basically , by and large , chiefly , generally , largely , mainly , mostly , predominantly , primarily , principally , substantially ;

The word gender has been used since the 14th century as a grammatical term, referring to classes of noun designated as masculine, feminine, or neuter in some languages. The sense denoting biological sex has also been used since the 14th century, but this did not become common until the mid 20th century. Although the words gender and sex are often used interchangeably, they have slightly different connotations; sex tends to refer to biological differences, while gender more often refers to cultural and social differences and sometimes encompasses a broader range of identities than the binary of male and female

RDF and OWL Full are designed for systems in which data may be widely distributed (., the Web). As such a system becomes larger, it becomes both impractical and virtually impossible to know where all of the data in the system are located. Therefore, one cannot generally assume that data obtained from such a system are complete. If some data appear to be missing, one has to assume, in general, that the data might exist somewhere else in the system. This assumption, roughly speaking, is known as the open world assumption [ OWL-GUIDE ].

Around 1960, Ray Solomonoff founded the theory of universal inductive inference , the theory of prediction based on observations; for example, predicting the next symbol based upon a given series of symbols. This is a formal inductive framework that combines algorithmic information theory with the Bayesian framework. Universal inductive inference is based on solid philosophical foundations, [17] and can be considered as a mathematically formalized Occam's razor . Fundamental ingredients of the theory are the concepts of algorithmic probability and Kolmogorov complexity .

Around 1960, Ray Solomonoff founded the theory of universal inductive inference , the theory of prediction based on observations; for example, predicting the next symbol based upon a given series of symbols. This is a formal inductive framework that combines algorithmic information theory with the Bayesian framework. Universal inductive inference is based on solid philosophical foundations, [17] and can be considered as a mathematically formalized Occam's razor . Fundamental ingredients of the theory are the concepts of algorithmic probability and Kolmogorov complexity .