summary models for routing keywords to linked data sources

Thanh Tran, AIFB Institute, KIT, [email protected] KIT – University of the State of Baden-Wuerttemberg and National Laboratory of the Helmholtz Association1

Summary Models for Routing Keywords to Linked Data SourcesThanh Tran, Lei Zhang, Rudi Studer

AIFB Institute, KIT

Thanh Tran, AIFB Institute, KIT, [email protected] KIT – University of the State of Baden-Wuerttemberg and National Laboratory of the Helmholtz Association

Agenda

Introduction

Opportunities & challenges

Contributions

Problem Definition

LOD Data

Keyword Query Answer

Keyword Query Routing

Summary Models

Keyword sets

Element-level vs. schema-level vs.

source-level Summary

Validity of Results vs. complexity

Theo. / Exp. Results

Conclusions2


Semantic Data

- 203 linked datasets serve 25 billion RDF triples interconnected by 395 million links- As of 09-2010 + other data (e.g. LON, ontologies, RDFa ) + increasing rapidly...

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More Data

More Links


Opportunities

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“Articles from awarded researchers at Stanford ”

Freebase contains data about people DBPedia contains information about awards DBLP contains bibliographic data

More Data

More Links

More complex information needs More precise results More integrated results


Problems“Articles from awarded researchers at Stanford ”

z) n(x,publicatio Stanford) name(y, y) worksAt(x, Award) Turing prizes(x,.,).( yxz

Formulating queries is a hard task!• Which data sources?• Which schema elements?

Processing queries is expensive!• Process against all data sources?

Large number of unknown & irrelevant sources! What is in there? What is relevant?

USABILITY SCALABILITY

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Given the needs expressed as sets of keywords, are there “corresponding answers” in linked data? and what combination of data sources can be used to

produce them?

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Identify valid combination of sources using keywords

Present schema elements for the user to formulate query

Let user choose combination of sources

Process only relevant combinations of sources


Contributions

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Introduce the novel problem of keyword query routing

Introduce various summary models, which aim to compactly represent the search space.

Investigate the resulting trade-offs between result quality and efficiency through theoretical analysis and practical experiments using publicly available linked data sources.

Propose the multi-level relationship graph to capture its search space.


Agenda

Introduction


Contributions

Problem Definition

LOD Data



Summary Models

Keyword sets


source-level Summary



Conclusions8


LOD Element-level Graph

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Web data modeled as a set of interlinked data graphs Each data graph represent a source Element-level graph vs. schema-level graph vs. source-level graph


LOD Schema-level Graph

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Author

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LOD Source-level Graph

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“Corresponding” Answers

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Problem Definition

Keyword query result (also called Steiner graph) is a subgraph of the union of the data- and schema-level graph that for every keyword, contains a matching element, and these elements are pairwise connected over a path.

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d-max Steiner graph is a Steiner graph where paths between keyword elements is d-max or less.

Keyword query routing: compute valid set of data sources called keyword routing plan. A plan is valid if its sources produce non-empty keyword query results.


A Valid Keyword Routing Plan

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The Search Space Multi-level inter-relationship graphs capture the entire search space Relationships between elements and between different levels

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Search space is too large! Naïve solution not applicable: apply existing approaches to

keyword search for computing Steiner graphs Steiner graphs might span several linked sources Search space grow exponentially with the number of

sources and their associated links


Agenda

Introduction


Contributions

Problem Definition

LOD Data



Summary Models

Keyword sets


source-level KERG



Conclusions16


Keyword Sets

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One keyword set for every data source Elements stand for distinct keywords mentioned in a source


Element-level Keyword-Element Relationship Graph (E- KERG)

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A keyword-element captures a keyword k and the data element mentioning k A relationship between two keyword-elements exists iff there is a path between

their associated data elements In d-max KERG, the paths to be considered have length d-max or less

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Schema-level Keyword-Element Relationship Graph (S-KERG)

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A keyword-element captures a keyword k and the schema element which contains some instances (date elements) mentioning k

A relationship between two keyword-elements exists if there is a path between some instances of their associated schema elements

Groups elements (relationships) when they capture same pair of keywords in the same class (same keyword relationships between same pair of classes)


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Data-Source-level Keyword-Element Relationship Graph (D-KERG)

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A keyword-element captures a keyword k and the source which contains some instances (date elements) mentioning k

A relationship between two keyword-elements exists if there is a path between some instances of their associated sources

Groups elements (relationships) when they capture same pair of keywords in the same source (same keyword relationships between the same of pair sources)


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Agenda

Introduction


Contributions

Problem Definition

LOD Data



Summary Models

Keyword sets


source-level KERG



Conclusions22


Theoretical Results When Steiner graphs can be found for K in the data,

then there will be keyword routing plan that can be found in KERG.

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The keyword routing plan derived from the summary are not necessarily valid s.t. there might be no corresponding Steiner graph in the data

Detailed results + algorithms + complexity results in the paper!


Experiments

Chunk of the BTC dataset containing 10M RDF triples from 154 sources, linked via 500K mappings

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Manually crafted 30 keyword valid multi-data-source queries, i.e., produce non-empty keyword answers and involve more than 2 sources Town River America Beijing Conference Database 2007


Validity

P@k measure the percentage of plans that are valid out of the top-k plans P@5 up to 100% for E-KERG (dmax =4), P@5 for KS only 6% More valid plans were computed when a higher value was used for dmax

dmax =3 seems to be a good tradeoff Queries with larger number of keywords resulted in lower precision

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Performance

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Times increased with higher values for dmax

Sharp for E-KERG and S-KERG Relatively stable for D-KERG

Times increase with number of keywords All other models had poor performance w.r.t complex queries but D-KERG E-KERG needed more than 100s for queries with more than 2 keywords

Time for D-KERG was no more than 10ms on average

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Conclusions

Keyword query routing helps users without knowledge of linked data and schemas to find combination of sources that contain answers corresponding to their needs

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Summarizing relationships is essential for dealing with the large-scale linked data Web (E-KERG achieved poor performance, requires more than 100s for complex queries)

Summarizing at the level of sources (D-KERG) represents the most practical trade-off, produces results in less than 10ms out of which every second one was valid

However, validity still low for complex queries (<30% when 4 keywords)

Baseline approaches for novel problem Further improve validity and consider relevance! Combine keyword query routing with source and structured query

processing to compute final results!


Thanks for Your Attention!

Institute AIFB, KIT

[email protected]

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mailto:[email protected]

summary models for routing keywords to linked data sources

Education

kit university

source elementlevel

data sourcesthanh tran

bibliographic data

semantic data

state of baden

helmholtz association

combination of data