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Linking SharePoint Documents with Structured Data
Andreas Blumauer, director of Poolparty Ltd, discusses the importance of linking SharePoint documents with structured data to enhance the usability and findability of enterprise information through knowledge graphs. He highlights various applications of knowledge graphs, including HR analytics and life sciences research, which allow for more efficient data querying and analysis. Additionally, the document outlines features like automatic tagging and semantic searching to improve data governance and enterprise knowledge management.
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Linking SharePoint Documents with Structured Data
- 1. Andreas Blumauer Director PoolParty Ltd,UK Linking SharePoint Documents with Structured Data Towards Unified Views of Business-critical Information
- 2. Graph-based Introduction 2 Semantic Web Company Founder & CEOof Andreas Blumauer developer & vendor of 2004founded 7.0 version active at based on Vienna located part of Enterprise Knowledge Graphs manages standard for part of >200 serves customers Taxonomies Ontologies standard for graduates Text Mining used for Graph databases integrates with PoolParty Software Ltd Director of parent company of London located named by
- 3. 3Problem Statements from our customers Datais often bound to specific applications which reduces reuse of the data dramatically. Tight coupling of data to applications limits the options to connect the data to other internal and external sources. People should participate in a data sharing culture which is beneficial for the whole enterprise. When we cannot express data in relational formats we typically leave the data in a non-machine readable format in a document. Data scientists should benefit from more consistent semantics of data to be reused for various machine learning tasks.
- 4. 4Gartner Hype Cycle for Artificial Intelligence, 2018 >Read more Organizations can expect significant value from knowledge graphs in many areas: ● ... ● Interrelated data is contextualized data, thereby aiding its discovery and findability via implicit and indirect connections. ● Once structured in the form of a knowledge graph, unstructured data can be queried, thereby preprocessing it for analysis.
- 5. 5Example: HR Analytics As anHR manager, for upcoming training programmes, I want to identify employees who ● have a certain skill set ● have a specific degree ● have skills that are increasingly important on the labour market ● fall into a specific salary range Employee database Resumes Labour market statistics → Linking Structured to Unstructured Data
- 6. 6Example: Research in Life Sciences Asa researcher in pharmaceutical industry, I want to plan new experiments more efficiently. I want to know what’s already available. I’m interested in former experiments where ● certain genes were tested ● under specific treatment conditions ● in a target therapeutic area ● with help from categorisation systems like ‘disease hierarchies’ UniProt, ChEMBL Experiments Documentation MeSH DrugBank → Linking Structured to Unstructured Data and to Industry Knowledge Graphs
- 7. 7Making Use of Knowledge Graphs Experiments →Knowledge Graphs serve as means to enrich unstructured information to provide a rich set of additional access points to document repositories
- 8. 8Making Use of Knowledge Graphsand Automated Reasoning
- 9. 9Example: Recommender System Project database Meeting notes Interestand working profiles → Benefiting from automatic recommendations based on personal profiles As an expert working for an International Development Bank, I want ● to get a better way to disseminate information to our staff so that projects would be more efficient and more successful, ● to receive relevant articles and information when meetings are scheduled, ● during searches, and eventually to power a chat bot.
- 10. “Things but notStrings”: Semantic Knowledge Graphs manage resources, not just terms http://www.my.com/ taxonomy/62346723 prefLabel Retina image http://www.my.com/ images/90546089 http://www.my.com/ taxonomy/ 97345854 prefLabel Funduscope altLabel Ophthalmoscope http://www.mycom.com /taxonomy/4543567 prefLabel Diagnostic Equipment has broader
- 11. PoolParty Semantic Suite Most complete Semantic Middlewareon the Global Market 11 Bain Capital is a venture capital company based in Boston, MA. Since inception it has invested in hundreds of companies including AMC Entertainment, Brookstone, and Burger King. The company was co-founded by Mitt Romney. Taxonomy & Ontology Server Entity Extractor & Semantic Classifier Data Integration & Data Linking Unstructured Data Semi- structured Data Structured Data UnifiedViews PoolParty GraphSearch Identify new candidate concepts to be included in a controlled vocabulary Controlled vocabularies as a basis for highly precise knowledge extraction and text classification Entity Extractor informs all incoming data streams about its semantics and links them RDF Graph Database Factsheet Schema mapping based on ontologies
- 12. 12Integrating data along the LinkedData Life Cycle
- 13. PowerTagging for SharePoint and PoolParty ata Glance 13 ▸ On-premise: PowerTagging Server can be part of SharePoint farm’s application server ▸ Hybrid scenario: PowerTagging Server can be both on-site or Azure located ▸ Integrations: SharePoint 2013 / 2016, Office 365 PowerTagging for SharePoint has been developed by Soitron Group
- 14. Autotagging & Manual Tagging 14 Tagsare added automatically based on PoolParty Entity Extractor
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- 19. PowerSearch: Introducing your own Knowledge Graph 19▸ Entity-centric views and context information about your search term ▸ Additional search refiners ▸ ‘Traversing the knowledge graph’ ▸ Highly configurable ‘knowledge base’ ▸ Search, Learn, and Understand
- 20. Benefits PowerTagging ▸ Consistent taggingbased on controlled vocabularies ▸ Automatic tag suggestions based on text analytics ▸ User-friendly enterprise taxonomy management ▸ Taxonomies kept in sync with Term Store PowerSearch ▸ Concept based search: autocomplete from taxonomy ▸ Automatic use of synonyms: get precise results ▸ Configurable search refiners: faceted search based on taxonomy hierarchy ▸ Include fact box for search term in search results: benefit from additional context information 20
- 21. Two Integration Scenarios 21 DAM/CMS Option 1: Conceptsare derived from a taxonomy and tagging is stored together with the asset in the DAM/CMS http://apple.com/macmini.jpg http://apple.com/graph/1234 PoolParty API Option 2: Concepts are derived from taxonomies, and any tag event is stored in a Graph Database to link all assets with concepts from the graph. DAM/CMS http://apple.com/macmini.jpg http://apple.com/graph/1234 PoolParty API http://apple.com/macmini.jpg http://apple.com/macmini.jpg http://apple.com/graph/1234 LD Store Wed 3 May, 2017User4711 DAM/CMS API Pool Party Pool Party
- 22. Maturity Model Roadmap fora more agile Data Governance Framework 22
- 23. GraphSearch over Integrated Data incl.SharePoint 23
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- 25. How it works 25 Employee database Resumes Labourmarket statistics PoolParty UnifiedViews RDF Graph Database PoolParty GraphSearch PoolParty Thesaurus Server PoolParty User Now I can identify employees along many dimensions.
- 26. CONNECT Andreas Blumauer Director, PoolPartySoftware Ltd CEO, Semantic Web Company GmbH ▸ andreas.blumauer@semantic-web.com ▸ http://linkedin.com/in/andreasblumauer ▸ https://twitter.com/semwebcompany 26 © Semantic Web Company - http://www.semantic-web.at/ and http://www.poolparty.biz/