From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2

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Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started. Parts 1 & 2

Data & Analytics

Panos Alexopoulos
Data and Knowledge Technologies
Professional
http://www.panosalexopoulos.com
p.alexopoulos@gmail.com
@PAlexop
From Taxonomies and Schemas to Knowledge Graphs
Recognizing and tackling the pitfalls of
large-scale semantic modeling

In 2016 I got a mission
“Good morning Mr.
Alexopoulos. Your mission,
should you decide to accept it
is to build a Knowledge Graph
for the Labour Market Domain.”
—Textkernel

which I accepted as I had the perfect recipe

that often didn’t quite work as expected

Knowledge
Graphs &
Semantics
Understanding the interplay

Knowledge Graphs are
interconnected entities
under shared semantics
Not something really new but rather a
rebranding of terms like semantic
networks, knowledge bases, ontologies,
linked data
If you have been designing database
schemas, taxonomies, ontologies or other
types of structured data, you already know
a lot about knowledge graphs.
What you may have missed is the shared
semantics aspect!

RDF, Property Graphs, JSON-LD,
NoSQL, ...
Small, medium, large, densely
connected, sparsely connected,
...
Concrete entities, abstract
entities, “real-world” entities,
hierarchical relations, entity
attributes, relation attributes,
associative relations,
constraints, axioms, rules, ...
Single domain, multiple
domains, single context,
multiple context
Data Analytics, Data Integration,
Semantic search, Question
answering, Reasoning
Important (but non-defining)
dimensions of Knowledge Graphs
TECHNOLOGY STACK SIZE AND CONNECTIVITY
EXACT NATURE OF
ELEMENTS
SCOPE PURPOSE

Crucial dimensions of Knowledge
Graphs
MEANING
ACCURACY
How correct/accurate
is the meaning of each
entity, relation or other
element for all people
and systems that use
the graph.
MEANING
EXPLICITNESS
How clear and
unambiguous is the
meaning of each entity,
relation or other
element for all people
and systems that use
the graph.
MEANING
AGREEMENT
How commonly
acceptable is the
meaning of each entity,
relation or other
element among the
people and systems
that use the graph.

“Enemies” of accuracy, explicitness
and agreement
AMBIGUITY VAGUENESS
BAD MODELING
DIVERSITY/VARIETY SEMANTIC CHANGE

Semantics
CSI
Investigating bad semantic modeling
practices

The “Crime” Scenes
SNOMED: A systematically organized
computer processable collection of
medical terms providing codes, terms,
synonyms and definitions used in
clinical documentation and reporting
KBPedia: A comprehensive knowledge
structure combining seven 'core' public
knowledge bases — Wikipedia, Wikidata,
schema.org, DBpedia, GeoNames,
OpenCyc, and UMBEL — into an
integrated whole

The “Crime” Scenes
Schema.org: A collaborative,
community activity with a mission to
create, maintain, and promote
schemas for structured data on the
Internet, on web pages, in email
messages, and beyond.
DBPedia: A large knowledge graph
automatically extracted from
Wikipedia infoboxes

The “Crime” Scenes
ESCO: A knowledge graph about
occupations, skills and
qualifications for the European
Labour Market, built by the European
Commision

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What are we Talking About, When we Talk About Ontology?

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Ontologies and Semantic Web technologies play an important role in the life sciences to help make data more interoperable and reusable. There are now many publicly available ontologies that enable biologists to describe everything from gene function through to animal physiology and disease. Various efforts such as the Open Biomedical Ontologies (OBO) foundry provide central registries for biomedical ontologies and ensure they remain interoperable through a set of common shared development principles. At EMBL-EBI we contribute to the development of biomedical ontologies and make extensive use of them in the annotation of public datasets. Biological data typically comes with rich and often complex metadata, so the ontologies provide a standard way to capture “what the data is about” and gives us hooks to connect to more data about similar things. These ontology annotations have been put to good use in a number of large-scale data integration efforts and there’s an increasing recognition of the need for ontologies in making data FAIR (Findable, Accessible, Interoperable and Reusable). EMBL-EBI build a number of integrative data platforms where ontologies are at the core of our domain models. One example is the Open Targets platform, where data about disease from 18 different databases can be aggregated and grouped based on therapeutic areas in the ontology and used to identify potential drug targets. The ontologies team at EMBL-EBI provide a suite of services that are aimed at making ontologies more accessible for both humans and machines. We work with scientific data curators and software developers to integrate ontologies and semantics into both the data generation and data presentation workflows. We provide: – An ontology lookup service (OLS) that provides search and visualisation services to over 200+ ontologies – Services for automating the annotation of metadata and learning from previous annotations (Zooma) – An ontology mapping and alignment service (OXO) – Tools for working with metadata and ontologies in spreadsheets (Webulous) – Software for enriching documents in search engines to support “semantic” query expansion I’ll present how we are using these services at EMBL-EBI to scale up the semantic annotation of metadata. I’ll talk about our open source technology stack and describe how we utilise a polyglot persistence approach (graph databases, triples stores, document stores etc) to optimize how we deliver ontologies and semantics to our users.

Ontology Services for the Biomedical Sciences

Connected Data World

Recommendation systems deliver more ROI than any other investment in Data Analytics. This talk will introduce the most basic but effective Recommendation System called Collaborative Filtering and show how to implement it using the Cypher Graph Query Language. The mathematics behind collaborative filtering will be explained as will the usefulness of Graph in implementing such an engine. We will use AgensGraph, which adds graph capability to PostgreSQL, for the talk. This talk is aimed at those who are new to either Cypher or basic recommendation system theory.

Develop A Basic Recommendation System using Cypher

Connected Data World

Linked Data is a set of best practices to publish data in RDF format. Despite the advantages of Linked Data (i.e., discoverability, interoperability, reusability, etc.) many datasets are not published in RDF. Transformation of existing structured datasets into RDF is possible thanks to RDF Mappings. To be able to define such mappings, it is necessary to be familiar with the Linked Data practices and to know perfectly the datasets concerned. An obstacle to the Linked Data democratization is that few people satisfy these two conditions. Tools making the process of Linked Data integration easier can foster Linked Open Data growth. Opendatasoft proposes a chatbot-like tool that can semi-automatically generate RDF mappings for existing structured datasets. The challenge is to automate part of the integration process that requires getting familiar with Linked Data practices.

A Semi-Automatic Tool for Linked Data Integration

Connected Data World

Data integration, data interoperation and data quality are major challenges that continue to haunt enterprises. Every enterprise either by choice or by chance has created massive silos of data in different formats, with duplications and quality issues. Knowledge graphs have proven to be a viable solution to address the integration and interoperation problem. Semantic technologies in particular provide an intelligent way of creating an abstract layer for the enterprise data model and mapping of siloed data to that model, allowing a smooth integration and a common view of the data. Technologies like OWL (Web Ontology Language) and RDF (Resource Description Framework) are the back bone of semantics for knowledge graph implementation. Enterprises use OWL to build an ontology model to create a common definition for concepts and how they are connected to each other in their specific domain. They then use RDF to create a triple format representation of their data by mapping it to the Ontology. This approach makes their data smart and machine understandable. But how can enterprises control and validate the quality of this mapped data? Furthermore, how can they use this one abstract representation of data to meet all their different business requirements? Different departments, different LoBs and different business branches all have their own data needs, creating a new challenge to be tackled by the enterprise. In this talk we will look at how the power of SHACL (SHAPES and Constraints Language), a W3C standard for defining constraint sets over data; complements the two core semantic technologies OWL and RDF. What are the similarities, the overlaps and the differences. We will talk about how SHACL gives enterprises the power to reuse, customize and validate their data for various scenarios, uses cases and business requirements; making the application of semantics even more practical.

One Ontology, One Data Set, Multiple Shapes with SHACL

Connected Data World

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

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