text: There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47 on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56 on the 374 analogy questions, statistically equivalent to the average human score of 57 . On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.
  @cite_54: Thispaperdescribes thestructure-mapping engine(SME), a program for studying . analogical processing .SME has been built to explore Gentner's structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory . Its flexibility enhances cognitive simulation studies by simplifying experimentation . Furthermore, SME is very efficient, making it a useful component in machine learning systems as well . We review the structure-mapping theory and describe the design of the engine . We analyze the complexity of the algorithm, and demonstrate that
summary: For example, there is an analogy between the solar system and Rutherford's model of the atom @cite_54 . The solar system is the source domain and Rutherford's model of the atom is the target domain. The basic objects in the source model are the planets and the sun. The basic objects in the target model are the electrons and the nucleus. The planets and the sun have various attributes, such as mass(sun) and mass(planet), and various relations, such as revolve(planet, sun) and attracts(sun, planet). Likewise, the nucleus and the electrons have attributes, such as charge(electron) and charge(nucleus), and relations, such as revolve(electron, nucleus) and attracts(nucleus, electron). SME maps revolve(planet, sun) to revolve(electron, nucleus) and attracts(sun, planet) to attracts(nucleus, electron).