Build a Searchable Knowledge Base

Jimmy Lai Yahoo! Search Engineer

r97922028 [at] ntu.edu.tw 2014/05/18

http://www.slideshare.net/jimmy_lai/build-a-searchable-knowledge-base

Outline• Introduction to Knowledge Base

• Construct a Knowledge Base

• Search the Knowledge Base • string match • synonym search • full text search • geo search • put all together

• More Applications

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Knowledge

• Knowledge is power. - Francis Bacon, 1597

• Knowledge is boundless and connected. So, an efficient interface to search and browse the knowledge base is essential.

• Let’s try to build a searchable knowledge base.

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Application of Knowledge Base

Personal assistant: Siri, Google now

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Search engine: Google’s knowledge graph

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Construct a Knowledge Base

1. Find good data sources.

2. Aggregate data as knowledge entity.

3. Construct structured data of knowledge entity.

4. Search the knowledge base.

5. Navigate the knowledge base.

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Wikipedia• A collaborated encyclopedia with more than 30M

articles over 287 languages.

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• A good source of knowledge base. However the data of Wikipedia is not well-structured.

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http://www.theguardian.com/technology/blog/2009/aug/13/wikipedia-edits

DBpedia

• http://wiki.dbpedia.org/About

• Structured data from Wikipedia.

• A good data source for a knowledge base.

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Knowledge Entity

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Identifier

Abstract

Relations

What can Python do for us• Data Wrangling

• Process the raw text data • Aggregate the data from different sources • Output data as json format

• Connecting the Data flow between systems • Automation script for starting services and

feeding data • REST API implementing search strategy

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Example code

git clone git@github.com:jimmylai/knowledge.git!

https://github.com/jimmylai/knowledge!

• required python packages: 1. fabric 2. pysolr 3. django

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Data Preparation1. Download data from DBpedia

http://downloads.dbpedia.org/current/en/

2. Filter out some specific knowledge entity zcat instance_types_en.nt.bz2 | get_id_list.py

3. Parse and aggregate data entity from files.

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data file script data fieldshort_abstracts_en.nt.bz2 get_abstract.py abstractraw_infobox_properties_en.nt.bz2 get_relation.py relationsgeo_coordinates_en.nt.bz2 get_geo.py latlonredirects_en.nt.bz2 get_redirect.py redirects

Aggregated Data Format"http://dbpedia.org/resource/Lake_Yosemite": { "latlon": "37.376389,-120.428889", "redirects": [ "Lake_yosemite" ], "abstract": "Lake Yosemite is an artificial freshwater lake located approximately five miles (8 km) east of Merced, California in the rolling Sierra Foothills. UC Merced is situated approximately half a mile (0.8 km) south of Lake Yosemite. The university is bounded by the lake on one side and two canals (Fairfield Canal and Le Grand Canal) run through the campus. In 2007, a myth featured in the Mythbusters' James Bond Special 1 episode was filmed and tested at Lake Yosemite.", "relations": { "type": "http://dbpedia.org/resource/Reservoir", "location": "http://dbpedia.org/resource/California" } }

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Search by• Solr is a full-text, real-time search engine based on Apache

lucene.

• Provides REST-like API.

• pysolr make the use of Solr easily.

• Download the latest version 4.8.0 from http://www.apache.org/dyn/closer.cgi/lucene/solr/4.8.0 and extract to solr/solr-4.8.0 dir

• Start Solr server and then check the web UI fab start_solrhttp://localhost:8983/solr/

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Search - String Match• To be able to search by entity name

python feed_data.py string_match

• config: solr/conf/string_match/schema.xml <field name="name" type="string" indexed="true" stored="true" multiValued="false"/> <field name="abstract" type="string" indexed="false" stored="true" multiValued="false"/>

• Feed the entities to Solr. Each entity with name and abstract fields.

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Search - String Match

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http://localhost:8983/solr/string_match/select?q=name%3A%22San+Francisco%22&wt=json&indent=true

Search by entity name.

Search - Synonym• To be able to search by synonym of entity name

python feed_data.py synonym_string_match

• config: solr/conf/synonym_string_match/schema.xml <field name="name" type=“name_text" indexed="true" stored="true" multiValued="false"/> !<fieldType name="name_text" class="solr.TextField" positionIncrementGap="100"> <analyzer type="index"> <tokenizer class="solr.StandardTokenizerFactory"/> <filter class="solr.SynonymFilterFactory" synonyms="synonyms.txt" ignoreCase="true" expand="true"/> <filter class="solr.LowerCaseFilterFactory"/> </analyzer> …

• Restart Solr server and the synonym file will be reloaded.17

Synonym handling at index time

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Synonym handling at query time

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Search - Synonym

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Search by synonym.

Search - Full Text Search• To be able to search by entity name

python feed_data.py full_text_search

• config: solr/conf/full_text_search/schema.xml <copyField source="name" dest="text"/> <copyField source="abstract" dest=“text"/> !

• Feed the entities to Solr. Each name and abstract field will be copied to the text field. After that we can do full text search without specify field to search.

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Search - Full Text Search

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Search - Geo Search• To be able to search by distance given a location

python feed_data.py geo_search

• config: solr/conf/geo_search/schema.xml <field name="location" type="location" indexed="true" stored="true" required="false" multiValued="false" />

• Feed the entities to Solr. Each entity contains a location field and the format is like "51.670100,-3.230100".

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Given condition on distance

Search - Put All Together• Search Strategy

1. Input a query

2. Search by synonym match

3. Search by full text

1. If input a location, filter the result by geo search

• Implement the search strategy as an API

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Implement the search strategy in a Django view

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Review

• A Knowledge Base with synonym, full-text and geo search API.

• The knowledge entities are connected by relation.

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More Applications• Question answering system:

1.Query analysis: identify the intension (e.g. looking for specific type of entity)

2.Search in the knowledge base 3.Return the knowledge entity

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The modern search engine don’t just provide web page urls. They provide the direct answer to users.

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More Data Sources and Knowledge Entities

• Open Data

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• Open APIs

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My Life in • Build online services for billions of users.

• Big data mining on cloud infrastructures.

• Open and Innovative working environment.

• International teamwork and English communication.

• Business trips to Silicon Valley.

• Send me your resume if you need a referral. r97922028 [at] ntu.edu.tw

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