Semantic Interoperability for Data in Context IG RDA Plenary 3: Friday 28th March 2014 (Day32 Gary Berg-Cross (SOCoP, RDA DFT WG co-chair),

DFT Basic Digital & Data Concepts - data is inherently collective data Digital Data refers to a structured sequence of bits/bytes that represents information content. In many contexts digital data and data are used interchangeably implying both the bits and the content. Real-Time Data is data/data collection which is produced in its own schedule & has a tight time relation to the processes that create it and that require immediate actions. Timeliness such as real time is an attribute of data. Dynamic Data is a type of data which is changing frequently and asynchronously. Note: Dynamic data has also been used in the context of Workflow- workflow that is executed a "dynamic data object", or you can call the results from executing the workflow a "dynamic data object" Referable Data is a type of data (digital or not) that is persistently stored and which is referred to by a persistent identifier. Digital data may be accesses by the identifier. Some data objects references may access a service on the object (OAI-ORE). Citable Data is a type of referable data that has undergone quality assessment and can be referred to as citations in publications.

Background to this Semantic Interoperability Long time work on “data integration and sharing”. Semantics is FEATURED in the Application layer of OSI Intensive work in the AI & knowledge engineering areas. But to many the goal of semantic interoperability remains elusive. More recently the Semantic Web thrust pursued the goal of robust semantic interoperability & robust exchange of data. Needs deep knowledge and support of reasoning to fulfill SW vision. >

SI has a Socio-Tech Aspect Who is doing what? How to Understand the Problem. What are the critical Issues ? Is it a knowledge representation problem? What is the role of Ontology ? What is the role of tools? What are the best methods ? Re-use and integration of data from heterogeneous sources within and across discipline boundaries has not been routinely achieved. Application of special technologies that infer, relate, interpret, and classify the implicit meanings of digital content are not easily adapted to the topical research interest or enfolded in traditional architectures. Use an agile approach, based on sets of competency questions? Don’t try too hard to train a Domain Expert in Gold Standard formal semantics? Since meaning is a cognitive agent phenomena, semantic interoperability is the technical analogue to human communication and cooperation. That makes it intrinsically HARD. Use metadata semantic annotation?

Graphic Overview of Semantic /Ontology Manifesto (EarthCube) Knowledge Infrastructure Vision Community Understanding of Semantic role and value Guiding principles 1.Uses Cases 2.Lightweight -opportunistic methods 3.Semantic interoperability with semantic heterogeneity 4.Bottom-up & top-down approaches 5.Domain - ontology engineer teams 6.Formalized bodies of knowledge across science domains 7.Broader “Reasoning” services “Insertion” Architecture & Workflow Between Based on the work of (alphabetically) Gary Berg-Cross, Isabel Cruz, Mike Dean, Tim Finin, Mark Gahegan, Pascal Hitzler, Hook Hua, Krzysztof Janowicz, Naicong Li, Philip Murphy, Bryce Nordgren, Leo Obrst, Mark Schildhauer, Amit Sheth, Krishna Sinha, Anne Thessen, Nancy Wiegand, and Ilya Zaslavsky Paper at edu/gibda2012/gibda 2012_submission_6.p df /

SI 6 Lightweight Methods & Products Choose lightweight approaches to support application needs and reduced entry barrier Low hanging fruit leverages initial vocabularies & existing conceptual models to ensure that a semantics-driven infrastructure is available for early use. Simple parts/patterns & direct relations to data Triple like parts More relation types here Bottom Up. A useful set of idea that supports a useful subset of (approximate) reasoning

7 GeoSpatial Data & Web Feature Service Standardizes Terms but Lacks Semantics A terminology created independently based on different conceptual models differing in terms/vocabulary but also & meanings.

Better Conceptualization of Properties - for Interoperability (CUAHSI) Organize Properties like size as a physical quality since it inheres in a physical object. Qualities like physical, bulk, & measured properties like stream flow, level, pollutants, evapotranspiration etc. and make them useable concepts rather than level concepts. Currently CUAHSI has them at many levels E.g Major, bulk properties 4 Water Body Water Density Unit Grams /cm 3 Water Density For connecting to Chem/BioChem ontologies there might be sub-categories of Physical for elements – optical, hardness, color See Dumontier Lab ontologies to represent bio-scientific concepts and relations. hasConstituent hasFeature hasUnit usesStandard Chesapeake Bay IsA Area HasFeature Area Quantity hasQuantity Real Number Sq Miles hasUnit hasValue 8 hasLayer …..

Incrementally Adding Better Semantic Relations/Properties Data models & SKOS offer some relations, but they are limited. SKOS is more useful for terms than concepts Consider Irreflexive, anti-symmetric & Transitive constructs that captures common understanding. Observation –Streams and lakes flow into rivers. Property “flows-into” is irreflexive any one river cannot flow into itself as a loop “flows-into” is also anti-symmetric if one river flows into the second, the second one can’t flow into the first. Transitive property for Regions to say that the subRegionOf property between regions is transitive If Logan, Cache County and Utah are regions, and Logan is a subRegion of Cache County, Cache County is a subRegion of Utah, then Logan is also a subRegion of Utah. 9

Grafton Street Dublin in Context Grafton Street (Irish: Sráid Grafton) is one of the two principal shopping streets in Dublin city centre.Irishshopping streetsDublin Do we refer to it a pedestrian mall or a shopping street? Is it a road object but with motor traffic restrictions? Or a public place? Or a non-identifiable part of the city surface? OpenStreetMapOpenStreetMap - All such references are usually outside a computer

What Grafton Street is Depends on its Setting – when we are talking about, AND what Features Grafton Street 1814 AD or 2014? Transport or commerce features?

12. Philosophy Psychology Perspectives ….. Semantics in Context: Connecting 3 Views for Geography/GIScience Knowledge GeoReality Task- Regiment Language Wetland….geo-entity..what boundary? Flows Into isa Type of connected-to Boundary segments = straight lines, so overall boundary is a polyline… Knowledge/GeoConcepts This is different than regular land and water Name Reality Understand Reality: Data evidence Model to express what you understand Models representing Geo-Knowledge Maybe there is more than 1 type of boundary

Example of (Powerful) Challenges – Semantic Mismatches, Inclusions & Alignments Language level for expressing semantics Syntax and logical representation differences of the past should be handled by standardization & rule translations. Different expressivity (Owl vs. Common Logic) might be harder. Ontology level (Grafton example) Different conceptualizations such as different class scope, Hierarchy level differences, coverage or granularity. Scientists use different concepts & categories; What does it mean to say that Concept P includes concept S? What does it mean to say that concept P and S are semantically close? Scientific understanding, often requires existing concepts to be revised or supplanted in the field Perspective – 4D vs. 3D, roads as straight lines or curves, time as interval or ratio….. Tacit assumptions (when messaging, an agent has in mind a number of “unspoken,” implicit consequences of that message.) – “You can’t drive on Grafton”…. Pragmatics of Intentions & goals (also Grafton example) We have different goals so application & use are targeted. We need to adjust conceptualization to accommodate these. 10

One View of Semantic Representation & Heterogeneity A challenge of deep semantic interoperability is that: A global and one size fits all (Gold Standard) representation for each distinct situation, such as Grafton St. represented by data is not realistic, and its procrustean nature may not be desirable if it ignores real heterogeneity The judgment of some (CF John Sowa) is that different representations might be optimal for different use cases Different levels of detail or granularity, along with different kinds of data entry options seem in practice suitable for different domains and settings. Since scientific research is diverse, and evolving, what approach to granular standards can be developed for use? Perhaps it is to use formal semantics to narrow the range of ambiguity for particular purposes.