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LEDS

KNOWLEDGE EXTRACTIONFROM HETEROGENEOUS

SEMI-STRUCTURED DATA SOURCES

MARTIN SEIDEL, MICHAEL KRUG, FRANK BURIAN, MARTIN GAEDKE

16. September 2016

LEDSCURRENT SITUATION

• knowledge in the Web often only available as weakly interlinked, heterogeneous, semi-structured data

à no semantic classification• how to link or merge data?• how to do semantic queries?

à not usable in a meaningful way

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LEDSGOAL

Extraction of knowledge from semi-structured data• knowledge in terms of semantic metadata• semantically enriched data then can utilize

the potential of Linked Data

à provide an automatic process

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LEDS

THE KESEDAAPPROACH

LEDSTHE KESEDA APPROACH

• Especially designed to work on JSON data

• Challenges when working with JSON data

à no schema, only name-value pairs

à any structure and depth possible

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LEDSTHE KESEDA APPROACH

{"id": "krug”,"firstName": "Michael","lastName": "Krug","title": "Dipl.-Inf.","phone": "+49 371 531 39929","email": "michael.krug@informatik.tu-chemnitz.de",[...]

}

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LEDSTHE KESEDA APPROACH

{"id": "2015-007","title": "SmartComposition: ...","author": [ "Michael Krug", "Martin Gaedke"],"year": "2015","type": "Conference Paper","event": {

"name": "24th International World Wide Web Conference","url": http://www.www2015.it/

},[...]

}

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Arrays

Objects

LEDSTHE KESEDA APPROACH

• multi-step algorithm• work in existing JSON structure

• find and store various matches with different weights• use additional information sources like API descriptions

• assign classes to objects with multiple properties

• link detected entities

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LEDSTHE KESEDA APPROACH

1. Differentiation of input sources / formats2. Preparation of data structure

3. Analysis of property labels4. Analysis of property values

5. Mapping of classes

6. Generate JSON-LD document7. Evaluation of results

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LEDS

PROTOTYPE

LEDSPROTOTYPE

• prototype implemented in Node.js• working with properties and classes from:• schema.org• foaf• dublincore• goodrelations• music ontology

• dictionaries for: first & last names, cities, streets, languages• list of manually curated synonyms• option to provide pre-defined mappings

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LEDSPROTOTYPE

• Web interface for• pre-configuration• mappings, synonyms, dictionaries

• data upload• result analysis• statistics and browsing

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LEDSPROTOTYPE

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CONFIGURATION

LEDSPROTOTYPE

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RESULTS

LEDS

EVALUATION

LEDSEVALUATION

Algorithm applied to datasets of

1) JSON array of people

2) JSON array of publications

a) Without custom pre-configuration

b) With custom pre-configuration

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LEDSEVALUATION

Initial Setup• dictionary and structure pattern matching• label à predicate string matching• classes and properties: schema.org, foaf, dublincore, goodrelations

Custom Pre-Configuration• set of label à predicate mappings (hand-picked for data context)• list of known synonyms• more structure patterns

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LEDS1A) PEOPLE W/O CONFIG

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LEDS1A) PEOPLE W/O CONFIG

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LEDS2A) PEOPLE W/ CONFIG

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LEDS2A) PEOPLE W/ CONFIG

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LEDS1B) PUBLICATIONS W/O CONFIG

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LEDS1B) PUBLICATIONS W/O CONFIG

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LEDS2B) PUBLICATIONS W/ CONFIG

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LEDS2B) PUBLICATIONS W/ CONFIG

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LEDS

SUMMARY

LEDSSUMMARY

➙ Approach for extracting knowledge from semi-structured data

➙ by applying a multi-step algorithm➙ to convert JSON data to RDF➙ that assigns known classes to objects and maps

their properties to S-P-O triples

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LEDSOPEN CHALLENGES

• detect and reuse JSON structure pattern• disambiguate values• apply quality control to results• improve scalability for large datasets• research application of machine learning

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LEDS

THANK YOU!MICHAEL.KRUG@INFORMATIK.TU-CHEMNITZ.DE

VSR.INFORMATIK.TU-CHEMNITZ.DE

WWW.LEDS-PROJEKT.DE

16. September 201629

LEDS

LEDSTHE KESEDA APPROACH

1. Differentiation of input sources / formats

• text, file, URL, API• check for format

• optional conversion of XML to JSON

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LEDSTHE KESEDA APPROACH

2. Preparation of data structure

• pre-process JSON tree to store matches and mappings• keep original structure to preserve hierachie for later

relations• detect arrays and objects for seperate processing

• clean up: remove empty entries

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LEDSTHE KESEDA APPROACH

3. Analysis of property labels

• string matching (substrings, prefixes, …)

• synonyms

• pre-defined mappings• use metadata from API description, if available

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LEDSTHE KESEDA APPROACH

4. Analysis of property values

• dictionaries

• structure patterns (uri, date, address, color…)

• data types (date, time, number, boolean…)• (lower weighted)

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LEDSTHE KESEDA APPROACH

5. Mapping of classes

• find class by number of matched properties

• select match that is most appropriate for chosen class

• take different weights into account

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LEDSTHE KESEDA APPROACH

6. Generate JSON-LD document

• use matches and mappings

• link entities depending on JSON tree structure

• validation of output• optional conversion to various RDF formats

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LEDSTHE KESEDA APPROACH

7. Evaluation of results

• manual or automatic comparision of actual vs. desired result to reweight matching components

• store correctly applied mappings for later reuse

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