Pesquisa de Doutorado da Veronica

 5.1 Ranking  ***standard ranking techniques for document-centric keyword search on text, such as: BM25 scoring, language models*** BM25 substitui o TF/IDF no ElasticSearch BM25 is a ranking function that ranks a set of documents based on the query terms appearing in each document, regardless of the inter-relationship between the query terms within a document (e.g., their relative proximity). It is not a single function, but actually a whole family of scoring functions, with slightly different components and parameters. It is used by search engines to rank matching documents according to their relevance to a given search query and is often referred to as “Okapi BM25,” since the Okapi information retrieval system was the first system implementing this function. The BM25 retrieval formula belongs to the BM family of retrieval models (BM stands for Best Match) Fonte:  https://doi.org/ 10.1007/978-0-387-39940-9_921 BM25F for ad-hoc entity retrieval on RDF data BM25F is a modific

Continuando o post sobre o survey de Busca Semântica ... seção 4 Cada subseção tem um tópico sobre Benchmark pq em Information Retrieval, assim como em BD, é uma prática  comum usar becnhmarks para avaliar sistemas/propostas.  Cada subseção começa com uma tabela para caracterizar o grupo de sistemas que serão analisados contendo as seguintes itens: Data e Search (duas dimensões da classificação), Approach (descrição dos passos ou das diretrizes comuns do grupo), Forças e Limitações. 4.1 Keyword Search in Text Basic techniques in matching are: lemmatization or stemming (houses -> house or hous) ... ainda não temos solução para portuguese stemmer no busc@NIMA ... , synonyms (search -> retrieval) ... a WordNet em Português pode ajudar (synsets) ..., error correction (algoritm -> algorithm), relevance feedback (given some relevant documents, enhance the query to find more relevant documents), proximity (of some or all of the query words) and concept models (matching the topic of

Hoje (15/12) comecei a pesquisar sobre "semantic search" na literatura mais recente.Fiz "foward snowballing" de um survey que usei no Mestrado. MANGOLD, C. A. ― Survey and classification of semantic search approaches. International Journal of Metadata Semantics and Ontology, Vol. 2, Nr. 1, 2007. Achei uma definição na Encyclopedia of Big Data Technologies (Springer 2018). Cudre-Mauroux P. (2019) Semantic Search. In: Sakr S., Zomaya A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_231 "Semantic Search regroups a set of techniques designed to improve traditional document or knowledge base search. Semantic Search aims at better grasping the context and the semantics of the user query and/or of the indexed content by leveraging natural language processing, Semantic Web, and machine learning techniques to retrieve more relevant results from a search engine. " Em termos de abordagens entendi que para o