The propositional calculus is a branch of logic. It is also called (first-order) propositional logic, statement logic, sentential calculus, sentential logic, or sometimes zeroth-order logic. It deals with propositions and relations between propositions, including the construction of arguments based on them. Compound propositions are formed by connecting propositions by logical connectives representing the truth functions of conjunction, disjunction, implication, biconditional, and negation. Some sources include other connectives, as in the table below.
In logic and mathematics, statements and are said to be logically equivalent if they have the same truth value in every model. The logical equivalence of and is sometimes expressed as , , , or , depending on the notation being used. However, these symbols are also used for material equivalence, so proper interpretation would depend on the context. Logical equivalence is different from material equivalence, although the two concepts are intrinsically related.
Equivalence or Equivalent may refer to:
The material conditional is an operation commonly used in logic. When the conditional symbol is interpreted as material implication, a formula is true unless is true and is false. Material implication can also be characterized inferentially by modus ponens, modus tollens, conditional proof, and classical reductio ad absurdum.
Logic in computer science covers the overlap between the field of logic and that of computer science. The topic can essentially be divided into three main areas:
In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including:
The semantic spectrum, sometimes referred to as the ontology spectrum, the smart data continuum, or semantic precision, is a series of increasingly precise or rather semantically expressive definitions for data elements in knowledge representations, especially for machine use.
In metadata, a match report is a report that compares two distinct data dictionaries and creates a list of the data elements that have been identified as semantically equivalent.
Semantic translation is the process of using semantic information to aid in the translation of data in one representation or data model to another representation or data model. Semantic translation takes advantage of semantics that associate meaning with individual data elements in one dictionary to create an equivalent meaning in a second system.
In metadata, a vocabulary-based transformation (VBT) is a transformation aided by the use of a semantic equivalence statements within a controlled vocabulary.
In computer metadata, semantic equivalence is a declaration that two data elements from different vocabularies contain data that has similar meaning. There are three types of semantic equivalence statements:
In metadata, a synonym ring or synset, is a group of data elements that are considered semantically equivalent for the purposes of information retrieval. These data elements are frequently found in different metadata registries. Although a group of terms can be considered equivalent, metadata registries store the synonyms at a central location called the preferred data element.
A semantic mapper is tool or service that aids in the transformation of data elements from one namespace into another namespace. A semantic mapper is an essential component of a semantic broker and one tool that is enabled by the Semantic Web technologies.
Ontology alignment, or ontology matching, is the process of determining correspondences between concepts in ontologies. A set of correspondences is also called an alignment. The phrase takes on a slightly different meaning, in computer science, cognitive science or philosophy.
Semantic file systems are file systems used for information persistence which structure the data according to their semantics and intent, rather than the location as with current file systems. It allows the data to be addressed by their content. Traditional hierarchical file-systems tend to impose a burden, for example when a sub-directory layout is contradicting a user's perception of where files would be stored. Having a tag-based interface alleviates this hierarchy problem and enables users to query for data in an intuitive fashion.
Amit Sheth is a computer scientist at University of South Carolina in Columbia, South Carolina. He is the founding Director of the Artificial Intelligence Institute, and a professor of Computer Science and Engineering. From 2007 to June 2019, he was the Lexis Nexis Ohio Eminent Scholar, director of the Ohio Center of Excellence in Knowledge-enabled Computing, and a professor of Computer Science at Wright State University. Sheth's work has been cited by over 48,800 publications. He has an h-index of 117, which puts him among the top 100 computer scientists with the highest h-index. Prior to founding the Kno.e.sis Center, he served as the director of the Large Scale Distributed Information Systems Lab at the University of Georgia in Athens, Georgia.
Semantic matching is a technique used in computer science to identify information which is semantically related.
In natural language processing, semantic role labeling is the process that assigns labels to words or phrases in a sentence that indicates their semantic role in the sentence, such as that of an agent, goal, or result.
Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.
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 or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.
