Apache Solr for Multi-language Content Discovery Through Entity Driven Search
Using entity driven search for multi-language content discovery and search.
As we countdown to the annual Lucene/Solr Revolution conference in Austin this October, we’re highlighting talks and sessions from past conferences. Today, we’re highlighting Alessandro Benedetti’s session on using entity driven search for multi-language content discovery and search.
This talk is about the description of the implementation of a Semantic Search Engine based on Solr. Meaningfully structuring content is critical, Natural Language Processing and Semantic Enrichment is becoming increasingly important to improve the quality of Solr search results. Our solution is based on three advanced features:
- Entity-oriented search – Searching not by keyword, but by entities (concepts in a certain domain)
- Knowledge graphs – Leveraging relationships amongst entities: Linked Data datasets (Freebase, DbPedia, Custom …)
- Search assistance – Autocomplete and Spellchecking are now common features, but using semantic data makes it possible to offer smarter features, driving the users to build queries in a natural way.
The approach includes unstructured data processing mechanisms integrated with Solr to automatically index semantic and multi-language information. Smart Autocomplete will complete users’ query with entity names and properties from the domain knowledge graph. As the user types, the system will propose a set of named entities and/or a set of entity types across different languages. As the user accepts a suggestion, the system will dynamically adapt following suggestions and return relevant documents. Semantic More Like This will find similar documents to a seed one, based on the underlying knowledge in the documents, instead of tokens.
Alessandro Benedetti is a search expert and semantic technology passionate, working in the R&D division of Zaizi. His favorite work is in R&D on information retrieval, NLP and machine learning with a big emphasis on data structures, algorithms and probability theory. Alessandro earned his Masters in Computer Science with full grade in 2009, then spent 6 month with Universita’ degli Studi di Roma working on his masters thesis around a new approach to improve semantic web search. Alessandro spent 3 years with Sourcesense as a Search and Open Source consultant and developer.
http://www.slideshare.net/lucidworks/multilanguage-content-discovery-through-entity-driven-search-alessandro-benedetti
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