Creating and Exploiting a Web of Semantic Data
Overview • Introduction • Semantic Web 101 • Recent Semantic Web trends • Examples: DBpedia, Wikitology • Conclusion
The Age of Big Data • Massive amounts of data is available today • Advances in many fields driven by availability of unstructured data, e. g. , text, audio, images • Increasingly, large amounts of structured and semi-structured data is also online • Much of this available in the Semantic Web language RDF, fostering integration and interoperability • Such structured data is especially important for the sciences
Twenty years ago… Tim Berners-Lee’s 1989 WWW proposal described a web of relationships among named objects unifying many information management tasks Capsule history • Guha’s MCF (~94) • XML+MCF=>RDF (~96) • RDF+OO=>RDFS (~99) • RDFS+KR=>DAML+OIL (00) • W 3 C’s SW activity (01) • W 3 C’s OWL (03) • SPARQL, RDFa (08) • Rules (09) http: //www. w 3. org/History/1989/proposal. html
Ten years ago …. • The W 3 C started developing standards for the Semantic Web • The vision, technology and use cases are still evolving • Moving from a web of documents to a web of data
Today 4. 5 billion integrated facts published on the Web as RDF Linked Open Data
Tomorrow Large collections of integrated facts published on the Web for many disciplines and domains
W 3 C’s Semantic Web Goal “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. ” -- Berners-Lee, Hendler and Lassila, The Semantic Web, Scientific American, 2001
Contrast with a non-Web approach The W 3 C Semantic Web approach is • Distributed • Open • Non-proprietary • Standards based
How can we share data on the Web? • POX, Plain Old XML, is one approach, but it has deficiencies • The Semantic Web languages RDF and OWL offer a simpler and more abstract data model (a graph) that is better for integration • Its well defined semantics supports knowledge modeling and inference • Supported by a stable, funded standards organization, the World Wide Web Consortium
Simple RDF Example dc: Title “Intelligent Information Systems on the Web and in the Aether” http: //umbc. edu/ ~finin/talks/idm 02/ dc: Creator Note: “blank node” bib: Aff http: //umbc. edu/ bib: name “Tim Finin” bib: email “finin@umbc. edu”
The RDF Data Model • An RDF document is an unordered collection of statements, each with a subject, predicate and object • Such triples can be thought of as a labelled arc in a graph • Statements describe properties of resources • A resource is any object that can be referenced or denoted by a URI • Properties themselves are also resources (URIs) • Dereferencing a URI produces useful additional information, e. g. , a definition or additional facts
RDF is the first SW language Graph XML Encoding <rdf: RDF ……. . > <…. > </rdf: RDF> Good for Machine processing RDF Data Model Triples stmt(doc. Inst, rdf_type, Document) stmt(person. Inst, rdf_type, Person) stmt(inroom. Inst, rdf_type, In. Room) stmt(person. Inst, holding, doc. Inst) stmt(inroom. Inst, person. Inst) Good for storage and reasoning Good for human viewing RDF is a simple language for graph based representations
XML encoding for RDF <rdf: RDF xmlns: rdf="http: //www. w 3. org/1999/02/22 -rdf-syntax-ns#" xmlns: dc="http: //purl. org/dc/elements/1. 1/" xmlns: bib="http: //daml. umbc. edu/ontologies/bib/"> <description about="http: //umbc. edu/~finin/talks/idm 02/"> <dc: title>Intelligent Information … and in the Aether</dc: Title> <dc: creator> <description> <bib: Name>Tim Finin</bib: Name> <bib: Email>finin@umbc. edu</bib: Email> <bib: Aff resource="http: //umbc. edu/" /> </description> </dc: Creator> </description> </rdf: RDF> dc: Title http: //umbc. edu/ ~finin/talks/idm 02/ “Intelligent Information Systems on the Web and in the Aether” dc: Creator bib: Aff http: //umbc. edu/ bib: name “Tim Finin” bib: email “finin@umbc. edu”
N 3 is a friendlier encoding @prefix rdf: http: //www. w 3. org/1999/02/22 -rdf-syntax-ns#. @prefix dc: http: //purl. org/dc/elements/1. 1/. @prefix bib: http: //daml. umbc. edu/ontologies/bib/. <http: //umbc. edu/~finin/talks/idm 02/> dc: title "Intelligent. . . and in the Aether" ; dc: creator [ bib: Name "Tim Finin"; bib: Email "finin@umbc. edu" bib: Aff: "http: //umbc. edu/" ]. dc: Title http: //umbc. edu/ ~finin/talks/idm 02/ http: //umbc. edu/ “Intelligent Information Systems on the Web and in the Aether” dc: Creator bib: Aff bib: name “Tim Finin” bib: email “finin@umbc. edu”
RDFS supports simple inferences • RDF Schema adds vocabulary for classes, properties & constraints • An RDF ontology plus some RDF statements may imply additional RDF statements (not possible in XML) • Note that this is part of the data model and not of the accessing or processing code. @prefix rdfs: <http: //www. . . >. @prefix : <genesis. n 3>. parent a rdf: property; rdfs: domain person; rdfs: range person. mother rdfs: sub. Property parent; rdfs: domain woman; rdfs: range person. eve mother cain. person a class. woman sub. Class person. mother a property. eve a person; a woman; parent cain a person.
OWL adds further richness OWL adds richer representational vocabulary, e. g. – parent. Of is the inverse of child. Of – Every person has exactly one mother – Every person is a man or a woman but not both – A man is the equivalent of a person with a sex property with value “male” OWL is based on ‘description logic’ – a logic subset with efficient reasoners that are complete – Good algorithms for reasoning about descriptions
That was then, this is now • 1996 -2000: focus on RDF and data • 2000 -2007: focus on OWL, developing ontologies, sophisticated reasoning • 2008 -…: Integrating and exploiting large RDF data collections backed by lightweight ontologies
A Linked Data story • Wikipedia as a source of knowledge –Wikis are a great ways to collaborate on building up knowledge resources • Wikipedia as an ontology –Every Wikipedia page is a concept or object • Wikipedia as RDF data –Map this ontology into RDF • DBpedia as the lynchpin for Linked Data –Exploit its breadth of coverage to integrate things
Populating Freebase KB
Underlying Powerset’s KB
Mined by True. Knowledge
Wikipedia as an ontology • Using Wikipedia as an ontology –each article (~3 M) is an ontology concept or instance –terms linked via category system (~200 k), infobox template use, inter-article links, infobox links –Article history contains metadata for trust, provenance, etc. • It’s a consensus ontology with broad coverage • Created and maintained by a diverse community for free! • Multilingual • Very current • Overall content quality is high
Wikipedia as an ontology • Uncategorized and miscategorized articles • Many ‘administrative’ categories: articles needing revision; useless ones: 1949 births • Multiple infobox templates for the same class • Multiple infobox attribute names for same property • No datatypes or domains for infobox attribute values • etc.
Dbpedia : Wikipedia in RDF • A community effort to extract structured information from Wikipedia and publish as RDF on the Web • Effort started in 2006 with EU funding • Data and software open sourced • DBpedia doesn’t extract information from Wikipedia’s text, but from the its structured information, e. g. , links, categories, infoboxes
DBpedia: Linked Data lynchpin
http: //lookup. dbpedia. org/
Dbpedia uses WP structured data DBpedia extracts structured data from Wikipedia, especially from Infoboxes
Dbpedia ontology • Dbpedia 3. 2 (Nov 2008) added a manually constructed ontology with Place – 170 classes in a subsumption hierarchy – 880 K instances – 940 properties with domain and range 248, 000 Person 214, 000 Work 193, 000 Species 90, 000 Org. 76, 000 Building 23, 000 • A partial, manual mapping was constructed from infobox attributes to these term • Current domain and range constraints are “loose” • Namespace: http: //dbpedia. org/ontology/
Person 56 properties
Organisation 50 properties
Place 110 properties
PREFIX dbp: <http: //dbpedia. org/resource/> PREFIX dbpo: <http: //dbpedia. org/ontology/> SELECT distinct ? Property ? Place WHERE {dbp: Barack_Obama ? Property ? Place rdf: type dbpo: Place. } http: //dbpedia. org/sparql/
DBpedia: Linked Data lynchpin
Consider Baltimore, MD
Looking at the RDF description We find assertions equating DBpedia's object for Baltimore with those in other LOD datasets: dbpedia: Baltimore%2 C_Maryland owl: same. As census: us/md/counties/baltimore; owl: same. As cyc: concept/Mx 4 rv. Vin-5 wp. Eb. Gdrc. N 5 Y 29 yc. A; owl: same. As freebase: guid. 9202 a 8 c 04000641 f 8000004921 a; owl: same. As geonames: 4347778/. Since owl: same. As is defined as an equivalence relation, the mapping works both ways
Linked Data Cloud, March 2009
Four principles for linked data • Use URIs to identify things that you expose to the Web as resources • Use HTTP URIs so that people can locate and look up (dereference) these things. • When someone looks up a URI, provide useful information • Include links to other, related URIs in the exposed data as a means of improving information discovery on the Web -- Tim Berners-Lee, 2006
4. 5 billion triples for free • The full public LOD dataset has about 4. 5 billion triples as of March 2009 • Linking assertions are spotty, but probably include order 10 M equivalences • Availability: –download the data in RDF –Query it via a public SPARQL servers – load it as an Amazon EC 2 public dataset –Launch it and required software as an Amazon public AMI image
Wikitology We’ve been exploring a different approach to derive an ontology from Wikipedia through a series of use cases: – Identifying user context in a collaboration system from documents viewed (2006) – Improve IR accuracy by adding Wikitology tags to documents (2007) – ACE: cross document co-reference resolution for named entities in text (2008) – TAC KBP: Knowledge Base population from text (2009) – Improve Web search engine by tagging documents and queries (2009)
Wikitology 2. 0 (2008) RDF Freebase KB Databases RDF graphs text Yago Human input & editing Word. Net
Wikitology tagging • Using Serif’s output, we produced an entity document for each entity. Included the entity’s name, nominal and pronominal mentions, APF type and subtype, and words in a window around the mentions • We tagged entity documents using Wikitology producing vectors of (1) terms and (2) categories for the entity • We used the vectors to compute features measuring entity pair similarity/dissimilarity
Wikitology Entity Document & Tags Wikitology entity document <DOC> <DOCNO>ABC 19980430. 1830. 0091. LDC 2000 T 44 -E 2 <DOCNO> <TEXT> Name Webb Hubbell PER Type & subtype Individual NAM: "Hubbell” "Hubbells” "Webb Hubbell” "Webb_Hubbell" PRO: "he” "him” "his" Mention heads abc's accountant after again ago alleges alone also and arranged attorney avoid been before being betray but came can cat charges cheating circle clearly close concluded conspiracy cooperate counsel's department did disgrace do dog dollars earned eightynine enough evasion feel financial firm first four friends going got grand happening has he help him hi s hope house hubbells hundred hush income increase independent indicted indictment inner investigating jackie_judd jail jordan judd jury justice kantor ken knew lady late law left lie little make many mickey mid money mr my nineteen nineties ninetyfour nothing now office others paying peter_jennings president's pressured probe prosecutors questions reported reveal rock saddened said schemed seen seven since starr statement such taxes tell them they thousand time today ultimately vernon washington webb_hubbell were what's whether which whitewater why wife years </TEXT> </DOC> Words surrounding mentions Wikitology article tag vector Webster_Hubbell 1. 000 Hubbell_Trading_Post National Historic Site 0. 379 United_States_v. _Hubbell 0. 377 Hubbell_Center 0. 226 Whitewater_controversy 0. 222 Wikitology category tag vector Clinton_administration_controversies 0. 204 American_political_scandals 0. 204 Living_people 0. 201 1949_births 0. 167 People_from_Arkansas 0. 167 Arkansas_politicians 0. 167 American_tax_evaders 0. 167 Arkansas_lawyers 0. 167
Top Ten Features (by F 1) Prec. Recall F 1 Feature Description 90. 8% 76. 6% 83. 1% some NAM mention has an exact match 92. 9% 71. 6% 80. 9% Dice score of NAM strings (based on the intersection of NAM strings, not words or n-grams of NAM strings) 95. 1% 65. 0% 77. 2% the/a longest NAM mention is an exact match 86. 9% 66. 2% 75. 1% Similarity based on cosine similarity of Wikitology Article Medium article tag vector 86. 1% 65. 4% 74. 3% Similarity based on cosine similarity of Wikitology Article Long article tag vector 64. 8% 82. 9% 72. 8% Dice score of character bigrams from the 'longest' NAM string 95. 9% 56. 2% 70. 9% all NAM mentions have an exact match in the other pair 85. 3% 52. 5% 65. 0% Similarity based on a match of entities' top Wikitology article tag 85. 3% 52. 3% 64. 8% Similarity based on a match of entities' top Wikitology article tag 85. 7% 32. 9% 47. 5% Pair has a known alias
Knowledge Base Population • The 2009 NIST Text Analysis Conference (TAC) will include a new Knowledge Base Population track • Goal: discover information about named entities (people, organizations, places) and incorporate it into a KB • TAC KBP has two related tasks: – Entity linking: doc. entity mention -> KB entity – Slot filling: given a document entity mention, find missing slot values in large corpus
KBs and IE are Symbiotic KB info helps interpret text Knowledge Base Information Extraction from Text IE helps populate KBs
Wikitology 3. 0 (2009) Application Specific Algorithms IR collection Articles Wikitology Code RDF reasoner Relational Database Triple Store Category Links Infobox Graph Infobox Page Link Graph Linked Semantic Web data & ontologies
Wikipedia’s social network • Wikipedia has an implicit ‘social network’ that can help disambiguate PER mentions • Resolving PER mentions in a short document to KB people who are linked in the KB is good • The same can be done for the network of ORG and GPE entities
WSN Data • We extracted 213 K people from the DBpedia’s Infobox dataset, ~30 K of which participate in an infobox link to another person • We extracted 875 K people from Freebase, 616 K of were linked to Wikipedia pages, 431 K of which are in one of 4. 8 M person-person article links • Consider a document that mentions two people: George Bush and Mr. Quayle
Which Bush & which Quayle? Six George Bushes Nine Male Quayles
A simple closeness metric Let Si = {two hop neighbors of Si} Cij = |intersection(Si, Sj)| / |union(Si, Sj) | Cij>0 for six of the 56 possible pairs 0. 43 George_H. _W. _Bush -- Dan_Quayle 0. 24 George_W. _Bush -- Dan_Quayle 0. 18 George_Bush_(biblical_scholar) -- Dan_Quayle 0. 02 George_Bush_(biblical_scholar) -- James_C. _Quayle 0. 02 George_H. _W. _Bush -- Anthony_Quayle 0. 01 George_H. _W. _Bush -- James_C. _Quayle
Application to TAC KBP • Using entity network data extracted from Dbpedia and Wikipedia provides evidence to support KBP tasks: –Mapping documentions into infobox entities –Mapping potential slot fillers into infobox entities –Evaluating the coherence of entities as potential slot fillers
Conclusion • The Semantic Web approach is a powerful approach for data interoperability and integration • The research focus is shifting to a “Web of Data” perspective • Many research issue remain: uncertainty, provenance, trust, parallel graph algorithms, reasoning over billions of triples, user-friendly tools, etc. • Just as the Web enhances human intelligence, the Semantic Web will enhance machine intelligence • The ideas and technology are still evolving
http: //ebiquity. umbc. edu/
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