The slide-doc tutorial explains
* Basic constructs for Resource Description Framework (RDF) and Ontology Web Language.
* Semantic Web Layers and types of ontologies
* Reference ontologies for Legal and Finance
* How to open and browse the ontologies in Protégé
The XBRL Bank Call Report (FFIEC 031) in FIBOJurgen Ziemer
The session explains how to load XBRL schema and instance data into the Financial Industry Business Ontology (FIBO).
Following the two step approach, participants can load their Call Report and other XBRL data into the ontology.
Financial Regulation Ontology (FRO) tutorial chapter 2 - Loading the Law Jurgen Ziemer
Chapter two shows how to populate FRO from XML files for law and regulations. The details of LKIF Legal Document are mainly for government agencies, who want to make correction or add more regulations and laws.
However, loaded content is basis for Legal reasoning in chapter three and process and design is similar for all data sources. All metadata, mapping, and lineage are stored within the ontology.
Financial Industry Semantics and OntologiesMike Bennett
Overview of the principles of semantic modelling as applied to the Financial Industry Business Ontology (FIBO), with history and development of FIBO, strategies for deployment and potential use in financial reporting.
Smart Data Webinar: A semantic solution for financial regulatory complianceDATAVERSITY
In this webinar Mike will describe a practical semantics-based approach to regulatory compliance and reporting for the financial sector using a reference ontology such as the Financial Industry Business Ontology (FIBO). This approach links the reference ontology to existing data resources with minimal disruption to existing data assets. The webinar will describe the kind of ontology that is needed for this kind of application, the principles for building or extending a reference ontology and some of the challenges in mapping this to legacy data.
This is my end to end description of what Hypercube can do for clients.
This deck explains semantics from first principles in a non technical way, before showing how these principles may be applied in managing business semantics. Finally a number of routes to technical deployment of semantics (ontology) solutions are shown.
This was presented to a workshop of the American Accounting Association in San Francisco in August 2019
Semantic Applications for Financial ServicesDavidSNewman
This presentation provides an overview of the business and technical drivers for building financial service applications using Semantic Technology. Multiple use cases are provided as examples.
Semantics in Financial Services -David NewmanPeter Berger
David Newman serves as a Senior Architect in the Enterprise Architecture group at Wells Fargo Bank. He has been following semantic technology for the last 3 years; and has developed several business ontologies. He has been instrumental in thought leadership at Wells Fargo on the application of Semantic Technology and is a representative of the Financial Services Technology Consortium (FSTC)on the W3C SPARQL Working Group.
The XBRL Bank Call Report (FFIEC 031) in FIBOJurgen Ziemer
The session explains how to load XBRL schema and instance data into the Financial Industry Business Ontology (FIBO).
Following the two step approach, participants can load their Call Report and other XBRL data into the ontology.
Financial Regulation Ontology (FRO) tutorial chapter 2 - Loading the Law Jurgen Ziemer
Chapter two shows how to populate FRO from XML files for law and regulations. The details of LKIF Legal Document are mainly for government agencies, who want to make correction or add more regulations and laws.
However, loaded content is basis for Legal reasoning in chapter three and process and design is similar for all data sources. All metadata, mapping, and lineage are stored within the ontology.
Financial Industry Semantics and OntologiesMike Bennett
Overview of the principles of semantic modelling as applied to the Financial Industry Business Ontology (FIBO), with history and development of FIBO, strategies for deployment and potential use in financial reporting.
Smart Data Webinar: A semantic solution for financial regulatory complianceDATAVERSITY
In this webinar Mike will describe a practical semantics-based approach to regulatory compliance and reporting for the financial sector using a reference ontology such as the Financial Industry Business Ontology (FIBO). This approach links the reference ontology to existing data resources with minimal disruption to existing data assets. The webinar will describe the kind of ontology that is needed for this kind of application, the principles for building or extending a reference ontology and some of the challenges in mapping this to legacy data.
This is my end to end description of what Hypercube can do for clients.
This deck explains semantics from first principles in a non technical way, before showing how these principles may be applied in managing business semantics. Finally a number of routes to technical deployment of semantics (ontology) solutions are shown.
This was presented to a workshop of the American Accounting Association in San Francisco in August 2019
Semantic Applications for Financial ServicesDavidSNewman
This presentation provides an overview of the business and technical drivers for building financial service applications using Semantic Technology. Multiple use cases are provided as examples.
Semantics in Financial Services -David NewmanPeter Berger
David Newman serves as a Senior Architect in the Enterprise Architecture group at Wells Fargo Bank. He has been following semantic technology for the last 3 years; and has developed several business ontologies. He has been instrumental in thought leadership at Wells Fargo on the application of Semantic Technology and is a representative of the Financial Services Technology Consortium (FSTC)on the W3C SPARQL Working Group.
Find the answers to:
Why go semantic?
Should i use RDF or OWL?
What is the difference, what is the link?
Did you say smart data?
In this presentations you can check RDF Integration examples, learn about Ontologies and OWL
By Tara Raafat, (PhD) Chief Ontologist at Mphasis.
Triplestores and inference, applications in Finance, text-mining. Projects and solutions for financial media and publishers.
Keystone Industrial Panel, ISWC 2014, Riva del Garda, 18 Oct 2014.
Thanks to Atanas Kiryakov for this presentation, I just cut it to size.
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2011/09/knowledgeengineering-fall2011.html and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=3_-HGnI6AZ0&list=PLDEA50C29F3D28257
Knowledge Engineering Course - fall 2011
Update on Financial Industry Business Ontology status, as presented to the Open Financial Data Group. Includes description of the canonical reference model (business conceptual ontology) and the principles by which this was built
Webinar in which Mike Bennett describes the unique approach Hypercube applies to modeling business semantics (the method used in creating the EDM Council's FIBO Business Conceptual Ontology). The end result of creating this kind of business conceptual ontology is that a firm will have a single, canonical source of meaning across all its data resources, like a golden copy but in the semantics space - so we sometimes refer to this a "Golden Ontology".
Mike explains the principles for creating an enterprise conceptual ontology. From this webinar you will learn:
3 things you need to know about ontologies
- Words are not Concepts
- Meaning is not Truth
- Syntax is not Semantics
3 things you need to do to build a Golden reference ontology:
- Classification
- Abstraction
- Partitioning
3 ways to use a Golden Ontology
- Querying across legacy data sources
- Mapping and data integration
- Reasoning with Semantic Web applications
Not only does electronic trading continue to make our financial markets more competitive, but it has brought numerous benefits to all investors This presentation seeks to provide an overview of the evolution of electronic trading, provide clear definitions of often misused terms, and demystify electronic trading strategies like high frequency trading.
Among the topics discussed in this presentation:
The modernization of our financial markets using electronic trading
Definitions of electronic trading, algorithmic trading and high frequency trading
The Securities and Exchange Commission and high frequency trading
The Commodity Futures Trading Commission and high frequency trading
Regulatory framework in place to safeguard investors who invest in markets where electronic trading is prevalent
Find the answers to:
Why go semantic?
Should i use RDF or OWL?
What is the difference, what is the link?
Did you say smart data?
In this presentations you can check RDF Integration examples, learn about Ontologies and OWL
By Tara Raafat, (PhD) Chief Ontologist at Mphasis.
Triplestores and inference, applications in Finance, text-mining. Projects and solutions for financial media and publishers.
Keystone Industrial Panel, ISWC 2014, Riva del Garda, 18 Oct 2014.
Thanks to Atanas Kiryakov for this presentation, I just cut it to size.
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2011/09/knowledgeengineering-fall2011.html and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=3_-HGnI6AZ0&list=PLDEA50C29F3D28257
Knowledge Engineering Course - fall 2011
Update on Financial Industry Business Ontology status, as presented to the Open Financial Data Group. Includes description of the canonical reference model (business conceptual ontology) and the principles by which this was built
Webinar in which Mike Bennett describes the unique approach Hypercube applies to modeling business semantics (the method used in creating the EDM Council's FIBO Business Conceptual Ontology). The end result of creating this kind of business conceptual ontology is that a firm will have a single, canonical source of meaning across all its data resources, like a golden copy but in the semantics space - so we sometimes refer to this a "Golden Ontology".
Mike explains the principles for creating an enterprise conceptual ontology. From this webinar you will learn:
3 things you need to know about ontologies
- Words are not Concepts
- Meaning is not Truth
- Syntax is not Semantics
3 things you need to do to build a Golden reference ontology:
- Classification
- Abstraction
- Partitioning
3 ways to use a Golden Ontology
- Querying across legacy data sources
- Mapping and data integration
- Reasoning with Semantic Web applications
Not only does electronic trading continue to make our financial markets more competitive, but it has brought numerous benefits to all investors This presentation seeks to provide an overview of the evolution of electronic trading, provide clear definitions of often misused terms, and demystify electronic trading strategies like high frequency trading.
Among the topics discussed in this presentation:
The modernization of our financial markets using electronic trading
Definitions of electronic trading, algorithmic trading and high frequency trading
The Securities and Exchange Commission and high frequency trading
The Commodity Futures Trading Commission and high frequency trading
Regulatory framework in place to safeguard investors who invest in markets where electronic trading is prevalent
Python & Finance: US Government Mandates, Financial Modeling, and Other Snake...ActiveState
Recently, the US SEC published a proposal for how to address the current lack of transparency of asset-backed securities through changing disclosure requirements to include the provision of a Python computer program. The goal is to capture all the complicated terms of the deal in code that can be used to analyze the cash flows in each deal and how the returns will get split up between different parties. Currently, investors, fund managers, and investment managers receive a complex, textual description of this information in the prospectus, which makes it difficult for them to perform or visualize a rigorous quantitative or if-then analysis of the asset-backed securities.
This all begs the question “Why Python?” One of the answers is that it’s open source and while there are a number of proprietary financial modeling solutions and more than a few trade description languages in use on Wall Street – there is little use asking for openness and transparency from issuers if the interpreter for that code is proprietary in nature. That said, Python has other aspects that make it a good choice for these purposes and has been widely used on Wall Street and in the finance community for financial modeling and number crunching.
At the very least, it’s not enough to have open data, one has to have open tools to fulfill the transparency requirements to establish meaningful use of financial information. Buyers, Sellers and Regulators alike need an open technology means to accurately and efficiently interpret financial information.
This presentation will discuss some of the aspects of Python that make it a good fit for the SEC’s proposal and some of the challenges and the implications of using Python for financial analysis. This presentation will also discuss some opportunities for collaboration between regulators and the open source related to the development of an ecosystem of open source projects that can exploit the availability of this proposed new rich source of financial information.
Neuroinformatics_Databses_Ontologies_Federated Database.pptxJagannath University
This will introduce and describe NIF(Neuroscience information framework), Federated databases, data federation vs data warehouse, ontology, ontology vs database, steps in creating ontology.
Neuroinformatics Databases Ontologies Federated Database.pptxJagannath University
Neuroscience Information Framework(NIF), Federated Database, Data Federation vs Data warehouse, ontology, steps in creating ontology, ontology vs database
Linked Open Data Principles, Technologies and ExamplesOpen Data Support
Theoretical and practical introducton to linked data, focusing both on the value proposition, the theory/foundations, and on practical examples. The material is tailored to the context of the EU institutions.
What is linked data
What is open data
What is the difference between linked and open data
How to publish linked data (5-star schema)
The economic and social aspects of linked data.
The Semantic Web is a vision of information that is understandable by computers. Although there is great exploitable potential, we are still in "Generation Zero'' of the Semantic Web, since there are few real-world compelling applications. The heterogeneity, the volume of data and the lack of standards are problems that could be addressed through some nature inspired methods. The paper presents the most important aspects of the Semantic Web, as well as its biggest issues; it then describes some methods inspired from nature - genetic algorithms, artificial neural networks, swarm intelligence, and the way these techniques can be used to deal with Semantic Web problems.
OpenID Foundation Research & Education Working Group Update - October 22, 2018OpenIDFoundation
OpenID Foundation Research & Education (R&E) Working Group update presented by Nick Roy (Internet2) at the OpenID Foundation Workshop at VMware on Monday, October 22, 2018.
Semantic Modeling for Information FederationCory Casanave
Semantic Modeling for Information Federation describes the UML profile and methodology for conceptual modeling and using conceptual reference models for federation and integration of information, systems and organizations.
This presentation contains both an introduction and detail appropriate for experienced architects.
The Web of Linked Open Data, or LOD, is the most relevant achievement of the Semantic Web. Initially proposed by Tim Berners-Lee in a seminal paper published in Scientific American in 2001, the Semantic Web envisions a web where software agents can interact with large volumes of structured, easy to process data. It is now when users have at our disposal the first, mature results of this vision. Among them, and probably the most significant ones, are the different LOD initiatives and projects that publish open data in standard formats like RDF.
This presentation provides an overview and comparison of different LOD initiatives in the area of patent information, and analyses potential opportunities for building new information services based on largely available datasets of patent information. Information is based on different interviews conducted with innovation agents and on the analysis of professional bibliography and current implementations.
LOD opportunities are not only restricted to information aggregators, but also to end-users and innovation agents that need to face with the difficulties of dealing with large amounts of data. In both cases, the opportunities offered by LOD need to be assessed, as LOD has just become a standard, universal method to distribute, share and access data.
The Learning Registry: Social networking for open educational resources?Lorna Campbell
This presentation will reflect on Cetis’ involvement with the Learning Registry and JISC’s Learning Registry Node Experiment at Mimas (The JLeRN Experiment), and their application to UKOER initiatives. Initially funded by the US Departments of Education and Defense, the Learning Registry (LR) is an open source network for storing and distributing metadata and curriculum activity and social usage data about learning resources across diverse educational systems.
Scanned by CamScannerScanned by CamScannerWeek 2.docxkenjordan97598
Scanned by CamScanner
Scanned by CamScanner
Week 2: Project Proposal
Category
Points
%
Description
Content
10
40
Discusses which topic scenario has been chosen and why, and how the topic is important to the study of cultural diversity.
Research Goals and Plan
10
40
Describes what the student expects to find and or what the students would be interested in learning; describes how the student plans to go about his or her research.
Organization and Editing
3
12
Proposal is well written, well organized, and free from errors in spelling, grammar, and punctuation.
Formatting
2
8
Proposal is in correct APA format, with a Title page.
Total
25
100
A quality paper will meet or exceed all of the above requirements.
Week 4: Reference List and Outline
Category
Points
%
Description
Outline and Organization
35
47
Outline lists the main topics the project will cover and incorporates the key elements of the chosen topic scenario; outline is clearly written and wellorganized.
Reference List
30
40
Reference list contains at least four credible sources.
Formatting
10
13
Submission is in correct APA format, with a Title page.
Total
75
100
A quality paper will meet or exceed all of the above requirements.
Week 7: Final Paper
Category
Points
%
Description
Content
105
70.2
Addresses each component of the chosen topic scenario, integrating concrete examples and strategies, and uses information from sources to support points.
Documentation & Formatting
15
10
Follows correct APA format, including a Title page and reference page with at least four credible sources.
Organization & Cohesiveness
10
6.6
Cohesive and well organized, with a clear introduction and a conclusion that summarizes the paper’s key points.
Editing
10
6.6
Uses proper grammar, punctuation, and spelling.
Length
10
6.6
Meets minimum length requirement of six to eight pages of text.
Total
150
100
A quality paper will meet or exceed all of the above requirements.
O F F S H O R E F I N A N C I A L C E N T R E S : P A R A S I T E S O R
S Y M B I O N T S ? *
Andrew K. Rose and Mark M. Spiegel
This article analyses the causes and consequences of offshore financial centres (OFCs). While OFCs
are likely to encourage bad behaviour in source countries, they may also have unintended positive
consequences, such as providing competition for the domestic banking sector. We derive and sim-
ulate a model of a home country monopoly bank facing a representative competitive OFC which
offers tax advantages attained by moving assets offshore at a cost that is increasing in distance to the
OFC. Our model predicts that proximity to an OFC is likely to be pro-competitive. We test and
confirm the predictions empirically. OFC proximity is associated with a more competitive domestic
banking system and greater overall financial depth.
Offshore financial centres (OFCs) are jurisdictions that oversee a disproportionate
level of financial activity by non-residents. Financial activity in OFCs is usually domi-
nated by the .
Using DBpedia for Thesaurus Management and Linked Open Data IntegrationMartin Kaltenböck
Using DBpedia for Thesaurus Creation and Management as well as Linked Open Data (LOD) Integration with PoolParty Semantic Suite (http://www.poolparty.biz) at Semantic Web Company (SWC, http://www.semantic-web.at).
STREETONOMICS: Exploring the Uncharted Territories of Informal Markets throug...sameer shah
Delve into the world of STREETONOMICS, where a team of 7 enthusiasts embarks on a journey to understand unorganized markets. By engaging with a coffee street vendor and crafting questionnaires, this project uncovers valuable insights into consumer behavior and market dynamics in informal settings."
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BONKMILLON Unleashes Its Bonkers Potential on Solana.pdfcoingabbar
Introducing BONKMILLON - The Most Bonkers Meme Coin Yet
Let's be real for a second – the world of meme coins can feel like a bit of a circus at times. Every other day, there's a new token promising to take you "to the moon" or offering some groundbreaking utility that'll change the game forever. But how many of them actually deliver on that hype?
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There is no set date for when Pi coins will enter the market.
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1. Elemental Economics - Introduction to mining.pdfNeal Brewster
After this first you should: Understand the nature of mining; have an awareness of the industry’s boundaries, corporate structure and size; appreciation the complex motivations and objectives of the industries’ various participants; know how mineral reserves are defined and estimated, and how they evolve over time.
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.
Financial Regulation Ontology (FRO) tutorial chapter 1 into
1. Financial Regulation Ontology
Tutorial chapter one – preface and introduction
“Alignment of Legal and Finance
is the foundation for the
Semantic Web approach to
compliance.”
Jurgen Ziemer, Jayzed Data Models Inc., http://finregont.com