Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results (possibly the distinctive selection of one single piece of information) or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.
Semantic queries work on named graphs, linked data or triples. This enables the query to process the actual relationships between information and infer the answers from the network of data. This is in contrast to semantic search, which uses semantics (meaning of language constructs) in unstructured text to produce a better search result. (See natural language processing.)
From a technical point of view, semantic queries are precise relational-type operations much like a database query. They work on structured data and therefore have the possibility to utilize comprehensive features like operators (e.g. >, < and =), namespaces, pattern matching, subclassing, transitive relations, semantic rules and contextual full text search. The semantic web technology stack of the W3C is offering SPARQL to formulate semantic queries in a syntax similar to SQL. Semantic queries are used in triplestores, graph databases, semantic wikis, natural language and artificial intelligence systems.
Relational databases represent all relationships between data in an implicit manner only. For example, the relationships between customers and products (stored in two content-tables and connected with an additional link-table) only come into existence in a query statement (SQL in the case of relational databases) written by a developer. Writing the query demands exact knowledge of the database schema.
Linked-Data represent all relationships between data in an explicit manner. In the above example, no query code needs to be written. The correct product for each customer can be fetched automatically.
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Explore les intégrations de mots, les modèles tels que CBOW et Skipgram, Fasttext, Glove, les intégrations de sous-mots et leurs applications dans la recherche et la classification de documents.
Named graphs are a key concept of Semantic Web architecture in which a set of Resource Description Framework statements (a graph) are identified using a URI, allowing descriptions to be made of that set of statements such as context, provenance information or other such metadata. Named graphs are a simple extension of the RDF data model through which graphs can be created but the model lacks an effective means of distinguishing between them once published on the Web at large.
Un triplestore est une base de données spécialement conçue pour le stockage et la récupération de données RDF (Resource Description Framework). Tout comme une base de données relationnelle, un triplestore stocke des données et il les récupère via un langage de requête. Mais contrairement à une base de données relationnelle, un triplestore ne stocke qu'un seul type de données : le triplet. Elle n'a donc pas besoin de phase d'initialisation pour enregistrer de nouvelles données.
DBpedia est un projet universitaire et communautaire d'exploration et extraction automatiques de données dérivées de Wikipédia. Son principe est de proposer une version structurée et normalisée au format du web sémantique des contenus de Wikipedia. DBpedia vise aussi à interconnecter Wikipédia avec d'autres ensembles de données ouvertes provenant du Web des données. DBpedia a été conçu par ses auteurs comme l'un des , connu également sous le nom de Web des données, et l'un de ses possibles points d'entrée.
Robustness of medical image classification models is limited by its exposure to the candidate disease classes. Generalized zero shot learning (GZSL) aims at correctly predicting seen and unseen classes and most current GZSL approaches have focused on the s ...
Cham2023
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Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information, such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider t ...
2023
This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine ...