Entity Search: Building Bridges between Two Worlds Krisztian Balog , - - PowerPoint PPT Presentation

Entity Search: Building Bridges between Two Worlds

Krisztian Balog, Edgar Meij, and Maarten de Rijke ISLA, University of Amsterdam

http://ilps.science.uva.nl

Entity search

• Information organized around entities
• Instead of finding documents about the

entity, find the entity itself

• Problem looked at by both the

Information Retrieval (IR) and the Semantic Web (SW) communities

Entity search tasks

• Entity ranking
• List completion
• Related entity finding

Motivation

• To which extent are IR and SW methods

capable of answering information needs related to entity finding?

Where are we now?

• Information Retrieval
• Identifying and ranking entities in

large volumes of data

• Mostly based on co-occurrences

between terms and entities

• Generated models are not always

meaningful for human consumption

Where are we now?

• Semantic Web
• Structured data, naturally organized

around entities

• Entity retrieval is as simple as running

SPARQL queries?

• Free-text querying is more appealing

to (naive) end users

Related entity finding

• Given
• Input entity E (name plus homepage)
• Type T of the target entity (person,
• rganization, or product)
• Narrative R (describes nature of

relation)

• Return homepages of related entities

Example topics

(E) Source entity name (E) Source entity URL (T) Target type (R) Narrative Medimmune, Inc. clueweb09-en0008-26-39300 Product Products of Medimmune, Inc. (E) Source entity name (E) Source entity URL (T) Target type (R) Narrative Boeing 747 clueweb09-en0005-75-02292 Organisation Airlines that currently use Boeing 747 planes.

Aim

• Compare IR and SW approaches on the

related entity finding task

• Focusing on finding all relevant entities,

but not on actually ranking them

Related entity finding

Our variation

• TREC Entity 2009 topics (20)
• Map source entity to a Wikipedia page (17)
• Map target category to the most specific

class within the DBPedia ontology

• Ground truth: Wikipedia pages from

relevance assessments

Example topic

(E) Source entity name (E) Source entity URL (T) Target type (R) Narrative Boeing 747 clueweb09-en0005-75-02292 Organisation Airlines that currently use Boeing 747 planes. Source entity DBPedia-owl Relation Boeing_747 Organisation/Company/Airline Airlines that currently use Boeing 747 planes.

IR approaches

• Aggregation of approaches employed at

the TREC Entity track

• Various ways of recognizing and ranking

entities

• Common to all is a mechanism for

capturing the co-occurrence between source and target entities

A typical IR approach

Query (input entity, relation) Document/snippet retrieval Answer candidate extraction Answer candidate (type) filtering Answer candidate ranking Output (related entities)

Two SW approaches

• SPARQL query
• Exhaustive graph search
• Find all paths between E and T in a

knowledge base

• The depth of search is limited

SELECT DISTINCT ?m ?r WHERE { ?m rdf:type dbpedia-owl:Drug . { ?m ?r dbpedia:MedImmune } UNION { dbpedia:MedImmune ?r ?m } }

SPARQL on DBPedia

Query: Products of Medimunne, Inc.

?m ?r dbpedia:Amifostine dbp-prop:wikilink dbpedia:Blinatumomab dbp-prop:wikilink dbpedia:Motavizumab dbp-prop:wikilink dbpedia:Palivizumab dbp-prop:wikilink

SPARQL on DBPedia

Query: Airlines that Air Canada has code share flights with.

?m ?r dbpedia:Air_Canada dbp-prop:wikilink dbpedia:Austrian_Airlines dbp-prop:wikilink dbpedia:Japan_Airlines dbp-prop:wikilink dbpedia:Lufthansa dbp-prop:wikilink dbpedia:Turkish_Airlines dbp-prop:wikilink ... dbpedia:Air_Ontario dbp-ontology:Company/parentCompany dbpedia:Air_Canada_Tango dbp-ontology:Company/parentCompany dbpedia:Canadian_Airlines dbp-ontology:foundationPerson

SPARQL on DBPedia

Query: Members of the band Jefferson Airplane.

?m ?r dbpedia:Jim_Morrison dbp-prop:wikilink dbpedia:Jimi_Hendrix dbp-prop:wikilink ... dbpedia:Jack_Casady dbp-ontology:associatedMusicalArtist dbpedia:Paul_Kantner dbp-ontology:associatedMusicalArtist dbpedia:Joey_Covington dbp-ontology:associatedMusicalArtist dbpedia:Marty_Balin dbp-ontology:associatedMusicalArtist ... dbpedia:Grace_Slick dbp-prop:pastMembers dbpedia:Jorma_Kaukonen dbp-prop:pastMembers ...

Findings

• IR and SW methods find basically the

same set of entities

• Most relations returned by SW methods

are of type wikilink

Next

• Extend search to Linked Open Data (LOD)
• We use the Linked Data Semantic

Repository (LDSR)

SPARQL on LOD

?m ?r dbpedia:Amifostine dbp-prop:wikilink dbpedia:Blinatumomab dbp-prop:wikilink dbpedia:Motavizumab dbp-prop:wikilink dbpedia:Palivizumab dbp-prop:wikilink dbpedia:Motavizumab fb:base.bioventurist.product.developed_by dbpedia:Palivizumab fb:base.bioventurist.science_or_technology_company.products dbpedia:Motavizumab fb:base.bioventurist.product.developed_by dbpedia:Palivizumab fb:base.bioventurist.science_or_technology_company.products

Query: Products of Medimunne, Inc.

Graph search on LOD

Findings

• More entities as well as more diverse

relations

• Having more data does not

automatically improve results

• Some of the identified entities are now

too general

Summarizing findings

• Information Retrieval
• Excellent ways of finding associations

between topics and entities

• Tend to perform better for less popular

entities (not represented in LOD)

• Missing: semantics of the found

associations

Summarizing findings

• Semantic Web
• Has the potential of generating a large

number of candidate entities and relations

• Could be as simple as instantiating a

SPARQL query

• For many queries LOD is very sparse

w.r.t. semantically meaningful links between entities

Zooming out

• Enhance text-based models with

semantic information from LOD

• Use IR models to discover and label links

between entities in LOD

TREC Entity 2010

• Main task: Related entity finding
• Pilot task: List completion
• Given URIs of related entities,

complete the list with additional entities from LOD

Questions?

Krisztian Balog

http://staff.science.uva.nl/~kbalog