Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking

making sense of text and data
Semantics 2018, Vienna
Analytics on Big Knowledge
Graphs Deliver Entity Awareness
and Help Data Linking

Presentation Outline
o Ontotext Introduction
o Technology and Portfolio
o Cognitive Analytics Meet Big Knowledge Graphs
o Big Company Data: Knowing, Matching and Cleaning
o Product Roadmap
Presentation Outline

o Global business information will be
key for competitiveness tomorrow
o Adequate business decisions require global information!
 Analytics cannot deliver deep market/business insights based only on proprietary data
 Broader context and signals are needed
o Merging data requires concept and entity awareness
 Entity matching across databases requires rich knowledge about the entity
 Entity recognition in text requires even more context
o Ontotext makes this possible
Vision

Mission
We help enterprises to identify meaning across:
o Diverse databases & unstructured data
We combine:
o Proprietary & Global data
o Graph databases & Text mining
o Symbolic reasoning & Machine learning

History and Essential Facts
o Started in year 2000 as Semantic Web pioneer
 Part of Sirma Group: ~400 persons, listed at Sofia Stock Exchange
 Got spun-off and took VC investment in 2008
o R&D Center in Sofia, 80% sales in USA and UK
 Over 400 person-years invested in R&D
 Multiple innovation awards: Washington Post, BBC, FT, ...
o Member of multiple industry bodies
 W3C, EDMC, ODI, LDBC, STI, DBPedia Foundation

Best known for GraphDB
“Despite all of this attention the market is
dominated by Neo4J and Ontotext
(GraphDB), which are graph and RDF
database providers respectively. These are
the longest established vendors in this space
(both founded in 2000) so they have a
longevity and experience that other
suppliers cannot yet match. How long this
will remain the case remains to be seen.”
Bloor Group report
Graph Databases, April 2015
http://www.bloorresearch.com/technology/graph-databases/

Fancy Stuff and Heavy Lifting
o We do advanced analytics:
We predicted BREXIT
 14 Jun 2016 whitepaper:
#BRExit Twitter Analysis: More Twitter Users
Want to Split with EU and Support #Brexit
https://ontotext.com/white-paper-brexit-twitter-analysis/
o But most of the time we do the
heavy lifting of data integration
and information extraction
 Enabling data scientists can do fancy things

Discovery in Knowledge Graphs
o Find suspicious
patterns like:
 Company in USA
 Controls another
company in USA
 Through a company
in an off-shore zone
o Show news
relevant to
these companies
8

Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking

Technology Excellence Delivered
o Unique technology mix: GraphDBTM engine + Text mining
o Robust technology: powers BBC.CO.UK/SPORT and FT.COM
o We serve the most knowledge intensive enterprises:

Linking Text to Big Knowledge Graphs
1. Integrate relevant structured data
 Build a Big Knowledge Graph from proprietary databases
and taxonomies combined with Linked Open Data
2. Infer new facts and unveil relationships
 Performing reasoning across data from different sources
3. Link text mentions to the Knowledge Graph
 Using text-mining to automatically discover references to
concepts and entities
4. Hybrid Queries and Search in GraphDB

Text Analytics:
Semantic Disambiguation
GraphDB
Vocabulary
Vocabulary Gazetteer
Disambiguation
NLP Pipeline
Language Detection
POS
...
...
...
Relevance Ranking
...
Dynamic
Vocabulary
Get
Suggestions
Annotate
Content
Apple : Organisation
Tim Cook : Person, CEO
Tim Cook : Person, Footballer
Samsung : Organisation
Apple : Organisation
Tim Cook : Person, CEO
Tim Cook : Person, Footballer
Samsung : Organisation
87% - Tim Cook : Person, CEO
68% - Apple : Organisation
56% - Samsung : Organisation
Apple CEO Tim Cook was
at a conference with the
CEO of Samsung. Tim
explained how smart
phones are changing the
consumer electronics
market.
Suggestions
Entity Detection from
Vocabulary
Disambiguation
Relevance

Sample Knowledge Graph with Metadata
Document
Apple
Organisation
SamsungAnnotation
textpos:123,142
relevance:56%
mentions
Annotation
textpos:123,142
relevance:68%
about
Tim Cook Person
target
target
tag
tag
ceo
type
type
competitor
Annotation
textpos:123,142
relevance:87%
about
target
tag
USA
NASDAQ
Computer
Hardware
location
exchange
sector
type

Linking News to Big Knowledge Graphs
o Link text to
knowledge
graphs
o Navigate
from news
to concepts
and from
there to
other news
Try it at http://now.ontotext.com #15

Semantic Media Monitoring
For each entity:
o popularity
trends
o relevant news
o related
entities
o knowledge
graph
information
16Try it at http://now.ontotext.com

Visual Graph: Node details
#17

Big KG Demosntration
o DBpedia (the English version) 496M
o Geonames (all geographic features on Earth) 150M
o owl:sameAs links between DBpedia and Geonames 471K
o GLEI (global company register data) 3M
o Panama Papers DB (#LinkedLeaks) 20M
o Other datasets and ontologies: WordNet, WorldFacts, FIBO
o News metadata (2000 articles/day enriched by NOW) 673M
o Total size (1.8B explicit + 328M inferred statements) 2 168М

GraphDB Workbench: Class Instances & Hierarchy
#19

GraphDB Workbench: Class Relations
#20

Context and Awareness
o Context allows concepts to be identified, the way people do
o Big knowledge graph can provide context for the entities in it
 Differentiating features and similar nodes
 How important and how popular it is
 Related entities and concepts
 Entities it is typically mentioned together with (co-occurrence)
o This is awareness!
o The kind of knowledge that people mean saying "I am aware of X" or
"She is cognizant of Y"

The Critical Mass
Malcolm Gladwell claims that one needs
to devote 10 000 hours to become an
expert in something, e.g. violin or hokey
(Outliers)

The Critical Mass
o A cognitive system needs:
 To know 1B facts
 About 100M concepts and entities
 Read 1M news articles
o In order to reach concept and entity
awareness in a specific domain
 The level of awareness that people mean saying
“My background is X”

Let’s play an Awareness game!
o Important airports near London?
o The most popular banks in UK?
o Companies similar to Google?
o People mentioned together with IBM in news?

We are getting closer!
o Our Business Knowledge Model can already answer many of
these questions better than you
o Most of this intelligence is available in the Ontotext Platform
o Knowledge model = KG + text mining + analytics
o We already offer two such knowledge models:
 Business and general news: one for processing general business master data (like people,
organizations, locations and their mentions in the news)
 Life sciences and healthcare

Customized Cognitive Marketing Intelligence
o Developing from scratch cognitive system
with global knowledge is infeasible
o We can provide and “onboard” one for you:
 Suggest open and commercial data sources
 Integrate them with your proprietary data sources
 Tune text analytics
 Develop specific analytics, reports, dashboards, etc.
o We can also maintain it for you:
 Various support and maintenance options, including …
 Managed data service: updates, monitoring, data quality

o POL data is the most common type of master/reference data
 Considering business applications and news
o Open POL data is available in vast quantities
 Geonames covers locations exhaustively; DBPedia covers well popular POL entities; Wikidata, …
 Open company data grows: OpenCorporates, GLEI, open national registers, various “data leaks”
o Within 3 years exhaustive global POL data will be commodity!
 And it will be widely used for BI and decision making
o Ontotext delivers Global POL data solutions today.
 We make them more affordable with more cognitive analytics
Person, Organization, Location (POL) Data

Oct
2016
Company Data Species (1/2)
Category Representatives Size (Orgs.)
Exhaustive Global Databases Dun & Bradstreet, BvD, Factset > 200M
Rich Company Databases Capital IQ (S&P), Thomson Reuters (various) 5-10M
Investment Databases CrunchBase, PitchBook, CBI, DJ Venture Source 200-600K
Very Big Open Databases OpenCorporates 130M
Global Official Open Databases GLEI (Global Legal Identifier), EU BRIS 1-30M
Open Encyclopedic DBPedia, Wikidata 0.3-1.2M
Open Leaks and Investigations Panama Papers (Offshore Leaks), Trump World Data 3-300K

Oct
2016
Company Data Species (2/2)
Category Loca
tions
Industry
Classi-
fication
High
Tech.
Fields
Invest.
Info
Org-Org
Relations
(e.g.Tree)
Org-
Person
Relations
Clean,
Correct,
Predictable
Exhaustive Global Databases ++ +/- - - ++ +/- 6
Rich Company Databases ++ + +/- +/- ++ +/- 8
Investment Databases +/- +/- + + ++/- +/- 4-6
Very Big Open Databases + +/- - - +/- - 8
Global Official Open Databases + - - - +/- - 8
Open Encyclopedic +/- +/- + - +/- + 3-5
Open Leaks and Investigations +/- - - - +/- + 4-6

Matching and Overlap
o Organizations matched across:
CrunchBase (CB), CB Insights
(CBI), Capital IQ (CIQ), DJ
Venture Source, …
o The Venn diagram presents
the overlap between sources
 The size of the circle indicates
number of entities per source
 The level of overlap indicates number
of entities matched between the two
sources

Oct
2016
Data Consolidation Across Data Sources

Entity Matching Across Datasets
o Match IDs of one of the same real entity across different databases
o Data Challenges
 Different schemata
 Name variations
 Different classifications and codes
 Lack of unique identifiers (even ticker symbols are not unique)
o Technology challenges
 Pre-selection is needed; brute-force matching is not good for 1M against 5M companies
 It is not trivial to come up with good pre-selection mechanism

Company Matching Sample Project
o We matched 5+ big datasets within couple of months
o Fully automated procedure, which takes few hours to execute
 90% SPARQL and GraphDB’s FTS connectors
o Location normalization through matching to Geonames
 Also industry classification alignment across the sources
o About 85% F-Score with simple structural matching rules
o To get higher accuracy, you need:
 Massive amount of manual work and fine-tuning of weights … or
 Cognitive analytics (importance, similarity, highly accurate named entity recognition, etc.)

Product Roadmap (short term)
o Ontotext platform
 Multi-tenant version of our Manual Annotation Tool
 Streamlined ETL and entity matching based on SPARK
 Configurable Semantic Search front end
o GraphDB
 Reconciliation
 Faster transactions on big knowledge graphs – 2x speed up of small transactions
 Faster SPARQL federation between local repositories
 Similarity based on Semantic Vectors

GraphDB Semantic Similarity Plugin
o Statistics similarity on knowledge graphs using Semantic vectors
o Creates statistical semantic models from your RDF data and search for
similar terms and documents
o Sample:
o Create index from the news from FactForge
o Find similar news, find relevant terms for a news, etc..

Take home
o Business needs global company data for market intelligence
o Linking Proprietary and global data is rocket science
 Mainstream tech cannot deal with such diversity
 Semantic data integration and cognitive analytics needed
o Ontotext is ready to help
 Consulting: help you build the concept for your next generation system
 Develop: build one for you or support you developing your platform
 Support and operations: from Level 3 support to Managed services

Thank you!
Experience the technology with our demonstrators
NOW: Semantic News Portal http://now.ontotext.com
RANK: News popularity ranking for companies http://rank.ontotext.com
FactForge: Hub for open data and news about People and Organizations
http://factforge.net
#42

Analytics on Big Knowledge Graphs Deliver Entity Awareness and Help Data Linking

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