some support nesting, some databases support symbols like "&", etc. Not all databases handle Boolean operators in the same way (e.g.May bring back too many, too few, or irrelevant results if keywords are not carefully selected.Making connections between keywords or emphasizing relationships between keywords when searching.Narrowing down or broadening your search results by connecting search terms together using logic.This will narrow down your research results because the search engine will bring back only resources about the first search term (cats), but exclude any resources that include the second search term (dogs). The Boolean operator NOT tells the search engine that you want to find information about the first search term, but nothing about the second. This will broaden your search results because the search engine will bring back any results that have either search term in them. The Boolean operator OR tells the search engine that you want to find information about either search term you've entered. This will narrow down your search results because the search engine will only bring back results that include both search terms. The Boolean operator AND tells a search engine that you want to find information about two (or more) search terms. These are logic-based words that help search engines narrow down or broaden search results. Boolean search is a basic logic tool that uses the operative words AND, OR and NOT to help employers significantly improve their efficiency at recruiting candidates they find well-suited for open roles in their company. Boolean searching uses operators: words like AND, OR, and NOT. AND is intersection, OR is union.Boolean searching is used to help find search results faster and with more precision. Now, you simply do set oporations on bunch of sets. Note that this model actually allows AND semantics by setting alpha=1, and OR semantics by alpha!=1.īoolean search is basically set terminology:Įach term is associated with a set that contains all the documents that have this term in them. Now - when we have a query of more than one term: q=t1 t2. a common technique is: P(word|document) = alpha*#occurances(word,document)/|document| + (1-alpha)*#occurances(word,corpus)/|corpus| To avoid having probability zero - we usually add smoothing technique. Other models are building a language model out of a document - a language model is described as P(word|M) = the probability of the model M to generate the word.Ī common language model is P(word|document) = #occurances(word,document)/|document| The first is using a boolean model to get candidates, and the 2nd is using vector-space to get a score for each document. In addition, this method has an important advantage - it returns a score associated to each document, and not only a boolean answer "relevant" or "not relevant".Īs-is vector-space does not allow AND,OR oporations, however this is easily solveable by doing 2-phase search. You may wish to use more than one Boolean Operator in a. Even if some of those articles about dogs also mention cats, they will not turn up in search results because using the NOT operator excludes them. You do not oporate on 'sets', you compare similarity of vectors - this is entirely different model. Negating Boolean Operator A search for cats NOT dogs retrieves all results that refer to cats and none of the results that refer to dogs. This model goes well with the tf-idf model (The td-idf determines what is the value in each entry of each vector). The more similar the document is to the query - the better the result is.Ī common similarity measure is cosine-similarity. The similarity in this model is done by creating a 'fake' document - which is the query, and comparing this fake document to any other document in the corpus. The dimension of each document is the number of terms in the vocabulary. In this model, each document is a vector, represented by the words (or bi-grams.) it contains. Probably the most common example of such a method is the vector-space model. ![]() ![]() ![]() Non boolean search includes approaches that are not purely boolean model techniques 1.
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