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Title: "Text Representation, Retrieval, and Understanding with Knowledge Graphs" - Ai2

Vídeo -> https://youtu.be/ZyYec3X4pkY

Abstract: Search engines and other information systems have started to evolve from retrieving documents to providing more intelligent information access. However, the evolution is still in its infancy due to computers’ limited ability in representing and understanding human language. This talk will present my work addressing these challenges with knowledge graphs. 

The first part is about utilizing entities from knowledge graphs to improve search. I will discuss how we build better text representations with entities and how the entity-based text representations improve text retrieval. 

The second part is about better text understanding through modeling entity salience (importance), as well as how the improved text understanding helps search under both feature-based and neural ranking settings. This talk concludes with future directions towards the next generation of intelligent information systems.

 Bag of Words

 

Vocabulary Mismatch, Shalow Understanding, Writing Queries requires knowledge (content oriented). 

  • KG (Structured Semantics) and Semantics: Entity-oriented search (Entity retrieval) + Semantic Search

node = entity (concrete and abstract), has attributes

Entity linking between Query and KG entities. 

Query and documents are represented as bag-of-entities and match occurs in the entity space

Move from words to entities. 

Sparse Graphs because similar entities may not be connected.

Connect all entities by their similarities in the embeddings spaces

Soft Match (embeddings) x Exact Match (words ou entities)

Ranking Performance

Search for one or more concepts. Search for relations is more common in Q&A

 

 Lessons learned: combined approaches

  • Large Scale Text Understanding: Entity salience (ranking)

Bag-of-Words x Bag-of-Entities: set of individuals, bag of things, shallow understanding


 

More than count "things" frequency, identify the importance, the central entity (centrality)

 

Hubness Problem of semantic similarity ... ver artigo: [Xu et al. 2015]

Very high dimensional embeddings space, anything that is not similar has approximate the same distance. (Para reduzir a sobrecarga de informação isso pode não ser um problema)

 

Separate similarity into several difference ranges to model how other entities are connected (not only similar): related, unrelated, conflict

 

 NÃO ENTENDI A A FÓRMULA DE CALCULO

Similarity != Relevance


 

  • Deep Learning
Matching the word embeddings with trigram and bigram, learn the ranking score

 

More Contributing, more Relevant

Ranking is learned from Document, Query and Entity embeddings

 

Combination of Entity Retrieval and Information Retrieval

Semantic: Structured (KG) and Distributed (Embeddings)

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