Using the Semantic Web to Support Ecoinformatics

Using the Semantic Web to Support Ecoinformatics Andriy Parafiynyk University of Maryland, Baltimore County http://ebiquity.umbc.edu/paper/html/id/319/Using-the-Semantic-Web-to-Support-Ecoinformatics Joint work with Tim Finin , Joel Sachs, Cynthia Sims Parr, Rong Pan, Lushan Han, Li Ding (UMBC), Allan Hollander (UCD), David Wang (UMCP)  This research was supported by NSF ITR 0326460 and matching funds received from USGS National Biological Information Infrastructure

Invasive Species

Invasive species cost the U.S. economy over $138 billion per year [1].

By various estimates, these species contribute to the decline of 35 to 46 percent of U.S. endangered and threatened species

The invasive species problem is growing, as the number of pathways of invasion increases.

[1] Pimental et al. 2000 Environmental and economic costs associated with non-indigenous species in the United States. Bioscience 50:53-65.

[2] Charles Groat, Director U.S. Geological Survey, http://www.usgs.gov/invasive_species/plw/usgsdirector01.html

Currently most common ways of dealing with data among biologists:

Journal articles

Excel spreadsheets

Local databases

Some information is on-line in HTML/XML

Semantic Web can offer:

Ontologies to arrive to a common vocabulary and define exactly what is what across disciplines (multiple ontologies with mappings possible)

Constant on-line data availability with convenient ways of data acquisition and processing

Data discovery (Swoogle)

Data integration from different sources, queries on data from multiple sources

Expanding the knowledge base by inferencing

Data can be easily updated or added, users notified

OLD NEW Green: data gathering; Pink: data integration and manipulation White: data analysis; Blue: results dissemination Collect data OR Find data tables in literature or data registry OR Email author of data Massage data manually Write up metadata record Register dataset with data registry Start over for next project Run analyses Publish paper Post supplemental data file on web Create local spreadsheet Build automatically updating dynamic dataset Develop intelligent query for semantic web data Download to local spreadsheet Run analyses Publish paper Reanalyze using latest dataset (Query and data already publicly available)

An NSF ITR collaborative project with

University of Maryland, Baltimore County

University of Maryland, College Park

U. Of California, Davis

Rocky Mountain Biological Laboratory

Food Webs

A food web models the trophic (feeding) relationships between organisms in an ecology

Food web simulators are used to explore the consequences of changes in the ecology, such as the introduction or removal of a species

A locations food web is usually constructed from studies of the frequencies of the species found there and the known trophic relations among them.

Goal: automatically construct a food web for a new location using existing data and knowledge

ELVIS: Ecosystem Location Visualization and Information System

East River Valley Trophic Web http://www.foodwebs.org/

Species List Constructor

Click a county, get a species list

The problem

We know which species exist in the location and can further restrict and fill in with other ecological models

But we don’t know which of them might be eaten by a potential invasive, or which might eat the invasive

We can reason from taxonomic data (similar species) and known natural history data (size, mass, habitat, etc.) to fill in the gaps.

Food Web Constructor

Predict food web links using database and taxonomic reasoning.

In an new estuary, Nile Tilapia could compete with ostracods (green) to eat algae. Predators (red) and prey (blue) of ostracods may be affected

Evidence Provider

Examine evidence for predicted links.

ELVIS

Final goal:

ELVIS

(Ecosystem Location Visualization and Information System) as an integrated set of web services for constructing food webs for a given location.

Background Ontologies

SpireEcoConcepts:

confirmed and potential food web links

bibliographic information of food web studies

ecosystem terms

taxonomic ranks

California Wildlife Habitat Relationships Ontology

life history

geographic range

management information

ETHAN (Evolutionary Trees and Natural History) Concepts and properties for ‘natural history’ information on species derived from data in the Animal diversity web and other taxonomic sources

Data representation: ETHAN Ontology

ethan_animals.owl: phylogenetic information about organisms

ethan_keywords.owl: geographic range, habitats, physical description, trophic information, reproduction, lifespan, behavioral information, conservation Status

Information in triples:

“ Esox lucius” is a subclass of “Esox”

“ Esox lucius” has max mass “1.4 kg”

“ Esox” eats “Actinopterygii”

Using ETHAN and OWL inferencing to predict success of invasive species

Known food web links: rabbit eats carrot

What about hare?

Yes with high probability since both are subclasses of the same class in taxonomic hierarchy, have same habitat etc

yummy!!! yummy???

http://swoogle.umbc.edu/

Running since summer 2004

1.8M RDF docs, 320M triples, 10K ontologies, 15K namespaces, 1.3M classes, 175K properties, 43M instances, 600 registered users

Applications and use cases

Supporting Semantic Web developers

Ontology designers, vocabulary discovery, who’s using my ontologies or data?, use analysis, errors, statistics, etc.

Searching specialized collections

Spire: aggregating observations and data from biologists

InferenceWeb: searching over and enhancing proofs

SemNews: Text Meaning of news stories

Supporting Semantic Web tools

Triple shop: finding data for SPARQL queries

1 2 3

Search for ontologies which contain this terms 1

746 ontologies were found that had these two terms By default, ontologies are ordered by their ‘popularity’, but they can also be ordered by date or size.

We can also search for any RDF documents containing these terms

5,378 documents were found that had these two terms

UMBC Triple Shop