With the tons of bits of data around enterprises and the challenge to turn these data into knowledge, meaning is arguably in the systems of the best database holder.
Turning data pieces into actionable knowledge and data-driven decisions takes a good and reliable database. The RDF database is one such solution.
It captures and analyzes large volumes of diverse data while at the same time is able to manage and retrieve each and every connection these data ever get to enter in.
In our latest slides, you will find out why we believe RDF graph databases work wonders with serving information needs and handling the growing amounts of diverse data every organization faces today.
Why and how a graph database can serve you better (and at a lower cost) than a relational database when it comes to representing, storing and querying highly interconnected data
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
How to migrate to GraphDB in 10 easy to follow steps Ontotext
GraphDB Migration Service helps you institute Ontotext GraphDB™ as your new semantic graph database. GraphDB Migration Service helps you institute Ontotext GraphDB™ as your new semantic graph database.
Designed with a view to making your transitioning to GraphDB frictionless and resource-effective, GraphDB Migration Service provides the technical support and expertise you and your team of developers need to build a highly efficient architecture for semantic annotation, indexing and retrieval of digital assets.
With GraphDB Migration Services you will:
* Optimize the cost of managing the RDF database;
* Improve the performance of your system;
* Get the maximum value from your semantic solution.
The Bounties of Semantic Data Integration for the Enterprise Ontotext
If you are looking for solutions that allow you not only to manage all of your data (structured, semi-structured and unstructured) but to also make the most out of them, using a common language is critical.
Adding Semantic Technology to data integration is the glue that holds together all your enterprise data and their relationships in a meaningful way.
Learn how you can quickly design data processing jobs and integrate massive amounts of data and see what semantic integration can do for your data and your business.
www.ontotext.com
Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018Ontotext
These are slides from a live webinar taken place January 2018.
GraphDB™ Fundamentals builds the basis for working with graph databases that utilize the W3C standards, and particularly GraphDB™. In this webinar, we demonstrated how to install and set-up GraphDB™ 8.4 and how you can generate your first RDF dataset. We also showed how to quickly integrate complex and highly interconnected data using RDF and SPARQL and much more.
With the help of GraphDB™, you can start smartly managing your data assets, visually represent your data model and get insights from them.
Smarter content with a Dynamic Semantic Publishing PlatformOntotext
Personalized content recommendation systems enable users to overcome the information overload associated with rapidly changing deep and wide content streams such as news. This webinar discusses Ontotext’s latest improvements to its Dynamic Semantic Publishing (DSP) platform NOW (News on the Web). The Platform includes social data mining, web usage mining, behavioral and contextual semantic fingerprinting, content typing and rich relationship search.
The Power of Semantic Technologies to Explore Linked Open DataOntotext
Atanas Kiryakov's, Ontotext’s CEO, presentation at the first edition of Graphorum (http://graphorum2017.dataversity.net/) – a new forum that taps into the growing interest in Graph Databases and Technologies. Graphorum is co-located with the Smart Data Conference, organized by the digital publishing platform Dataversity.
The presentation demonstrates the capabilities of Ontotext’s own approach to contributing to the discipline of more intelligent information gathering and analysis by:
- graphically explorinh the connectivity patterns in big datasets;
- building new links between identical entities residing in different data silos;
- getting insights of what type of queries can be run against various linked data sets;
- reliably filtering information based on relationships, e.g., between people and organizations, in the news;
- demonstrating the conversion of tabular data into RDF.
Learn more at http://ontotext.com/.
Why and how a graph database can serve you better (and at a lower cost) than a relational database when it comes to representing, storing and querying highly interconnected data
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
How to migrate to GraphDB in 10 easy to follow steps Ontotext
GraphDB Migration Service helps you institute Ontotext GraphDB™ as your new semantic graph database. GraphDB Migration Service helps you institute Ontotext GraphDB™ as your new semantic graph database.
Designed with a view to making your transitioning to GraphDB frictionless and resource-effective, GraphDB Migration Service provides the technical support and expertise you and your team of developers need to build a highly efficient architecture for semantic annotation, indexing and retrieval of digital assets.
With GraphDB Migration Services you will:
* Optimize the cost of managing the RDF database;
* Improve the performance of your system;
* Get the maximum value from your semantic solution.
The Bounties of Semantic Data Integration for the Enterprise Ontotext
If you are looking for solutions that allow you not only to manage all of your data (structured, semi-structured and unstructured) but to also make the most out of them, using a common language is critical.
Adding Semantic Technology to data integration is the glue that holds together all your enterprise data and their relationships in a meaningful way.
Learn how you can quickly design data processing jobs and integrate massive amounts of data and see what semantic integration can do for your data and your business.
www.ontotext.com
Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018Ontotext
These are slides from a live webinar taken place January 2018.
GraphDB™ Fundamentals builds the basis for working with graph databases that utilize the W3C standards, and particularly GraphDB™. In this webinar, we demonstrated how to install and set-up GraphDB™ 8.4 and how you can generate your first RDF dataset. We also showed how to quickly integrate complex and highly interconnected data using RDF and SPARQL and much more.
With the help of GraphDB™, you can start smartly managing your data assets, visually represent your data model and get insights from them.
Smarter content with a Dynamic Semantic Publishing PlatformOntotext
Personalized content recommendation systems enable users to overcome the information overload associated with rapidly changing deep and wide content streams such as news. This webinar discusses Ontotext’s latest improvements to its Dynamic Semantic Publishing (DSP) platform NOW (News on the Web). The Platform includes social data mining, web usage mining, behavioral and contextual semantic fingerprinting, content typing and rich relationship search.
The Power of Semantic Technologies to Explore Linked Open DataOntotext
Atanas Kiryakov's, Ontotext’s CEO, presentation at the first edition of Graphorum (http://graphorum2017.dataversity.net/) – a new forum that taps into the growing interest in Graph Databases and Technologies. Graphorum is co-located with the Smart Data Conference, organized by the digital publishing platform Dataversity.
The presentation demonstrates the capabilities of Ontotext’s own approach to contributing to the discipline of more intelligent information gathering and analysis by:
- graphically explorinh the connectivity patterns in big datasets;
- building new links between identical entities residing in different data silos;
- getting insights of what type of queries can be run against various linked data sets;
- reliably filtering information based on relationships, e.g., between people and organizations, in the news;
- demonstrating the conversion of tabular data into RDF.
Learn more at http://ontotext.com/.
Data is produced at a phenomenal rate
Our ability to store has grown
Users expect more sophisticated information
How?
Objective: Fit data to a model
Potential Result: Higher-level meta information that may not be obvious when looking at raw data
Similar terms
Exploratory data analysis
Data driven discovery
Deductive learning
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data LinkingOntotext
A presentation of Ontotext’s CEO Atanas Kiryakov, given during Semantics 2018 - an annual conference that brings together researchers and professionals from all over the world to share knowledge and expertise on semantic computing.
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
The Physics Department of the University of Cagliari and the Linkalab Group invited me to talk about the Semantic Web and Linked Data - this is simply an introduction to the technologies involved.
Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise.
Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage.
In this presentation, you will learn about:
- Challenges and risks of product development and data management,
- How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements,
- How to conduct BOM and change management with Linkurious.
Text analytics for Google Spreadsheets using Text Mining add-on SpazioDati
This add-on allows Google Spreadsheet users to enhance their textual spreadsheets content by automatically extracting named entities (such as places, persons, events or concepts) and linking them to Wikipedia by using Dandelion API.
Fireside Chat with Bloor Research: State of the Graph Database Market 2020Cambridge Semantics
Sean Martin, CTO of Cambridge Semantics, Philip Howard, Research Director at Bloor Research and co-author of “Graph Database Market Update 2020”, and Steve Sarsfield, VP of Product at Cambridge Semantics, hold a fireside chat on the State of the Graph Database Market.
A podium abstract presented at AMIA 2016 Joint Summits on Translational Science. This discusses Data Café — A Platform For Creating Biomedical Data Lakes.
Introduction
Domain Expert
Goal identification and Data Understanding
Data Cleaning
Missing values
Noisy Data
Inconsistent Data
Data Integration
Data Transformation
Data Reduction
Feature Selection
Sampling Discretization
Data Curation @ SpazioDati - NEXA Lunch SeminarSpazioDati
Some insights about data curation processes @ SpazioDati. How we're using Big Data tools, Linked Data technologies, to build our products: Dandelion API (dandelion.eu) and Atoka (atoka.io).
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
Slides from my talk at Big Data Spain 2014 in Madrid.
In this talk, we will discuss our approach to bring large scale deep analytics to the masses. R is an extremely popular numerical computer environment, but scientific data processing frequently hits its memory limits. On the other hand, system to execute data intensive tasks like Hadoop or Stratosphere are not popular among R users because writing programs using these paradigms is cumbersome. We present an innovative approach to overcome these limitations using the Stratosphere/Apache Flink big data platform by means of a R package and ready-to-use distributed algorithm.
This solution allows the user, with small modifications in the R code, to easily execute distributed scenarios using popular machine learning techniques. We will cover the implementation details of the proposed solution including the architecture of the system, the functionality implemented and working examples.
In addition, we will cover what are the differences between our approach and other solutions that integrate R with Hadoop or other large-scale analytics systems. Finally, the results of the performance tests show that this solution is competitive with the already existing R implementations for small amounts of data and able to scale-up to gigabyte level.
As a part of my CRM course we had the opportunity to work on the various functions of how CRM enables organizations. The aspect that we focused on was the Data collection, storage and access that CRM enables.
Keynote: Anything is Possible: Apply Graphs to Your Most Complex Data Problem...Neo4j
During his presentation, Carl will examine the range of database usages and their corresponding database models. He’ll offer insight into the process of selecting a DBMS, based on purpose, and deployment. Carl will share the benefits of graph databases, consider potential advantages, and recommend approaches to adoption. You’ll learn how the various kinds of graphs serve different purposes and walk away with insights on the future of graph databases in general. If you can think of it, you can graph it.
Data is produced at a phenomenal rate
Our ability to store has grown
Users expect more sophisticated information
How?
Objective: Fit data to a model
Potential Result: Higher-level meta information that may not be obvious when looking at raw data
Similar terms
Exploratory data analysis
Data driven discovery
Deductive learning
Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data LinkingOntotext
A presentation of Ontotext’s CEO Atanas Kiryakov, given during Semantics 2018 - an annual conference that brings together researchers and professionals from all over the world to share knowledge and expertise on semantic computing.
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
The Physics Department of the University of Cagliari and the Linkalab Group invited me to talk about the Semantic Web and Linked Data - this is simply an introduction to the technologies involved.
Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise.
Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage.
In this presentation, you will learn about:
- Challenges and risks of product development and data management,
- How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements,
- How to conduct BOM and change management with Linkurious.
Text analytics for Google Spreadsheets using Text Mining add-on SpazioDati
This add-on allows Google Spreadsheet users to enhance their textual spreadsheets content by automatically extracting named entities (such as places, persons, events or concepts) and linking them to Wikipedia by using Dandelion API.
Fireside Chat with Bloor Research: State of the Graph Database Market 2020Cambridge Semantics
Sean Martin, CTO of Cambridge Semantics, Philip Howard, Research Director at Bloor Research and co-author of “Graph Database Market Update 2020”, and Steve Sarsfield, VP of Product at Cambridge Semantics, hold a fireside chat on the State of the Graph Database Market.
A podium abstract presented at AMIA 2016 Joint Summits on Translational Science. This discusses Data Café — A Platform For Creating Biomedical Data Lakes.
Introduction
Domain Expert
Goal identification and Data Understanding
Data Cleaning
Missing values
Noisy Data
Inconsistent Data
Data Integration
Data Transformation
Data Reduction
Feature Selection
Sampling Discretization
Data Curation @ SpazioDati - NEXA Lunch SeminarSpazioDati
Some insights about data curation processes @ SpazioDati. How we're using Big Data tools, Linked Data technologies, to build our products: Dandelion API (dandelion.eu) and Atoka (atoka.io).
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
Slides from my talk at Big Data Spain 2014 in Madrid.
In this talk, we will discuss our approach to bring large scale deep analytics to the masses. R is an extremely popular numerical computer environment, but scientific data processing frequently hits its memory limits. On the other hand, system to execute data intensive tasks like Hadoop or Stratosphere are not popular among R users because writing programs using these paradigms is cumbersome. We present an innovative approach to overcome these limitations using the Stratosphere/Apache Flink big data platform by means of a R package and ready-to-use distributed algorithm.
This solution allows the user, with small modifications in the R code, to easily execute distributed scenarios using popular machine learning techniques. We will cover the implementation details of the proposed solution including the architecture of the system, the functionality implemented and working examples.
In addition, we will cover what are the differences between our approach and other solutions that integrate R with Hadoop or other large-scale analytics systems. Finally, the results of the performance tests show that this solution is competitive with the already existing R implementations for small amounts of data and able to scale-up to gigabyte level.
As a part of my CRM course we had the opportunity to work on the various functions of how CRM enables organizations. The aspect that we focused on was the Data collection, storage and access that CRM enables.
Keynote: Anything is Possible: Apply Graphs to Your Most Complex Data Problem...Neo4j
During his presentation, Carl will examine the range of database usages and their corresponding database models. He’ll offer insight into the process of selecting a DBMS, based on purpose, and deployment. Carl will share the benefits of graph databases, consider potential advantages, and recommend approaches to adoption. You’ll learn how the various kinds of graphs serve different purposes and walk away with insights on the future of graph databases in general. If you can think of it, you can graph it.
Find out the top trending big data tools of 2019, and choose the best data analytics tool that could match your business requirement as well as brings better business insights.
Discuss the advantages of Hadoop technology and distributed data fil.pdfarhamgarmentsdelhi
Discuss the advantages of Hadoop technology and distributed data file systems. How is an
Hadoop Distributed File System different from a Relational Database system? What
organizational issues are best solved using Hadoop technology? Give examples of the type of
data they will analyze. What companies currently use Hadoopo related technologies.
Solution
The advantages of Hadoop technology
1. Scalable
Hadoop is a highly scalable storage platform, because it can store and distribute very large data
sets across hundreds of inexpensive servers that operate in parallel. Unlike traditional relational
database systems (RDBMS) that can\'t scale to process large amounts of data, Hadoop enables
businesses to run applications on thousands of nodes involving thousands of terabytes of data.
2. Cost effective
Hadoop also offers a cost effective storage solution for businesses\' exploding data sets. The
problem with traditional relational database management systems is that it is extremely cost
prohibitive to scale to such a degree in order to process such massive volumes of data. In an
effort to reduce costs, many companies in the past would have had to down-sample data and
classify it based on certain assumptions as to which data was the most valuable. The raw data
would be deleted, as it would be too cost-prohibitive to keep. While this approach may have
worked in the short term, this meant that when business priorities changed, the complete raw
data set was not available, as it was too expensive to store. Hadoop, on the other hand, is
designed as a scale-out architecture that can affordably store all of a company\'s data for later
use. The cost savings are staggering: instead of costing thousands to tens of thousands of pounds
per terabyte, Hadoop offers computing and storage capabilities for hundreds of pounds per
terabyte.
3. Flexible
Hadoop enables businesses to easily access new data sources and tap into different types of data
(both structured and unstructured) to generate value from that data. This means businesses can
use Hadoop to derive valuable business insights from data sources such as social media, email
conversations or clickstream data. In addition, Hadoop can be used for a wide variety of
purposes, such as log processing, recommendation systems, data warehousing, market campaign
analysis and fraud detection.
4. Fast
Hadoop\'s unique storage method is based on a distributed file system that basically \'maps\' data
wherever it is located on a cluster. The tools for data processing are often on the same servers
where the data is located, resulting in much faster data processing. If you\'re dealing with large
volumes of unstructured data, Hadoop is able to efficiently process terabytes of data in just
minutes, and petabytes in hours.
5. Resilient to failure
A key advantage of using Hadoop is its fault tolerance. When data is sent to an individual node,
that data is also replicated to other nodes in the cluster, which means that in the event of.
Teradata specializes in storing and analyzing structured, relational data. It has recently purchased Aster Data Systems, Inc. in order to extend its platform to include the capability of handling what is often called ‘big’, ‘semi-structured’ or multi-structured (see below) data.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven RecipesOntotext
This presentation will provide a brief introduction to logical reasoning and overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2.
While automatic reasoning has always inspired the imagination, numerous projects have failed to deliver to the promises. The typical pitfalls related to ontologies and symbolic reasoning fall into two categories:
- Over-engineered ontologies. The selected ontology language and modeling patterns can be too expressive. This can make the results of inference hard to understand and verify, which in its turn makes KG hard to evolve and maintain. It can also impose performance penalties far greater than the benefits.
- Inappropriate reasoning support. There are many inference algorithms and implementation approaches, which work well with taxonomies and conceptual models of few thousands of concepts, but cannot cope with KG of millions of entities.
- Inappropriate data layer architecture. One such example is reasoning with virtual KG, which is often infeasible.
[Webinar] GraphDB Fundamentals: Adding Meaning to Your DataOntotext
In this webinar, Desislava Hristova demonstrated how to install and set-up GraphDB™ and how one can generate RDF dataset. She also showed how one can quickly integrate complex and highly interconnected data using RDF, how to write some simple SPARQL queries and more.
In a nutshell, this webinar is suitable for those who are new to RDF databases and would like to learn how they can smartly manage their data assets with GraphDB™.
[Conference] Cognitive Graph Analytics on Company Data and NewsOntotext
Atanas Kiryakov, Ontotext's CEO, presented at the Data Day Texas 2018 conference, which took place in Austin, TX, USA, on January 27th.
Ontotext's talk was part of the Graph Day Sessions and its focus was 'Cognitive graph analytics on company data and news', aiming to demonstrate the power of Graph Analytics to create links between various datasets and lead to knowledge discovery.
Hercule: Journalist Platform to Find Breaking News and Fight Fake OnesOntotext
Hercule: a platform to help journalists detect emerging news topics, check their veracity, track an event as it unfolds and find the various angles in a story as it develops.
GraphDB Cloud: Enterprise Ready RDF Database on DemandOntotext
GraphDB Cloud is an enterprise grade RDF graph database providing high-performance querying over large volumes of RDF data. On this webinar, Ontotext demonstrates how to instantly create and deploy a fully managed Graph Database, then import & query data with the (OpenRDF) GraphDB Workbench, and finally explore and visualize data with the build in visualization tools.
[Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry ...Ontotext
This webinar continues series are demonstrating how linked open data and semantic tagging of news can be used for comprehensive media monitoring, market and business intelligence. The platform for the demonstrations is FactForge: a hub for news and data about people, organizations, and locations (POL). FactForge embodies a big knowledge graph (BKG) of more than 1 billion facts that allows various analytical queries, including tracing suspicious patterns of company control; media monitoring of people, including companies owned by them, their subsidiaries, etc.
What is GraphDB and how can it help you run a smart data-driven business?
Learn about GraphDB through the solutions it offers in a simple and easy to understand way. In the slides below we have unpacked GraphDB for you, using as little tech talk as possible.
Efficient Practices for Large Scale Text Mining ProcessOntotext
Text mining is a need when managing large scale textual collections. It facilitates access to, otherwise, hard to organise unstructured and heterogeneous documents, allows for extraction of hidden knowledge and opens new dimensions in data exploration.
In this webinar, Ivelina Nikolova, PhD, shares best practices and text analysis examples from successful text mining process in domains like news, financial and scientific publishing, pharma industry and cultural heritage.
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
Best Practices for Large Scale Text Mining ProcessingOntotext
Q&A:
NOW facilitates semantic search by having annotations attached to search strings. How compolex does that get, e.g. with wildcards between annotated strings?
NOW’s searchbox is quite basic at the moment, but still supports a few scenarios.
1. Pure concept/faceted search - search for all documents containing a concept or where a set of concepts are co-occurring. Ranking is based on frequence of occurrence.
2. Concept/faceted + Full Text search - search for both concepts and particular textual term of phrase.
3. Full text search
With search, pretty much anything can be done to customise it. For the NOW showcase we’ve kept it fairly simple, as usually every client has a slightly different case and wants to tune search in a slightly different direction.
The search in NOW is faceted which means that you search with concepts (facets) and you retrieve all documents which contain mentions of the searched concept. If you search by more than one facet the engine retrieves documents which contain mentions of both concepts but there is no restriction that they occur next to each other.
Is the tagging service expandable (say with custom ontologies)? also is it a something you offer as a service? it is unclear to me from the website.
The TAG service is used for demonstration purposes only. The models behind it are trained for annotating news articles. The pipeline is customizable for every concrete scenario, different domains and entities of interest. You can access several of our pipelines as a service through the S4 platform or you can have them hosted as an on premise solution. In some cases our clients want domain adaptation or improvements in particular area, or to tag with their internal dataset - in this case we offer again an on premise deployment and also a managed service hosted on our hardware.
Hdoes your system accomodate cluster analysis using unsupervised keyword/phrase annotation for knowledge discovery?
As much as the patterns of user behaviour are also considered knowledge discovery we employ these for suggesting related reads. Apart from these we have experience tailoring custom clustering pipelines which also rely on features like keyword and named entities.
For topic extraction how many topics can we extract? from twitter corpus wgat csn we infer?
For topic extraction we have determined that we obtain best results when suggesting 3 categories. These are taken from IPTC but only the uppermost levels which are less than 20.
The twitter corpus example is from a project Ontotext participates in called Pheme. The goal of the project is to detect rumours and to check their veracity, thus help journalists in their hunt for attractive news.
Do you provide Processing Resources and JAPE rules for GATE framework and that can be used with GATE embedded?
We are contributing to the GATE framework and everything which has been wrapped up as PRs has been included the corresponding GATE distributions.
Build Narratives, Connect Artifacts: Linked Open Data for Cultural HeritageOntotext
Many issues are faced by scholars, book researchers, museum directors who try to find the underlying connection between resources. Scholars in particular continuously emphasizes the role of digital humanities and the value of linked data in cultural heritage information systems.
Semantic Data Normalization For Efficient Clinical Trial ResearchOntotext
Clinical trials, both public and proprietary, hold a huge amount of valuable information. Acquiring knowledge from that information in a cost and time efficient manner is a major industry pain point.
Although information from clinical trials is stored in structured or semi-structured form, it is rarely coded with medical terminologies, which creates a significant level of ambiguity and increases the effort for data preparation for analytical purposes.
Gain Super Powers in Data Science: Relationship Discovery Across Public DataOntotext
What data scientists know better than anybody else is that data relationship is what matters the most. You can’t understand your data if you look at it as pieces in data silos.
In this webinar we’ll showcase how to discover relationships across public data.
Gaining Advantage in e-Learning with Semantic Adaptive TechnologyOntotext
In this presentation, we will introduce you to a solution that involves adaptive semantic technology for educational institutions and e-learning providers. You will learn how to integrate 3rd party resources, legacy assets, and other content sources to create the so-called knowledge graph of all structured and unstructured data.
How is the Semantic Web vision unfolding and what does it take for the Web to fully reach its potential and evolve from a Web of Documents to a Web of Data through universal data representation standards.
Diving in Panama Papers and Open Data to Discover Emerging NewsOntotext
Get guidance through the gigantic sea of freely released data from Panama Papers as well as Linked Open Data could. You will learn how it can empower you understanding of today’s news or any other information source.
How to Reveal Hidden Relationships in Data and Risk AnalyticsOntotext
Imagine risk analysis manager or compliance officer who can discover easily relationships like this: Big Bucks Café out of Seattle controls My Local Café in NYC through an offshore company. Such discovery can be a game changer if My Local Café pretends to be an independent small enterprise, while recently Big Bucks experiences financial difficulties.
Why Semantics Matter? Adding the semantic edge to your content,right from au...Ontotext
We’ll address a few of the basic industry pain points and show how semantics can come to the rescue, including:
How semantics can add value across the various phases of digital product development lifecycle.
Contextual authoring and content curation through automated editorial workflow solutions.
Enhanced content discoverability through relevant recommendations.
Coming together of bulletproof content delivery platform and dynamic semantic publishing technology
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
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.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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
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
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
It Don’t Mean a Thing If It Ain’t Got Semantics
1. It Don’t Mean a Thing If It Ain‘t
Got Semantics
Why consider an RDF
Graph Database?
2. 1
Making sense of what is given* has always been hard.
*the literal Latin meaning of datum is something given
3. 2
Today it is even harder: we are given tons of bits of data and
we are to turn these data into knowledge.
4. But if we don't want to be dragged down by dead weight,
we have to turn these tons of bits of data into action.
3
5. For having data is one thing,
but understanding them is another.
4
6. True understanding comes with connecting and integrating data
pieces from all kinds of data sets.
5
7. 6
To turn data pieces into building
blocks of smart integrated
approaches to solving complex
business problems, we need
systems and platforms.
8. 7
Systems capable of discovering
relationships and detecting patterns
within all kinds of data. And platforms
that capture and analyze large
volumes of diverse data.
9. 8
One way to build these systems
and platforms is with a graph
database. The graph is one of the
very expressive and powerful
models with which we can
approach thе challenge of the
huge amounts of heterogeneous,
diverse data that surround us.
10. 9
One such graph database
is the RDF Graph Database.
This specific
type of a graph
database,
is used to
manage
unstructured
and structured
data and
is invaluable
when it comes
to finding
intelligent data
management
solutions.
11. 10
An RDF Graph database
has very little viable
alternatives when it comes
to capturing, managing
and storing data
that can be easily consumed by
other users, federated across
different information systems, or
linked by a third party system.
12. 11
But all of them, Ontotext’s GraphDB included, work wonders with serving
information needs and handling the growing amounts of diverse data.
Not all RDF Graph Databases are created equal - each has its strengths
and weaknesses.
13. 12
Integrated smartly and
connected meaningfully
with an RDF graph database,
enterprise data can meet the
complexity of the captured,
created and curated
information bits.
14. 13
Ready to give an RDF Database a chance to bring meaning to your data?
15. www.ontotext.com
You can also reach us via email at
info@ontotext.com
and directly by calling
1-866-972-6686 (North America),
or +359 2 974 61 60 (Europe)
Download GRAPHDB Free
or call us for advice on
how you can do this.