# Presentation: 215428

## Semantic Integration of Heterogeneous NASA Mission Data Sources

- Rich Keller           Dan Berrios          Shawn Wolfe
- 	   David Hall            Ian Sturken

*Semantic Technologies Applied Research Team*
- Information Sharing & Integration Group
- Intelligent Systems Division
- NASA Ames Research Center

## Goal: Virtual data integration

**Virtual Integration**: enable construction of single virtual data source that presents a semantically unified view across a set of heterogeneous data sources

- Web page

- SQL database

- Web services application

- XML file

- Excel file

- WebDAV servers

- document repository

- ftp server

- RDF repository

## Outline

- Semantic Integration Basics
- SemanticIntegrator (SI) Architecture
- Application to Science Ops Planning
- Conclusions

## What is Information Integration?

**Integration** involves:
- bringing together information from multiple sources
- synthesizing a unified view of the information

- Different shades of integration:
- Syntactic vs. Semantic
- Shallow vs. Deep

## Syntactic vs. Semantic Integration

_**Syntactic integration**_: integrate based on surface commonalities across data labels and values
- Eg: correspond “temperature” field in one DB with “temperature” field in another based on identical field name and datatype (numeric)
_**Semantic integration**_** **: integrate based on commonalities in *meaning* behind data
- Eg: correspond “temperature” fields based on the fact that both measure the same property of the same physical subsystem and their scientific units are compatible

| Syntactic | Uses weak surface similarities |
| --- | --- |
| Semantic | Uses strong, explicit underlying data model |

## Shallow vs. Deep Integration

_**Shallow integration**_: retrieve the union of all potentially-relevant information from all data sources and present everything to the user (Google-like approach)
_**Deep integration**_: synthesize a *single* view from all available data sources and present that integrated view to the user
- Requires defining a common integrated view of data
- Requires identification and disambiguation of similar data across sources
- Is challenging!

## Deep, Semantic Integration Example

| Name | Work
Phone | Email | Room |
| --- | --- | --- | --- |
| Shawn Wolfe | 604-2234 | shawn@email | N269/ 231 |
| Rich Keller | 604-3388 | keller@email | N269/ 240 |
| Ernie Smith | 604-1254 | ernest.e.smith@nasa.gov | N269/ 122 |

**Excel TI Staff List**

**Keller Home Page from TI Web Site**

* **Interleaves data from both sources*
* **Referees conflicts across sources*

## Ontologies: Key to Deep, Semantic Integration

-  Semantic Integration requires a deep understanding of the implicit meaning and context surrounding the data
-  *Semantic data models* based on ontologies capture underlying meaning and context needed to support deep cross-source data integration

## Outline

- Semantic Integration Basics
- SemanticIntegrator (SI) Architecture
- Application to Science Ops Planning
- Conclusions

## Generic Integration Architecture

**Database**

**Native Data Sources**

**Integrated**
**Data Source**

**Web Service**

- Excel file

**User**

## Integration Problems/Approaches

**Database**

**Native Data Sources**

**Integrated**
**Data Source**

**Web Service**

- Excel file

**User**

- Problems:
- Data sources store data in different formats and speak different “languages”
- Data sources don’t capture meaning of data
- Data sources don’t capture user’s world view

- Approach:
- Use the language of ontologies (RDF) as a the common format in which to perform integration
- “Wrap” all data sources so they appear to store their data in RDF
- Develop data source ontologies to capture meaning of data in native sources
- Develop an integrated ontology to describe the user’s world view of the merged data
- Develop translation rules that allow you to map vocabularies across sources

## SemanticIntegrator Architecture

**DS1:**
**Database**

**Translation**
**Rules**

**Native Data Sources**

**Interface**

**Virtual **
**Integrated**
**Data Source**
- (RDFS format)

**DS2:**
**Web Service**

**DS3:**
**Web Page**

**VIDS Ontology**

**DS1 Ontology**

**DS2 Ontology**

**DS3 Ontology**

**DS4 Ontology**

**DS4:**
**Excel file**

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**RDFS Triple Store**

**RDFS Triple Store**

**RDFS Triple Store**

**RDFD Triple Store**

- DSM uses translation rules to transform the query so it can be posed to the appropriate RDF data sources

- Query is rewritten into native format for the data source by a Wrapper

- DSM translates results via rules back to language used by VIDS.

- Results are returned; Wrapper translates to RDFS, and passes back

- Interface displays results

- Client requests data from virtual integrated data source (VIDS)

- External data source is queried

- Query routed via VIDS to data source mediator (DSM)

- Triple Store passes query through to wrapper

## Outline

- Semantic Integration Basics
- SemanticIntegrator (SI) Architecture
- Application to Science Ops Planning
- Conclusions

## Info. Integration for Science Operations Planning

**Robot Mule tracks Astronauts & takes photos when commanded**

**Robot in “follow me” mode**

**Voice annotation is recorded and transmitted to database in habitat & to RST on earth**

**Astros can work fully in parallel, talking to personal agents**

**Info. Integration for Science Operations Planning**

_**Utah Field Tests 2003 and 2004**_** **
** ****50 Participants over 17 days **
** ****3 NASA centers & 2 universities**
** ****Diverse scenarios, rough terrain**
** ****2 geologists; authentic science**

- Mobile Agents: Analog Simulation of Mars Surface Exploration
- by Human-Robot Teams

**Co-I’s: Bill Clancey & Maarten Sierhuis**

## Mobile Agents Data Sources

- Field Data
- Images of field sites, environ. features, mineral samples
- Voice notes
- Site data (lat/lon, topography)
- Stored in ScienceOrganizer semantic repository
- Analytic Data
- Sample analysis data (e.g., composition)
- Stored in Excel spreadsheet
- Mineralogy Data
- Chemical composition, atomic weight
- Available @ minerals.com
- GIS Data
- Satellite images
- Geographic data (e.g., population, features)
- Available from Microsoft’s TerraServer Web service

## Applying SemanticIntegrator to Mobile Agents

- 4 data sources
- 5 ontologies:
- 4 different ontologies to impart meaning to data sources
- 1 ontology represents the integrated source
- Rules capture translations between sources
- Simple interface to display integrated data (SIMA: SemanticIntegrator for Mobile Agents)

## Ontologies

**Field Data**

**Integrated Data**

**GIS Data**

**Analysis Data**

**Mineralogy Data**

## SIMA Interface

- Satellite

## Outline

- Semantic Integration Basics
- SemanticIntegrator (SI) Architecture
- Application to Science Ops Planning
- Conclusions

## Conclusions

- Goal: avoid expensive “hard-coded”, “one-off” integration strategies
- SemanticIntegrator’s explicit integration framework enables _reuse_ of components and knowledge, reducing incremental integration overhead:
- Data source wrappers/ontologies can be reused
- Rules can be reused