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Finding knowledge, data and answers on the Semantic Web

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Title: Finding knowledge, data and answers on the Semantic Web


1
Finding knowledge, data and answers on the
Semantic Web
  • Tim Finin
  • University of Maryland, Baltimore County
  • http//ebiquity.umbc.edu/resource/html/id/223/
  • Joint work with Li Ding, Anupam Joshi, Cynthia
    Parr,Joel Sachs, Andriy Parafiynyk and Lushan Han

? http//creativecommons.org/licenses/by-nc-sa/2.0
/ This work was partially supported by DARPA
contract F30602-97-1-0215, NSF grants CCR007080
and IIS9875433
2
This talk
  • Motivation
  • Semantic Web background
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • Social Semantic Web
  • Conclusions

3
Google has made us smarter
4
But what about our agents?
  • Agents still have a very minimal understanding of
    text and images.

5
But what about our agents?
  • A Google for knowledge on the Semantic Web is
    needed by software agents and programs

6
This talk
  • Motivation
  • Semantic Web background
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • Social Semantic Web
  • Conclusions

7
Brief history of the Semantic Web
  • Tim Berners-Lees original 1989 WWW proposal
    described a web of relationships among
    namedobjects unifying many info. management
    tasks.
  • Guhas MCF (94)
  • XMLMCFgtRDF (96)
  • Semantic Web coined (97)
  • RDFOOgtRDFS (99)
  • RDFSKRgtDAMLOIL (00)
  • W3Cs SW activity (01)
  • W3Cs OWL (03)
  • SPARQL (06)
  • Rules, RDFa, .
  • http//www.w3.org/History/1989/proposal.html

8
Interest is high
  • Interest in industry, government and VCs is high
  • RDF is in Adobes products, Oracle 10g and 11g,
    Microsoft Vista, and Yahoos food portal
  • Several high-visibility startups use RDF
  • Joost (internet TV), Teranode (Bioinformatics),
    Garlik (personal info monitoring)
  • And, if you want more evidence that interest is
    high

9
1795
695CD Only
10
What do we mean by Semantic Web
SemanticWeb
explicitsemantics
KR based
RDFOWL
11
RDF is the first SW language
Graph
XML Encoding
RDF Data Model
ltrdfRDF ..gt lt.gt lt.gt lt/rdfRDFgt
Good For HumanViewing
Good for MachineProcessing
Triples
stmt(docInst, rdf_type, Document) stmt(personInst,
rdf_type, Person) stmt(inroomInst, rdf_type,
InRoom) stmt(personInst, holding,
docInst) stmt(inroomInst, person, personInst)
Good For Reasoning
  • RDF is a simple language for building graph based
    representations
  • Grounded in web standards
  • With terms to support ontologies, description
    logic, rules and much of first order logic

12
IMHO
  • Better NLP will help search engines, its a long
    term, incremental project
  • We need an well-defined and extensible
    representation system for explicit knowledge
  • It should be backed by open, non-proprietary
    standards supported by industry, Government and
    other interested parties
  • The W3C approach is not perfect
  • But The perfect is the enemy of the good.
  • Semantic Web vs. semantic web

13
This talk
  • Motivation
  • Semantic Web background
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • Social Semantic Web
  • Conclusions

14
  • http//swoogle.umbc.edu/
  • Running since summer 2004
  • 2.1M RDF docs, 420M triples, 10K ontologies,15K
    namespaces, 1.5M classes, 185K properties, 49M
    instances, 800 registered users

15
Swoogle Architecture
16
A Hybrid Harvesting Framework
true
Swoogle Sample Dataset
Submissions pings
Inductive learner
would
Seeds R
Seeds M
Seeds H
RDF crawling
Bounded HTML crawling
Meta crawling
google
Google API call
crawl
crawl
the Web
17
Performance Site Coverage
  • SW06MAR - Basic statistics (Mar 31, 2006)
  • 1.3M SWDs from 157K websites
  • 268M triples
  • 61K SWOs including gt10K in high quality
  • 1.4M SWTs using 12K namespaces
  • Significance
  • Compare with existing works ( DAML crawler,
    scutter )
  • Compare SW06MAR with Googles estimated SWDs

SWDs per website
Website
18
Performance crawlers contribution
  • High SWD ratio 42 URLs are confirmed as SWD
  • Consistent growth rate 3000 SWDs per day
  • RDF crawler best harvesting method
  • HTML crawler best accuracy
  • Meta crawler best in detecting websites

of documents
19
This talk
  • Motivation
  • Semantic Web background
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • Social Semantic Web
  • Conclusions

20
Applications and use cases
  • Supporting Semantic Web developers
  • Ontology designers, vocabulary discovery, whos
    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 SW tools
  • Triple shop finding data for SPARQL queries

1
2
3
21
1
22
80 ontologies were found that had these three
terms
By default, ontologies are ordered by their
popularity, but they can also be ordered by
recency or size.
Lets look at this one
23
Basic Metadata hasDateDiscovered  2005-01-17
hasDatePing  2006-03-21 hasPingState
 PingModified type  SemanticWebDocument
isEmbedded  false hasGrammar  RDFXML
hasParseState  ParseSuccess hasDateLastmodified
 2005-04-29 hasDateCache  2006-03-21
hasEncoding  ISO-8859-1 hasLength  18K
hasCntTriple  311.00 hasOntoRatio  0.98
hasCntSwt  94.00 hasCntSwtDef  72.00
hasCntInstance  8.00
24
Who uses this ontology and how do they access it?
25
rdfsrange was used 41 times to assert a value.
owlObjectProperty was instantiated 28 times
timeCal defined once and used 24 times (e.g.,
as range)
26
These are the namespaces this ontology uses.
Clicking on one shows all of the documents using
the namespace.
All of this is available in RDF form for the
agents among us.
27
Heres what the agent sees. Note the swoogle and
wob (web of belief) ontologies.
28
We can also search for terms (classes,
properties) like terms for person.
29
10K terms associated with person! Ordered by
use.
Lets look at foafPersons metadata
30
Metadata stored for a term is information about
its definition both what and by whom
31
10K terms associated with person! Ordered by
use.
32
How do other terms use foafPerson? 100 documents
assert that foafpublication is a property of a
foafPerson
33
87K documents used foafgender with a foafPerson
instance as the subject
34
3K documents used dccreator with a foafPerson
instance as the object
35
Swoogles archive saves every version of a SWD
its seen.
36
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37
2
  • An NSF ITR collaborative project with
  • University of Maryland, Baltimore County
  • University of Maryland, College Park
  • U. Of California, Davis
  • Rocky Mountain Biological Laboratory

38
An invasive species scenario
  • Nile Tilapia fish have been found in a California
    lake.
  • Can this invasive species thrive in this
    environment?
  • If so, what will be the likelyconsequences for
    theecology?
  • Sowe need to understandthe effects of
    introducingthis fish into the food webof a
    typical California lake

39
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

40
East River Valley Trophic Web
http//www.foodwebs.org/
41
Species List Constructor
  • Click a county, get a species list

42
The problem
  • We have data on what species are known to be in
    the location and can further restrict and fill in
    with other ecological models
  • But we dont know which of these the Nile Tilapia
    eats of who might eat it.
  • We can reason from taxonomic data (similar
    species) and known natural history data (size,
    mass, habitat, etc.) to fill in the gaps.

43
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44
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
45
Evidence Provider
46
Status
  • 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 concepts and properties to
    represent food webs, and ELVIS related tasks,
    inputs and outputs
  • 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.
    250K classes on plants and animals
  • Under development
  • Connect to visualization software
  • Connect to triple shop to discover more data

47
Supporting SW Tools
3
  • Semantic Web applications can access Swoogle
    through a REST-based Web interface or via SQL.
  • Two examples
  • A system to help scientists construct datasets
    from RDF documents on the Web
  • Tools to manage Semantic Web data in Blogs and
    other forms of social media

48
UMBC Triple Shop
  • http//sparql.cs.umbc.edu/
  • Online SPARQL RDF query processing with several
    interesting features
  • Automatically finds SWDs for give queries using
    Swoogle backend database
  • Datasets, queries and results can be saved,
    tagged, annotated, shared, searched for, etc.
  • RDF datasets as first class objects
  • Can be stored on our server or downloaded
  • Can be materialized in a database or(soon) as a
    Jena model

49
Whats SPARQL?
  • SPARQL is the standard language ( protocol) for
    querying RDF graphs
  • Think SQL for RDF
  • PREFIX rdf lthttp//www.w3.org/1999/02/22-rdf-synt
    ax-nsgt
  • PREFIX foaf lthttp//xmlns.com/foaf/0.1/gt
  • SELECT ?person ?name ?email
  • FROM lthttp//rdf.example.org/people.rdfgt
  • WHERE ?person a foafPerson .
  • ?person foafname ?name .
  • OPTIONAL ?person foafmbox
    ?email .

50
The Fractal nature of SW systems
  • A SPARQL endpoint can make any Web data source
    look like a RDF graph that can be queried
  • Give a graph as a query, get a graph as a result

51
Web-scale semantic web data access
data access service
the Web
agent
Index RDF data
ask (person)
Search vocabulary
Search URIrefs in SW vocabulary
inform (foafPerson)
Compose query
ask (?x rdftype foafPerson)
Search URLs in SWD index
Populate RDF database
inform (doc URLs)
Fetch docs
Query local RDF database
52
Who knows Anupam Joshi? Show me their names,
email address and pictures
53
The UMBC ebiquity site publishes lots of RDF
data, including FOAF profiles
54
PREFIX foaf lthttp//xmlns.com/foaf/0.1/gt SELECT
DISTINCT ?p2name ?p2mbox ?p2pix FROM ??? WHERE
?p1 foafsurname "Joshi" . ?p1
foaffirstName Anupam" . ?p1
foafmbox ?p1mbox . ?p2
foafknows ?p3 . ?p3 foafmbox
?p1mbox . ?p2 foafname ?p2name
. ?p2 foafmbox ?p2mbox .
OPTIONAL ?p2 foafdepiction ?p2pix .
ORDER BY ?p2name
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58
302 RDF documents were found that might have
useful data.
59
Well select them all and add them to the current
dataset.
60
Well run the query against this dataset to see
if the results are as expected.
61
The results can be produced in any of several
formats
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63
Looks like a useful dataset. Lets save it and
also materialize it the TS triple store.
An extension will let us ask that it be
automatically updated when constituents change
64
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65
We can also annotate, save and share queries.
66
This talk
  • Motivation
  • Semantic Web background
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • Social Semantic Web
  • Conclusions

67
  • Social media sites have become thebiggest source
    of new content on the Web
  • Blogs, Wikis, Photo sites, forums, etc.
  • Accounting for 1/3 of new Web content

68
  • Its a global phenomenon
  • Japanese is now the mostcommon language

69
  • Social media sites have embraced newways of
    letting users add semanticinformation
  • Showing users the potential of semantics

70
Social Media and the Semantic Web
  • Many are exploring how Semantic Web technology
    can work with social media
  • Social media like blogs are typically temporally
    organized
  • valued for their timely and dynamic information!
  • If static pages form the Webs long term memory,
    then the Blogosphere is its stream of
    consciousness
  • Maybe we can (1) help people publish data in RDF
    on their blogs and (2) mine social media sites
    for useful information

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73
A good Semantic Web opportunity
  • We want to make it easy for scientists to enter
    and collect information from social media
  • Professionals, students and amateurs!
  • Two early examples
  • SPOTter a tool to add Semantic Web data to
    blogs
  • Splickr a system to mine Flickr for images of
    organisms

74
SPOTter SPire Observation Tool
  • Weve developed some simple components to help
    people add RDF data to blogs and ping Swoogle to
    get it indexed.
  • SPOTter is an initial prototype that uses the
    ETHAN ontology and is being used in some BioBlitz
    activities with students.
  • Were working toward a version that uses Twitter
    so that people can make the blog entries from the
    cell phones via SMS
  • The SPOTter agent will get the entries (via RSS)
    and index the data

75
SPOTter button
Once entered, the data isembedded into the blog
postand Swoogle is pinged to index it
76
  • We can draw a bounding box onThe map and find
    observations
  • An RSS feed provided for eachquery

Prototype SPOTter Search engine
77
Flickr
  • The Flickr photo sharing site has millions of
    photographs
  • Many of plants and animals
  • Most of them have descriptions, timestamps, tags
    and even geo-tags
  • Flickr has even introduced machine tags that
    can be mapped into RDF
  • Any Flickr users (humans or bots) can add
    comments and annotations
  • Theres a good API
  • It could be a good source of ecological
    information

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Results for people and machines
81
This talk
  • Motivation
  • Semantic Web background
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • Social Semantic Web
  • Conclusions

82
Conclusion
  • The web will contain the worlds knowledge in
    forms accessible to people and computers
  • We need better ways to discover, index, search
    and reason over SW knowledge
  • SW search engines address different tasks than
    html search engines
  • So they require different techniques and APIs
  • Swoogle like systems can help create consensus
    ontologies and foster best practices
  • Social media provide new challenges and
    opportunities for the Semantic Web

83
For more information
http//ebiquity.umbc.edu/
Annotatedin OWL
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