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Document Retrieval google knowledge graph Doing graph search over structured knolwedge rather than traditional search

Approaches to Question Answering:

1.Knowledge Based Approach -Build a semantic representation of the query : Times, Dates, locations, entities, numeric quantities -Map from this semantics to query structured data or resources Eg: Geospatial databases, Ontologies(Wikipedia Infoboxes, dbPedia, WordNet, Yago)

  1. Text Based(Mainly Factoid) QA -Question Processing: Detect question type, answer type, focus, relations. Formulate queries to send to a search engine -Passage Retrieval: Retrieve ranked documents. Break into suitable passages and rerank -Answer Processing: Extract candidate answers(as named entities). Rank candidates(using evidence from relations in the text and external sources)

  2. Hybrid Approaches -Build a shallow representation of the query -Genearte answer candidates using IR methods: Augmented with ontologies and semi-structured data -Score each candidate using richer knowledge sources

Document Retreival Research Papers

Improving Document Ranking with Dual Word Embeddings

A Dual Embedding Space Model for Document Ranking

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