Semantic similarity

About: Semantic similarity is a research topic. Over the lifetime, 14605 publications have been published within this topic receiving 364659 citations. The topic is also known as: semantic relatedness.

Representation and processing of structures with binary sparse distributed codes

Abstract: The schemes for compositional distributed representations include those allowing on-the-fly construction of fixed dimensionality codevectors to encode structures of various complexity. Similarity of such codevectors takes into account both structural and semantic similarity of represented structures. We provide a comparative description of sparse binary distributed representation developed in the framework of the associative-projective neural network architecture and the more well known holographic reduced representations of T.A. Plate (1995) and binary spatter codes of P. Kanerva (1996). The key procedure in associative-projective neural networks is context-dependent thinning which binds codevectors and maintains their sparseness. The codevectors are stored in structured memory array which can be realized as distributed auto-associative memory. Examples of distributed representation of structured data are given. Fast estimation of the similarity of analogical episodes by the overlap of their codevectors is used in the modeling of analogical reasoning both for retrieval of analogs from memory and for analogical mapping.

Three theses of representation in the semantic web

Abstract: The Semantic Web is vitally dependent on a formal meaning for the constructs of its languages. For Semantic Web languages to work well together their formal meanings must employ a common view (or thesis) of representation, otherwise it will not be possible to reconcile documents written in different languages. The thesis of representation underlying RDF and RDFS is particularly troublesome in this regard, as it has several unusual aspects, both semantic and syntactic. A more-standard thesis of representation would result in the ability to reuse existing results and tools in the Semantic Web.

Thin slices of creativity: Using single-word utterances to assess creative cognition

Abstract: We investigated the hypothesis that individual differences in creative cognition can be manifest even in brief responses, such as single-word utterances. Participants (n = 193) were instructed to say a verb upon seeing a noun displayed on a computer screen and were cued to respond creatively to half of the nouns. For every noun–verb pair (72 pairs per subject), we assessed the semantic distance between the noun and the verb, using latent semantic analysis (LSA). Semantic distance was higher in the cued ("creative") condition than the uncued condition, within subjects. Critically, between subjects, semantic distance in the cued condition had a strong relationship to a creativity factor derived from a battery of verbal, nonverbal, and achievement-based creativity measures (β= .50), and this relation remained when controlling for intelligence and personality. The data show that creative cognition can be assessed reliably and validly from such thin slices of behavior.

Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning.

Abstract: Drug-drug interaction (DDI) is a change in the effect of a drug when patient takes another drug. Characterizing DDIs is extremely important to avoid potential adverse drug reactions. We represent DDIs as a complex network in which nodes refer to drugs and links refer to their potential interactions. Recently, the problem of link prediction has attracted much consideration in scientific community. We represent the process of link prediction as a binary classification task on networks of potential DDIs. We use link prediction techniques for predicting unknown interactions between drugs in five arbitrary chosen large-scale DDI databases, namely DrugBank, KEGG, NDF-RT, SemMedDB, and Twosides. We estimated the performance of link prediction using a series of experiments on DDI networks. We performed link prediction using unsupervised and supervised approach including classification tree, k-nearest neighbors, support vector machine, random forest, and gradient boosting machine classifiers based on topological and semantic similarity features. Supervised approach clearly outperforms unsupervised approach. The Twosides network gained the best prediction performance regarding the area under the precision-recall curve (0.93 for both random forests and gradient boosting machine). The applied methodology can be used as a tool to help researchers to identify potential DDIs. The supervised link prediction approach proved to be promising for potential DDIs prediction and may facilitate the identification of potential DDIs in clinical research.

Expressing Implicit Semantic Relations without Supervision

Abstract: We present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. For a given input word pair X:Y with some unspecified semantic relations, the corresponding output list of patterns (P1,..., Pm) is ranked according to how well each pattern Pi expresses the relations between X and Y. For example, given X = ostrich and Y = bird, the two highest ranking output patterns are "X is the largest Y" and "Y such as the X". The output patterns are intended to be useful for finding further pairs with the same relations, to support the construction of lexicons, ontologies, and semantic networks. The patterns are sorted by pertinence, where the pertinence of a pattern Pi for a word pair X:Y is the expected relational similarity between the given pair and typical pairs for Pi. The algorithm is empirically evaluated on two tasks, solving multiple-choice SAT word analogy questions and classifying semantic relations in noun-modifier pairs. On both tasks, the algorithm achieves state-of-the-art results, performing significantly better than several alternative pattern ranking algorithms, based on tf-idf.