1. Donna Fritzsche (Hummingbird Design)
Ontology Summit 2017: AI, Learning, Reasoning, and Ontologies Introduction to Ontologies and Reasoning (Track C) Summary Report March 29, 12:30 EST
Track Co-Champions
Donna Fritzsche (Hummingbird Design)
Ram D. Sriram (NIST)

2. Track C: Ontologies & Reasoning
The goal of the 2017 Ontology Summit is to explore, identify and articulate the relationships between between ontologies, AI, machine learning and reasoning. The theme of march 22nd virtual meeting is to discuss techniques developed for reasoning using various ontological foundations.

3. Anatomy of an Intelligent Agent: A View
Consider the basic definition of an Intelligent Agent and its components:
“In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is "rational", as defined in economics.) Intelligent agents may also learn or use knowledge to achieve their goals.” (Wikipedia)
Basic Components
Sensors
Knowledge
Inference (& Machine Learning)
Actuators
Additional attributes
Feedback mechanisms
Emotion and sentiment analysis
Social network

4. Anatomy of an Intelligent Agent: A View
Consider the basic definition of an Intelligent Agent and its components:
“In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is "rational", as defined in economics.) Intelligent agents may also learn or use knowledge to achieve their goals.” (Wikipedia)
Basic Components
Sensors
Knowledge
Inference (& Machine Learning)
Actuators
Additional attributes
Feedback mechanisms
Emotion and sentiment analysis
Social network

5. Ontology Spectrum for Reasoning
strong semantics
Modal Logic
First Order Logic
Logical Theory
Is Disjoint Subclass of with transitivity property
From less to more expressive
CATEGORY THEORY
Description Logic
DAML+OIL, OWL
UML
Conceptual Model
Is Subclass of
RDF/S
Semantic Interoperability
XTM
Extended ER
Thesaurus
Has Narrower Meaning Than
The Ontology Spectrum depicts a range of semantic models. What is normally known as an ontology can range from the simple notion of a the terminological model can range from the simple notion of a Taxonomy (terms[1] or concepts[2] with minimal hierarchic or parent/child structure), to a Thesaurus (terms, synonyms, broader than/narrower than term taxonomies, association relation), to a Conceptual Model (concepts structured in a subclass hierarchy, generalized relations, properties, attributes, instances), to a Logical Theory (elements of a Conceptual Model focusing however on real world semantics and extended with axioms and rules, also represented in a logical KR language enabling machine semantic interpretation). A Conceptual Model can be considered a weak ontology; a Logical Theory can be considered a strong ontology. The Ontology Spectrum therefore displays the range of models in terms of expressivity or richness of the semantics that the model can represent , from “weak” or less expressive semantics at the lower left (value set, for example), to “strong” or more expressive semantics at the upper right. The blue lines, labeled by syntactic interoperability, structural interoperability, and semantic interoperability, indicate roughly the expressiveness of the model required to respectively address those levels of interoperability. XML is sufficient for syntactic interoperability, XML Schema enables structural interoperability, but a minimum of RDF is necessary for semantic interoperability.
[1] Terms (terminology): Natural language words or phrases that act as indices to the underlying meaning, i.e., the concept (or composition of concepts) The syntax (e.g., string) that stands in for or is used to indicate the semantics (meaning).
[2] Concept: A unit of semantics (meaning), the node (entity) or link (relation) in the mental or knowledge representation model. In an ontology, a concept is the primary knowledge construct, typically a class, relation, property, or attribute, generally associated with or characterized by logical rules. In an ontology, these classes, relations, properties are called concepts because it is intended that they correspond to the mental concepts that human beings have when they understand a particular body of knowledge (subject matter area or domain). In general, a concept can be considered a placeholder for a real world referent, and thus ontology as an engineering prduct is about representing the semantics of the real world in a model that is usable and interpretable by machine. ER
DB Schemas, XML Schema
Structural Interoperability
Taxonomy
Is Sub-Classification of
Relational
Model, XML
Syntactic Interoperability
weak semantics
Courtesy: Leo Obrst, MITRE 5 5 1

6. Agenda for March 22, 2017 Time: 12:30 pm – 1pm
Speaker: Eugen Kuksa, University of Magdeburg
Presentation Title: Reasoning with Ontologies in Ontohub
Time: 1pm – 1:30pm
Speaker: Pascal Hitzler, Wright State University
Presentation Title: On the Roles of Axiomatizations for Ontologies
Time: 1:30pm – 2pm:
Speaker: Jans Aasman, Franz Inc.
Presentation Title: Cognitive Probability Graphs need an Ontology, Jans Aasman, Franz Inc.
Time: 2:00 – 2:30pm Discussion (optional)

7. Reasoning with ontologies in Ontohub
Reasoning in Ontohub | 1
FACULT Y OF
COMPUT ER SCIENCE
Reasoning with ontologies in Ontohub
Eugen Kuksa
Research group for theoretical computer science Institute for Intelligent Cooperating Systems (IKS) Otto-von-Guericke-Universität, Magdeburg, Germany

8. Ontohub • . . . Web application: https://ontohub.org
Repository for ontologies
Version control (git)
Integrated editor for small files
Analyses ontologies
Supports different logics
OWL
CommonLogic
TPTP (FOF, THF, etc.)
• . . .
Brings tool support
FaCT, Pellet
CVC4, Darwin, E-Prover, Geo-III, SPASS, Vampire
Leo-II, Satallax, Isabelle

9. Ontohub: Key Points How Ontohub works with an example
The Premise Selection Algorithm -- SInE (SUMO Inference Engine)
Other premise selection algorithms are being added to Ontohub

10. Roles of Logical Axiomatizations for Ontologies
Pascal Hitzler
Data Semantics Laboratory (DaSe Lab) Data Science and Security Cluster (DSSC)
Wright State University
March 2017 – Ontology Summit 2017 – Pascal Hitzler

11. Summary Monotonic and Non-monotonic reasoning
Explanations of inference in OWL (Abox & Tbox)
Instance-based and schema-based inferences

12. Ontolog Forum probability graphs and ontologies
March 2017
Jans Aasman
allegrograph.com

13. AKA: Cognitive Computing
Structured
Data
Knowledge
Domain knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontologies
Knowledge
Domain knowledge
Linked Open Data
Vocabularies
Taxonomies/Ontologies
Unstructured Data
Unstructured Data and IOT
Probabilistic Inferences.

14. The stack for Cognitive Computing in healthcare
Level 9
Personalized Medicine, Prescriptive Analytics, True Cost of Care
KNIME as an analytics framework
A declarative, semantic description of data frames to automate retrieval of features from the graph
A direct R interface to semantic graph
Idem for SPARK ML and H20
The results of analytics are fed back into the graph.
Level 8
Data Science Platform – Knime, Spark ML, R, H2O
Level 7
Big Semantic Data Graph
Level 6
Linked Open Data and Knowledge
Level 5
Radically simplify ETL
Level 4
Radically simplified terminologies: Unified Terminology System
Level 3
Radically simplified schemas: Unified Clinical Event Ontology
Level 2
Enterprise Data Warehouse
Level 1
Fragmented solutions, silos

15. Question
What is the role of ontologies in predictive analytics that needs inexact inferences?

16. Snapshot: Reasoning & Ontologies Presentations
Summary Statement
Channels/KR
Inference Strategy
Reasoning with Ontologies in Ontohub
Roles of Axiomatizations for Ontologies
Cognitive Probability Graphs
Part 2 presentation

17. Coming on April 19th
Lise Getoor, University of California, Santa Cruz, Knowledge Graphs and Reasoning
Yolanda Gil, USC-ISI, Reasoning about Scientific Knowledge with Work Flow Constraints
Spencer Rugaber, Georgia Tech, Applications of  Ontologies to Biologically Inspired Design