The document discusses Boehringer Ingelheim Pharma's development of a publication tracking system using semantic technologies. It aims to automatically import publication data, perform data curation, and enable advanced visualization and analysis. Some key challenges include cleaning noisy author and institution data, adding internal BI data, and linking to external impact factors. The system utilizes tools like PoolParty, Virtuoso, and SPARQL to semantically enrich and link publication data. It is meant to provide advanced analytics beyond what was possible in their previous manually curated system.
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Presentation given at JISC 'Managing Research Data International Workshop', Birmingham, UK. 29th March 2011
http://www.jisc.ac.uk/whatwedo/programmes/mrd/rdmevents/mrdinternationalworkshop.aspx
A Erasmus project developed in Portugal (Instituto Politécnico de Bragança, IPB) about a design to revitalize the product and its image, but always maintaining Ferreira brand values. Creative concept is linked to the "carpe diem" philosophy. Enjoy each day, celebrate each moment. Port Wine packaging is a magnetic folding box which is designed as a gift with a gift paper pattern.
Presentation: Data Activities in Austria, Lisbeth Mosnik, BMVIT (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna.
Presentation given at EuropeanaTech 2018 in Rotterdam, The Netherlands. Provides a summary of insights gained from working for about a decade on challenges related to temporal aspects of the web, persistence.
A Linked Data Dataset for Madrid Transport Authority's DatasetsOscar Corcho
Presentation done at the CIT2014 conference in Santander, describing the initial work towards providing a Linked Data dataset for Consorcio Regional de Transportes de Madrid
LOCAH Project and Considerations of Linked Data ApproachesAdrian Stevenson
Presentation given at JISC 'Managing Research Data International Workshop', Birmingham, UK. 29th March 2011
http://www.jisc.ac.uk/whatwedo/programmes/mrd/rdmevents/mrdinternationalworkshop.aspx
A Erasmus project developed in Portugal (Instituto Politécnico de Bragança, IPB) about a design to revitalize the product and its image, but always maintaining Ferreira brand values. Creative concept is linked to the "carpe diem" philosophy. Enjoy each day, celebrate each moment. Port Wine packaging is a magnetic folding box which is designed as a gift with a gift paper pattern.
Presentation: Data Activities in Austria, Lisbeth Mosnik, BMVIT (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna.
Project Description of the Linked Open Data (LOD) PILOT Austria - presented at the PiLOD event at VU Amsterdam (Netherlands) on 29.01. 2014 (see: http://www.pilod.nl/) by Martin Kaltenböck of Semantic Web Company.
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How to become the best datascientist in EuropeDigitYser
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Join the data science bootcamp starting mid September 2016 - prepare during the summer camp for coders.
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BDVA Big Data Summit 2016 (Valencia, Spain): Cross-Lingual Knowledge Extracti...webLyzard technology
Presentation of the webLyzard knowledge extraction and visual analytics portfolio to build cross-lingual big data applications; including technology showcases [1] developed within European FP7 research projects (ASAP, Pheme) and the H2020 Innovation Action InVID [2].
[1] www.weblyzard.com/research
[2] www.weblyzard.com/showcases
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...Semantic Web Company
Organising data, for most of us, means Excel spreadsheets and folders upon folders. Knowledge graph technology, however, organises data in ways similar to the brain – through context and relations. By connecting your data, you (and also machines) are able to gain context within your knowledge, helping you to make informed decisions based on all of the information you already have.
So, how can enterprises benefit from this and scale?
PwC Sr. Research Fellow for Emerging Tech, Alan Morrison, and Sebastian Gabler, Head of Sales of Semantic Web Company tackle the importance of Enterprise Knowledge Graphs and how these technologies scale business efficiency.
Learn about:
• Application-centric development to data-centric approaches
• How enterprise architects learn how to benefit from knowledge graphs: use cases
• Learn which use cases fit well to which type of graph, and which technologies are involved
• Understand how RDF helps with data integration.
• What is AI-assisted entity linking?
• Understand data virtualisation vs. materialisation
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
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Similar to Aleksandar Kapisoda: The semantic approach for tracking scientific publications
Project Description of the Linked Open Data (LOD) PILOT Austria - presented at the PiLOD event at VU Amsterdam (Netherlands) on 29.01. 2014 (see: http://www.pilod.nl/) by Martin Kaltenböck of Semantic Web Company.
Presentation: Study: #Big Data in #Austria, Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH & Martin Köhler, Austrian Institute of Technology, AIT (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna.
PoolParty Semantic Suite - Solutions for Sustainable DevelopmentMartin Kaltenböck
Presentation of the webinar: PoolParty for Sustainable Development - the Climate Tagger - taking place on 5 November 2015. More information and other presentations to be found here: http://bit.ly/1NpTcGT.
Recording of the webinar: https://www.youtube.com/watch?v=3GxtFfLL1ps.
Conference Opening Science to Meet Future Challenges, Warsaw, March 11, 2014, organized by Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw.
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How to boost your datascience skills.
Ho to recruit the most promissing young graduates.
How a company can boost its digital transformation effort.
How to become data driven.
Join the data science bootcamp starting mid September 2016 - prepare during the summer camp for coders.
In this talk we will go over the data that is available for community and Wikimedia Foundation to use, some of our findings of the last couple years and, if time allows, we can talk about EventStreams, our new public service that exposes live streams of data about Wikimedia projects. For example: live edits from various parts of the world. EventStreams is easy to consume and it is well suited to create powerful visualizations such us this one: https://wikimediablog.files.wordpress.com/2017/03/eventstreamsglobe.gif
BDVA Big Data Summit 2016 (Valencia, Spain): Cross-Lingual Knowledge Extracti...webLyzard technology
Presentation of the webLyzard knowledge extraction and visual analytics portfolio to build cross-lingual big data applications; including technology showcases [1] developed within European FP7 research projects (ASAP, Pheme) and the H2020 Innovation Action InVID [2].
[1] www.weblyzard.com/research
[2] www.weblyzard.com/showcases
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...Semantic Web Company
Organising data, for most of us, means Excel spreadsheets and folders upon folders. Knowledge graph technology, however, organises data in ways similar to the brain – through context and relations. By connecting your data, you (and also machines) are able to gain context within your knowledge, helping you to make informed decisions based on all of the information you already have.
So, how can enterprises benefit from this and scale?
PwC Sr. Research Fellow for Emerging Tech, Alan Morrison, and Sebastian Gabler, Head of Sales of Semantic Web Company tackle the importance of Enterprise Knowledge Graphs and how these technologies scale business efficiency.
Learn about:
• Application-centric development to data-centric approaches
• How enterprise architects learn how to benefit from knowledge graphs: use cases
• Learn which use cases fit well to which type of graph, and which technologies are involved
• Understand how RDF helps with data integration.
• What is AI-assisted entity linking?
• Understand data virtualisation vs. materialisation
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Deep Text Analytics - How to extract hidden information and aboutness from textSemantic Web Company
- Deep Text Analytics (DTA) is an application of Semantic AI
- DTA fuses methods and algorithms taken from language modeling, corpus linguistics, machine learning, knowledge representation and the semantic web result into Deep Text Analytics methods
- Main areas of use cases for DTA are Information retrieval, NLU, Question answering, and Recommender Systems
Leveraging Knowledge Graphs in your Enterprise Knowledge Management SystemSemantic Web Company
Knowledge graphs and graph-based data in general are becoming increasingly important for addressing various data management challenges in industries such as financial services, life sciences, healthcare or energy.
At the core of this challenge is the comprehensive management of graph-based data, ranging from taxonomy to ontology management to the administration of comprehensive data graphs along with a defined governance framework. Various data sources are integrated and linked (semi) automatically using NLP and machine learning algorithms. Tools for securing high data quality and consistency are an integral part of such a platform.
PoolParty 7.0 can now handle a full range of enterprise data management tasks. Based on agile data integration, machine learning and text mining, or ontology-based data analysis, applications are developed that allow knowledge workers, marketers, analysts or researchers a comprehensive and in-depth view of previously unlinked data assets.
At the heart of the new release is the PoolParty GraphEditor, which complements the Taxonomy, Thesaurus, and Ontology Manager components that have been around for some time. All in all, data engineers and subject matter experts can now administrate and analyze enterprise-wide and heterogeneous data stocks with comfortable means, or link them with the help of artificial intelligence.
Unified views of business-critical information across all customer-facing processes and HR-related tasks are most relevant for decision makers.
In this talk we present a SharePoint extension that supports the automatic linking of unstructured content like Word documents with structured information from other databases, such as statistical data. As a result, decision makers have knowledge portals based on linked data at their fingertips.
While the importance of managed metadata and Term Store is clear to most SharePoint architects, the significance of a semantic layer outside of the content silos has not yet been explored systematically.
We will present a four-layered content architecture and will take a close look on some of the aspects of the semantic layer and its integration with SharePoint:
- Keeping Term Store and the semantic layer in sync
- Automatic tagging of SharePoint content
- Use of graph databases to store tags
- Entity-centric search & analytics applications
Metadata is most often stored per data source, and therefore it is meaningless outside of the silo. In this presentation, we will give a live demo of a SharePoint extension that makes use of an explicit semantic layer based on standards. This approach builds the basis to start linking data across the silos in a most agile way.
The resulting knowledge graph can start on a small scale, to develop continuously and to grow with the requirements. In this presentation we will give an example to illustrate how initially disconnected HR-related data (CVs in SharePoint; statistical data from labour market; skills and competencies taxonomies; salary spreadsheets) gets linked automatically, and is then made available through an extensive search & analytics application.
Slides based on a workshop held at SEMANTiCS 2018 in Vienna. Introduces a methodology for knowledge graph management based on Semantic Web standards, ranging from taxonomies over ontologies, mappings, graph and entity linking. Further topics covered: Semantic AI and machine learning, text mining, and semantic search.
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
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The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
Machines learn better with Semantics!
See how taxonomy management and the maintenance of knowledge graphs benefit from machine learning and corpus analysis, and how, in return, machine learning gets improved when using semantic knowledge models for further enrichment.
A quick introduction to taxonomies, and how they relate to ontologies and knowledge graph. See how they can serve as part of a semantic layer in your information architecture. Learn which use cases can be developed based on this.
PoolParty GraphSearch - The Fusion of Search, Recommendation and AnalyticsSemantic Web Company
See how Cognitive Search works when based on Semantic Knowledge Graphs.
We showcase the latest developments and new features of PoolParty GraphSearch:
- Navigate a semantic knowledge graph
- Ontology-based data access (OBDA)
- Search over various search spaces: Ontology-driven facets including hierarchies
- Sophisticated autocomplete including context information
- Custom views on entity-centric and document-centric search results
- Linked data: put various tagging services such as TRIT or PoolParty Extractor in series and benefit from comprehensive semantic enrichment
- Statistical charts to explain results from unified data repositories quickly
- Plug-in system for various recommendation and matchmaking algorithms
This talk discusses how companies can apply semantic technologies to build cognitive applications. It examines the role of semantic technologies within the larger Artificial Intelligence (AI) technology ecosystem, with the aim of raising awareness of different solution approaches.
To succeed in a digital and increasingly self-service-oriented business environment, companies can no longer rely solely on IT professionals. Solutions like the PoolParty Semantic Suite utilize domain experts and business users to shape the cognitive intelligence of knowledge-driven applications.
Cognitive solutions essentially mimic how the human brain works. The search for cognitive solutions has challenged computer scientists for more than six decades. The research has matured to the extent that it has moved out of the laboratory and is now being applied in a range of knowledge-intensive industries.
There is no such thing as a single, all-encompassing “AI technology.” Rather, the large global professional technology community and software vendors are continuously developing a broad set of methods and tools for natural language processing and advanced data analytics. They are creating a growing library of machine learning algorithms to enhance the automated learning capabilities of computer systems. These emerging technologies need to be customized or combined with complementary solutions as semantic knowledge graphs, depending on the use case.
A hybrid approach to cognitive computing, employing both the statistical and knowledge base models, will have a critical influence on the development of applications. Highly automated data processing based on sophisticated machine-learning algorithms must give end user the option to independently modify the functioning of smart applications in order to overcome the disadvantages associated with ‘black-box’ approaches.
This talk will give an overview over state-of-the-art smart applications, which are becoming a fusion of search, recommendation, and question-answer machines. We will cover specific use cases in focused knowledge domains, and we will discuss how this approach allows for AI-enabled use cases and application scenarios that are currently highly prioritized by corporate and digital business players.
In this engaging, 1-hour webinar (hosted by http://www.poolparty.biz and http://www.mekon.com), you will learn how to tailor information chunks to readers’ unique needs. We will talk about:
- Benefits and principles of granular structured content, and how to start preparing your own content for this new architecture.
- Best practices for linking structured content to standards-based taxonomies, and some pitfalls to avoid
- The underlying semantic architecture that you can work toward for a truly mature and scalable approach to linking content and data
- Key use cases that you can apply to your own organization
See how you can configure your linked data eco-system based on PoolParty's semantic middleware configurator. Benefit from Shadow Concept Extraction by making implicit knowledge visible. Combine knowledge graphs with machine learning and integrate semantics into your enterprise information systems.
Technical Deep Dive: Learn more about the most complete Semantic Middleware on the market. See how to integrate semantic services into your Enterprise Information Systems.
Taxonomies and Ontologies – The Yin and Yang of Knowledge ModellingSemantic Web Company
See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
This talk addresses two questions: “How can the quality of taxonomies be defined?” and “How can it be measured?” See how quality criteria vary depending on how a taxonomy is applied, such as automatic content classification in ecommerce or a knowledge graph for data integration in enterprises. Distinguish between formal quality, structural properties, content coverage, and network topology. Investigate the advantages of standards-based and machine-processable SKOS taxonomies to be able to measure the quality of taxonomies automatically, as well as several tools and techniques for quality assessment.
Consistency is crucial to a good user experience. Designers go to great lengths to create and test consistent visual designs. The structural design of an information environment, which is of equal importance to a good user experience, is too often ignored. Blumauer presents a “four-layered content architecture” for making sense of any information environment by clearly distinguishing between the content, metadata, and semantic layers and the navigation logic. He discusses several use cases for a taxonomy-driven user experience such as personalization or dynamically created topic pages.
Opendatabay - Open Data Marketplace.pptxOpendatabay
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Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
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M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Aleksandar Kapisoda: The semantic approach for tracking scientific publications
1. Boehringer Ingelheim Pharma GmbH & Co. KG
Scientific Information Center - Aleksandar Kapisoda
Semantics 2015 - Vienna, Austria
The semantic approach for tracking
scientific publications
2. Content
1. Intro
2. Goal
3. Overview Data & Technology
4. Workflow / Pipeline
5. Challenges
6. Data Curation
7. Conclusions
8. Outlook
9. Acknowledgement
Semantics 2015
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2
4. 1895
We don't live in that kind of world
1895
Paul Otlet
Semantics 2015
Vienna, Austria
4http://www.mondotheque.be/wiki/index.php/Here
5. Paul Otlet
The Father of Information Science
He is one of several people who have been considered the
father of information science, a field he called "documentation".
As a young man, THE UTOPIAN started to think about a system that could represent
the multiple networked relations between objects of various formats with various
objectives.
Paul Otlet designed explicitly mapped multiple relations between multi-media
objects (so not just books) and allowed for constant transformation and modification.
THE UTOPIAN was imagining his universal information structure by making 'symbolic
links' from document to document.
Semantics 2015
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5
6. 1895 - Universal Decimal Classification system
http://www.mondotheque.be/wiki/index.php/Here
Paul Otlet created 1895 the
Universal Decimal Classification,
(based on the Dewey Decimal
Classification),
one of the most prominent
examples of faceted
classification
Semantics 2015
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6
7. 1895 - Universal Decimal Classification system
2015 - Taxonomies, Dictionaries & Ontologies
https://blog.semantic-web.at/wp-content/uploads/2011/02/GICS_PP.jpg
In 2015
we are creating,
editing and using
Taxonomies,
Dictionaries & Ontologies.
Semantics 2015
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7
8. 1934 - Radiated Library
Semantics 2015
Vienna, Austria
8
Paul Otlets vision
The Book of the Books
A great network of
knowledge which is centered on
documents, included the notions,
books, journals, radio, television…
In 1934, Paul Otlet laid
out this vision in
what he called “Radiated Library”
vision.
http://www.mondotheque.be/wiki/index.php/Here
9. 1934 - Radiated Library
2015 – World Wide Web / Internet
Otlet's writings have sometimes been
called prescient of the current
World Wide Web/ Internet
His vision of a great network of
knowledge was centered on documents
and included the notions of hyperlinks,
search engines, remote access,
and social networks—although these
notions were described by different names.
In 1934, Otlet laid out this vision of the
computer and internet in what he called
“Radiated Library” vision.
Semantics 2015
Vienna, Austria
9
http://www.mondotheque.be/wiki/index.php/Here
10. 1934 - Universal Information Structure
https://s-media-cache-ak0.pinimg.com/736x/7d/71/0f/7d710ffe8ad97234ebc4867546d68a28.jpg
Semantics 2015
Vienna, Austria
10
Paul Otlet
was imagining his
universal information structure
by making 'symbolic links'
from document to document,
11. 1934 - Universal Information Structure
2015 – Semantic Web
Paul Otlet
was imagining his
universal information structure
by making 'symbolic links'
from document to document,
a system that looks surprisingly similar
to what we now might call a
'Semantic Web'.
https://s-media-cache-ak0.pinimg.com/736x/7d/71/0f/7d710ffe8ad97234ebc4867546d68a28.jpg
Semantics 2015
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12. Ubiquitous Web/Symbiotic Web
Semantics @ BI - Evolution of Information Management
12
Evolution of WebTechnology
Semantic Web
Semantic Databases, Linked Data
Semantic Search, RDF,Text Mining
2020 - 2030
1990 - 2000
2000 - 2010
2010 - 2020
Year Evolution of Information Management at BI
Scientific Information Center
„Expert Searches“ based onText MiningTechnologies
Data Analysis based on SemanticTechnololgies
Version
Web 4.0
Web 1.0
Web 2.0
Web 3.0
Scientific Information Center
Text Mining, BI-internal Wikis (MediaWiki)
Social Web
Blogs, Wikis
Keyword Search
World Wide Web
Portals, Internet
Databases, File Servers, SQL
Scientific Library
E-Journals
LinkSolver
Interaction between
humans and machines
in symbiosis
Comparison WebTechnology vs. BI internal Information Management
13. BI – Publication Tracker
Goal
Why BI needs
a Publication Tracking System?
Semantics 2015
Vienna, Austria
13
14. BI – Publication Tracker
Goals
Automatically Data import
ContentCuration
State of the Art Visualisation
Storage in a semantic database
Data Analysis possible
Semantics 2015
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14
Manually added database
NoContent Curation
Primite Visualisation
Storage
No Data Analysis possible
Scientific Publication Database
(State July 2015)
BI Scientific PublicationTracking
Going live September 2015
15. Goal – Data Analysis
Number of BI Research Publications in 2015 (Q1, Q2)
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Sample Data
TA: Therapeutic Area
16. Goal – Data Analysis
Impact Factors 2015 (Q1 + Q2) & Published Article
Semantics 2015
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16
Sample Data
TA: Therapeutic Area
17. Goal - Analysing data
Based on Impact Factor Journal Ranking
Semantics 2015
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17
https://sciencetechblog.files.wordpress.com/2011/05/journal-impact-factors-2008_1.jpg
18. BI – Publication Tracker
Why BI is using
Semantic Technology
for
Publication Tracking?
Semantics 2015
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18
19. Scientific Publication
How it is looking like?
Semantics 2015
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19
http://www.ncbi.nlm.nih.gov/pubmed/26210363
20. BI – Publication Tracker
Overview Data & Technology
• Data & Data Storage
• xml. files from OVID http://www.ovid.com
• MS Excel (sheet)
• Virtuoso Universal Server as a Triple Store http://virtuoso.openlinksw.com/
• Systems
• PoolParty (Thesaurus Server) https://www.poolparty.biz/portfolio-item/poolparty-thesaurus-server
• PoolParty Graph Search https://www.poolparty.biz/tag/graph-search
• SPARQL http://www.w3.org/TR/sparql11-query/#docResultDesc
• Spring https://de.wikipedia.org/wiki/Spring_(Framework)
• Maven http://maven.apache.org
Semantics 2015
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20
21. BI – Publication Tracker
Workflow / Pipeline
auto-alerts from ovid (.xml file)
Alert Profile
(SearchTerms)
Scheduled Alerts
Content Enrichment
Admin User Interface
SIC Crawler
21
Current awareness searches
Data Curation
Thesaurus Management System
“reflects the average
number of citations to
recent articles published
in a journal”
Impact Factor List
Virtuoso Database
BI PublicationTracker
User Interface
22. Data Curation & Analysis
Challenges
Cleaning noisy data
from ovid.xml
Authors
Institutions
Cleaning noisy data
from ovid.xml
Authors
Institutions
Adding
BI internal data
Division
Theraupeutic Area
Location
Adding
BI internal data
Division
Theraupeutic Area
Location
Adding
external data
Impact Factors
Lightweight
High scalable
User Interface
Adding
external data
Impact Factors
Lightweight
High scalable
User Interface
Challenges
Semantics 2015
Vienna, Austria
22
23. Data Curration - Challenge
Data from .xml
Cleaning noisy data
from ovid.xml
Authors
Institutions
Cleaning noisy data
from ovid.xml
Authors
Institutions
Adding
BI internal data
Division
Theraupeutic Area
Location
Adding
BI internal data
Division
Theraupeutic Area
Location
Building
Web Application
Lightweight
High scalable
User Interface
Building
Web Application
Lightweight
High scalable
User Interface
Noisy Data
PoolParty
Thesaurus Server Admin GUI User GUI
Semantics 2015
Vienna, Austria
23
33. Data Curration - Challenge
BI internal Data
Cleaning noisy data
from ovid.xml
Authors
Institutions
Cleaning noisy data
from ovid.xml
Authors
Institutions
Adding
BI internal data
Division
Theraupeutic Area
Location
Adding
BI internal data
Division
Theraupeutic Area
Location
Building
Web Application
Lightweight
High scalable
User Interface
Building
Web Application
Lightweight
High scalable
User Interface
Missing BI internal data
PoolParty
Thesaurus Server Admin GUI User GUI
Semantics 2015
Vienna, Austria
33
35. Data Visualisation & Analysis
Challenges
Cleaning noisy data
from ovid.xml
Authors
Institutions
Cleaning noisy data
from ovid.xml
Authors
Institutions
Adding
BI internal data
Division
Theraupeutic Area
Location
Adding
BI internal data
Division
Theraupeutic Area
Location
Adding
external data
Impact Factors
Lightweight
High scalable
User Interface
Adding
external data
Impact Factors
Lightweight
High scalable
User Interface
Visualization & Analysis
PoolParty
Thesaurus Server Admin GUI
Visualisation &
Analysis
Semantics 2015
Vienna, Austria
35
37. Conclusions
Cleaning noisy data
from ovid.xml
Authors
Institutions
Cleaning noisy data
from ovid.xml
Authors
Institutions
Adding & Linking
BI internal data
Division
Theraupeutic Area
Location
Adding & Linking
BI internal data
Division
Theraupeutic Area
Location
Adding & Linking
external data
Impact Factors
Lightweight
High scalable
User Interface
Adding & Linking
external data
Impact Factors
Lightweight
High scalable
User Interface
PoolParty
Thesaurus Server Admin GUI
Visualisation &
Analysis
Semantics 2015
Vienna, Austria
37
38. Conclusions
• Linked Data:
Reuse of the Data (SPARQL Endpoint)
Domain Expert
• Data Network Solution
Semantics 2015
Vienna, Austria
38
39. Outlook:
What We Want to achieve in the Next Steps
Technology
User Perspective
GUI
• Export of Search Results
Optimization of data of data import
• Using Ovid RSS-feeds for updates
Semantics 2015
Vienna, Austria
39