2014 Ontology Summit & Symposium Big Data and Semantic Web Meet Applied Ontology Summary Presented by Presented by Ram D. Sriram Chief, Software and Systems Division Information Technology Laboratory National Institute of Standards and Technology, USA sriram@nist.gov On behalf of Ontology Summit 2014 Organizers and Participants 1
Overview of Ontology Summits • The Ontology Summit is an annual series of events that started in 2006 with the joint sponsorship of Ontolog and NIST • The summit is largely a self-organizing, bottom-up, volunteer driven effort, that solicits contributions from participants around the world in both industry and participants around the world in both industry and academia • Each year's Summit (different theme every year) consists of a series events and continued discourse spanning three months, culminating in a free, two-day face-to-face workshop and symposium • URL: http://ontolog.cim3.net/cgi- bin/wiki.pl?OntologySummit 2
Summit History • 2006: Upper Ontology • 2007: Ontology, Taxonomy, Folksonomy: Understanding the Distinctions • 2008: Toward an Open Ontology Repository • 2009: Toward Ontology-based Standards • 2010: Creating the Ontologists of the Future • 2010: Creating the Ontologists of the Future • 2011: Making the Case for Ontology • 2012: Ontology for Big Systems • 2013: Ontology Evaluation across the Ontology Lifecycle. • 2014: Big Data and Semantic Web Meet Applied Ontology 3
BIG DATA 4
Issues in Big Data Value, Viewpoint, Visualization 5
Spurious Relationships 6 Courtesy: http://www.tylervigen.com
THE SEMANTIC WEB THE SEMANTIC WEB 7
The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. 8 From Berners-lee, Hendler, J., and Lassila, The Semantic Web, Scientific American, May 2001.
The Semantic Web • The Web (2010) is a Web collection of links and 2010 2010 resources ? – Is syntactic & structural only ? – Excludes semantic ? ? interoperability at high levels. ? ? ? – Google has a linked data ? ? structure (keyword) & has no notion of the semantics Humans have to do the understanding Humans have to do the understanding (meaning) of your query (meaning) of your query • Semantic Web extends the Semantic Web Web so information is Evolving In Transit given well-defined meaning Force Structure As Is Deployed Force Locations Theater – Enables semantic interoperability at high levels Home base – Google of tomorrow will be Capabilitiies concept based (we are seeing Terrain that now) Logistics Units – Able to evaluate knowledge in context Marsh 9 Machines partially understand what humans mean Courtesy: Leo Obrst, MITRE
Semantic Web Context Enable Reasoning: Proof, Logic SWRL, RIF, FOL, Inference Semantic Add Full Ontology Language so Machines can OWL Interpret the Semantics Web Trust Expose Data & Service Semantics Expose Data & Service Semantics RDF/RDF Schema RDF/RDF Schema Security, Tru Structure XML Schema Current Syntax, Transmission XML Web “ Digital Dial Tone”, Global Addressing HTTP, Unicode, URIs Anyone, anywhere can add to an evolving, decentralized “global database” Explicit semantics enable looser coupling, flexible composition of services and data Courtesy: Leo Obrst, MITRE 10
Semantic Web Architecture Courtesy: Jim Hendler 11
ONTOLOGIES ONTOLOGIES 12
What Is An Ontology • An ontology is an explicit description of a domain: – concepts – properties and attributes of concepts – constraints on properties and attributes – Individuals (often, but not always) – Individuals (often, but not always) • An ontology defines – a common vocabulary – a shared understanding 13 Courtesy: Natalya F. Noy
Example: A biological ontology is: • A machine interpretable representation of some aspect of biological reality – what kinds of – what kinds of eye disc sense organ things exist? develops is_a – what are the from eye relationships between these part_of things? ommatidium 14 Courtesy: Musen
The Foundational Model of Anatomy The Foundational Model of Anatomy 15
Engineering Ontology Thing Upper Ontology Collection Individual = Temporal Thing Spatial Thing = Other Relationships Event Pumping Mechanical Device done-by Domain Engine Pump Hydraulic System Ontology has-part supplies-fuel-to Jet Engine Hydraulic Pump Fuel Pump connected-to part-of Aircraft Engine Driven Pump Fuel System Fuel Filter 16 Courtesy: Gruninger
Ontology Spectrum: One View strong semantics strong semantics Modal Logic First Order Logic Logical Theory Is Disjoint Subclass of with Description Logic transitivity property OWL UML Conceptual Model Is Subclass of Is Subclass of Semantic Interoperability Semantic Interoperability RDF/S RDF/S XTM Extended ER Thesaurus Has Narrower Meaning Than ER Structural Interoperability DB Schemas, XML Schema Taxonomy Is Sub-Classification of Relational Model, XML Syntactic Interoperability 17 weak semantics weak semantics Courtesy: Obrst
Ontology Spectrum: Application see also http://vimeo.com/11529540 Concept (referent Ontology strong category) based Logical Theory weak Conceptual Model Term - based Expressivity More Expressive Thesaurus Semantic Models Enable More Complex Applications Taxonomy Enterprise Modeling Synonyms, Real World Domain Modeling, Semantic Categorization, (system, service, data), Enhanced Search Search (using concepts, properties, relations, Simple Search & Question-Answering (Improved Recall) rules), Machine Interpretability (M2M, M2H Navigation, (Improved Precision), & Navigation, semantic interoperability), Automated Simple Indexing Querying, SW Services Cross Indexing Reasoning, SW Services 18 Courtesy: Obrst Application
Ontology Application Scenarios • Common Access to Information OA – information required by multiple agents Ontology – expressed in wrong terms/format specifies specifies conforms to – ontology used as agreed standard, Operational T1 basis for converting/mapping Application 1 Application n Tn Data – Benefits : interoperability, more effective AD T2 use/reuse of knowledge builds translators Application 2 Ontology-Based Search – Ontology used for concept-based Ontology structuring of information in a repository Information – Benefits: better information access KW Search Engine 19 Courtesy: Gruninger
More Application Scenarios DA • Neutral Authoring uses – artifact authored in single language, authors Ontology based on ontology Operational translate translate – converted to multiple target formats Data – Benefits : knowledge reuse, AU maintainability, long term knowledge ... Application Application 1 N retention Ontology as Specification OA authors – build ontology for required domain – produce software consistent with Ontology ontology (optional) manual or partially automated used to conforms to build – Benefits: documentation, AD maintenance, Application Application reliability, knowledge (re)use 1 N 20 Courtesy: Gruninger
A Military Example of Ontology for Data Integration Ontology: defines the terms Ontology Aircraft used to describe and represent an area of Identifier knowledge (subject Signature Location Time Observed matter): vocabulary + meaning + machine understandable Time Location Service Identifier Signature … Observed Tid Type Long T … Navy 330296 F-14D 121°8'6" 2.35 Sense Lat stamp S-code Model Coord … Time CNM023 MIG-29 13458 Army CNM023 MIG-29 121.135° 13458 121.135° 330296 F-14D 121°8'6" 2.35 Navy 330298 AH-1G C 121°2‘2" 2.45 Tupolev CNM035 121.25° 13465 Tupolev TU154 Army CNM035 121.25° 13465 330298 AH-1G C 121°2‘2" 2.45 TU154 Sexigesimal Decimal UTM Geographic Commander, Coordinate Coordinates Army S2, S3 Navy 21 Courtesy: Leo Obrst, MITRE
Interoperability Example Application A PSRL Application B feature sweptSolid fillet extrudedSolid revolvedSolid baseExtrudedSolid bossExtrudedSolid PSRL Syntax, PSRL Semantics PSRL grammar, baseExtrudedSolid(extrude1) B ’ s Semantics PSRL grammar, extrusion(extrude1) A ’ s Semantics baseExtrude(extrude1) and Semantic equivalences hasParent(sketch1) Courtesy: Lalit Patil, Deba Dutta &Ram D. Sriram
Bioportal BIO-REGENT (bioportal.bioontology.org) Patent Document Scientific Publication Court Case Knowledge Source: Bio Ontology Bio Ontology (Technical Domain) Knowledge Source: Patent System Issued Patents Ontology and (Business/Legal Applications Domain) File Wrappers Court Cases Technical Publications Regulations and Laws Siloed Patent System Information Courtesy: Kincho Law (partial support from NIST)
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