing, graph analytics, and machine learning on big data.
Data Science and AI in Biomedicine: The World has ChangedPhilip Bourne
This document discusses the changing landscape of data science and AI in biomedicine. Some key points:
- We are at a tipping point where data science is becoming a driver of biomedical research rather than just a tool. Biomedical researchers need to become data scientists.
- Data science is interdisciplinary and touches every field due to the rise of digital data. It requires openness, translation of findings, and consideration of responsibilities like algorithmic bias.
- Advances like AlphaFold2 show the power of large collaborative efforts combining data, computing resources, engineering, and domain expertise. This points to the need for public-private partnerships and new models of open data sharing.
- The definition of
ODSC Presentation "Putting Deep Learning to Work" by Alex Ermolaev, NvidiaAlex Ermolaev
We will look at the best practices for using deep learning as well as most popular use cases across several horizontal and vertical domains.
Open Data Science Conference West, San Francisco, November 2-4, 2017
This document discusses machine learning techniques for ranking and recommending information. It covers several academic papers on learning to rank, optimizing search engines using click data, and challenges in diversification, group recommendations, and context-aware recommendations. Examples of context include time of day, device, mood, season, and location. The document encourages getting in touch to discuss serious recommenders and search.
Introduction to question answering for linked data & big dataAndre Freitas
This document discusses question answering (QA) systems in the context of big data and heterogeneous data scenarios. It outlines the motivation and challenges for developing natural language interfaces for databases. The document covers the basic concepts and taxonomy of QA systems, including question types, answer types, data sources, and domains. It also discusses the anatomy and components of a typical QA system.
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing computers.
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing programs.
Similar to Building and Using a Knowledge Graph to Combat Human Trafficking (20)
Learning to Adapt to Sensor Changes and FailuresCraig Knoblock
This document discusses adapting to changes in sensors and sensor data. It presents three key points:
1) Learning to replace individual failed sensors by reconstructing their values using other correlated working sensors, even without overlapping data between the old and new sensors.
2) Automatically adapting to changes in entire sensor systems or devices by learning transformations between the old and new sensor data formats.
3) Estimating the quality of sensor adaptations and detecting sensor failures by simulating failures and comparing actual adaptation errors to similar past cases.
From Artwork to Cyber Attacks: Lessons Learned in Building Knowledge Graphs u...Craig Knoblock
Over the last few years we have been building domain-specific knowledge graphs for a variety of real-world problems, including creating virtual museums, combating human trafficking, identifying illegal arms sales, and predicting cyber attacks. We have developed a variety of techniques to construct such knowledge graphs, including techniques for extracting data from online sources, aligning the data to a domain ontology, and linking the data across sources. In his talk I will present these techniques and describe our experience in applying Semantic Web technologies to build knowledge graphs for real-world problems.
Lessons Learned in Building Linked Data for the American Art CollaborativeCraig Knoblock
Slides for the paper presented at the 2017 International Semantic Web Conference (ISWC) in Vienna Austria on Oct 23. Paper is available here: https://iswc2017.semanticweb.org/wp-content/uploads/papers/MainProceedings/382.pdf
A scalable architecture for extracting, aligning, linking, and visualizing mu...Craig Knoblock
The document proposes an architecture for extracting, aligning, linking, and visualizing multi-source intelligence data at scale. The architecture uses open source software like Apache Nutch, Karma, ElasticSearch, and Hadoop to extract structured and unstructured data, integrate the data using machine learning, compute similarities, resolve entities, construct a knowledge graph, and allow querying and visualization of the graph. An example scenario of analyzing a country's nuclear capabilities from open sources is provided to illustrate the system.
From Virtual Museums to Peacebuilding: Creating and Using Linked KnowledgeCraig Knoblock
Companies, such as Google and Microsoft, are building web-scale linked knowledge bases for the purpose of indexing and searching the Web, but these efforts do not address the problem of building accurate, fine-grained, deep knowledge bases for specific application domains. We are developing an integration framework, called Karma, which supports the rapid, end-to-end construction of such linked knowledge bases. In this talk I will describe machine-learning techniques for mapping new data sources to a domain model and linking the data across sources. I will also present several applications of this technology, including building virtual museums and integrating data sources for peacebuilding.
Semantics for Big Data Integration and AnalysisCraig Knoblock
Much of the focus on big data has been on the problem of processing very large sources. There is an equally hard problem of how to normalize, integrate, and transform the data from many sources into the format required to run large-scale anal- ysis and visualization tools. We have previously developed an approach to semi-automatically mapping diverse sources into a shared domain ontology so that they can be quickly com- bined. In this paper we describe our approach to building and executing integration and restructuring plans to support analysis and visualization tools on very large and diverse datasets.
A Semantic Approach to Retrieving, Linking, and Integrating Heterogeneous Ge...Craig Knoblock
This document proposes a semantic approach to retrieve, link, and integrate heterogeneous geospatial data. It models geospatial data using RDF and an ontology, links similar entities across data sources using geospatial relationships and similarity metrics, and integrates the data by eliminating redundancy and combining complementary properties with SPARQL queries. The approach aims to empower end-users to more easily extract, combine and use geospatial data from different sources.
Discovering Alignments in Ontologies of Linked DataCraig Knoblock
This document summarizes a research paper that presents an approach for automatically discovering schema-level mappings between ontologies of linked data sources. The approach uses an extensional approach to align concepts based on the overlap of instances belonging to different concepts. It can discover alignments between atomic and conjunctive restriction classes, as well as detect concept coverings using disjunctive restriction classes. The approach is able to find rich alignments even when ontologies are rudimentary, and can detect outliers that may require corrections.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Elizabeth Buie - Older adults: Are we really designing for our future selves?
Building and Using a Knowledge Graph to Combat Human Trafficking
1. Building and Using a Knowledge Graph to
Combat Human Trafficking
Pedro Szekely
Craig Knoblock, Jason Slepicka, Andrew Philpot, Amandeep Singh, Chengye Yin, Dipsy Kapoor, Prem Natarajan, Daniel Marcu, Kevin
Knight, David Stallard, Subessware S. Karunamoorthy, Rajagopal Bojanapalli, Steven Minton, Brian Amanatullah, Todd Hughes, Mike
Tamayo, David Flynt, Rachel Artiss, Shih-Fu Chang, Tao Chen, Gerald Hiebel and Lidia Ferreira
Information Sciences Institute, University of Southern California
Columbia University, Inferlink, Next Century, NASA JPL
2.
3. Profits per Year: $32 Billion
Average Age of Entry To Prostitutionin the US: 14
PIMP’s Profit Per Victim Per Year: $150,000
Advertising Budget On the Web: $45 Million
4. Find the locations where a potential
victim of human trafficking was advertised
6. Example: Find the locations where a
potential victim of human trafficking was advertised
> 100 million pages advertising adult services
7.
8.
9.
10. “… showing how the Semantic Web can solve
problems that end users have right now”
“A Semantic Web application is one whose
schema is expected to change”
David Karger,
keynote ESWC 2013
11. Reusable technology for building domain-specific search
Crawling Extraction
Data Acquisition
Mapping To
Ontology
Entity Linking
& Similarity
Knowledge Graph
Deployment
Query &
Visualization
Elastic
Search
Graph
DB
schema.org geonames
Crawling Mapping To
Ontology
Entity
Linking
Knowledge
Graph
Deployment
Query &
Visualization
Extraction
15. Text Extraction
“YOU don't wanna miss out
on ME :) Perfect lil booty
Green eyes Long curly black
hair Im a Irish,Armenian and
Filipino mixed princess :) ❤
Kim ❤ 7○7~7two7~7four77
❤ HH 80 roses ❤ Hour 120
roses ❤ 15 mins 60 roses”
name: Kim
eye-color: green
hair-color: black
phone: 707-727-7477
rate: $60/15min
$80/30min
$120/60min
16. Reusable technology for building domain-specific search
Crawling Extraction
Data Acquisition
Mapping To
Ontology
Entity Linking
& Similarity
Knowledge Graph
Deployment
Query &
Visualization
Elastic
Search
Graph
DB
schema.org geonames
Crawling Mapping To
Ontology
Entity
Linking
Knowledge
Graph
Deployment
Query &
Visualization
Extraction
24. Reusable technology for building domain-specific search
Crawling Extraction
Data Acquisition
Mapping To
Ontology
Entity Linking
& Similarity
Knowledge Graph
Deployment
Query &
Visualization
Elastic
Search
Graph
DB
schema.org geonames
Crawling Mapping To
Ontology
Entity
Linking
Knowledge
Graph
Deployment
Query &
Visualization
Extraction
25. Using Text Similarity to Connect the Dots
E M I LY SEXY.** wHiTe/lATin girl **bUsTy SWEET.LoTs Of fUn. Call Me.
O_U_T_C___A___L_L_S
LAYLA SEXY.** wHiTe girl ** bUsTy SWEET.LoTs Of fUn.Call Me.
O____U____T____C___A___L____L____S
LI LA SEXY.** WhiTe girl ** bUsTy SWEET.LoTs Of fUn.Call Me.
O_U_T_C___A___L_L_S
29. Reusable technology for building domain-specific search
Crawling Extraction
Data Acquisition
Mapping To
Ontology
Entity Linking
& Similarity
Knowledge Graph
Deployment
Query &
Visualization
Elastic
Search
Graph
DB
schema.org geonames
Crawling Mapping To
Ontology
Entity
Linking
Knowledge
Graph
Deployment
Query &
Visualization
Extraction
30. SPARQL ElasticSearch
> 100 million docs
> 1 billion triples
Challenging Easy
Text +
structured query
Restricted Native support
Faceted browsing Hard Easy
Familiar to
developers
No Yes
32. One Index Per Main Class
AdultService-1
Person-1
Offer-1
availableAt
seller
phone
619-319-7315
Santa Barbara
hairColor
red
price
250/hour
startDate
2014-12-07
eyeColor
blue
name
Jessica
itemProvided
Offer-2
Person-2
availableAt
Washington DC
phone
seller
email
price
250/hour
startDate
2014-05-28
AdultService-2
eyeColor
blue
name
Jessica
itemProvided
34. Adult Service As Roots
AdultService-1
Person-1Offer-1
availableAt
seller
phone
Santa Barbara
hairColor
red
price
250/hour
startDate
2014-12-07
eyeColor
blue
name
Jessica
619-319-7315
offers
Offer-2
Person-2
availableAt
Washington DC
phone
seller
email
swedebeauty@gmail.com
price
250/hour
startDate
2014-05-28
AdultService-2
eyeColor
blue
name
Jessica
offers
619-319-7315
43. Conclusions
• Using an ontology to integrate data
• Continuous schema evolution
• ElasticSearch as an RDF store
• Using a JSON-based tool chain
• Deployment of large SemanticWeb app