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Semantic data models

by Joan Peckham, Fred Maryanski - ACM Computing Surveys , 1988
"... Semantic data models have emerged from a requirement for more expressive conceptual data models. Current generation data models lack direct support for relationships, data abstraction, inheritance, constraints, unstructured objects, and the dynamic properties of an application. Although the need for ..."
Abstract - Cited by 178 (4 self) - Add to MetaCart
Semantic data models have emerged from a requirement for more expressive conceptual data models. Current generation data models lack direct support for relationships, data abstraction, inheritance, constraints, unstructured objects, and the dynamic properties of an application. Although the need

Semantic Data Caching and Replacement

by Shaul Dar, Michael J. Franklin, Björn T. Jónsson, Divesh Srivastava, Michael Tan , 1996
"... We propose a semantic model for client-side caching and replacement in a client-server database system and compare this approach to page caching and tuple caching strategies. Our caching model is based on, and derives its advantages from, three key ideas. First, the client maintains a semantic descr ..."
Abstract - Cited by 208 (4 self) - Add to MetaCart
description of the data in its cache,which allows for a compact specification, as a remainder query, of the tuples needed to answer a query that are not available in the cache. Second, usage information for replacement policies is maintained in an adaptive fashion for semantic regions, which are associated

Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema

by Jeen Broekstra, Arjohn Kampman, Frank Van Harmelen , 2002
"... RDF and RDF Schema are two W3C standards aimed at enriching the Web with machine-processable semantic data. ..."
Abstract - Cited by 543 (11 self) - Add to MetaCart
RDF and RDF Schema are two W3C standards aimed at enriching the Web with machine-processable semantic data.

Automatic labeling of semantic roles

by Daniel Gildea - Computational Linguistics , 2002
"... We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Various lexical and syntactic features are derived from parse trees and used to derive statistical classifiers from hand-annotated training data. 1 ..."
Abstract - Cited by 747 (15 self) - Add to MetaCart
We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Various lexical and syntactic features are derived from parse trees and used to derive statistical classifiers from hand-annotated training data. 1

A semantics of multiple inheritance

by Luca Cardelli - Information and Computation , 1988
"... There are two major ways of structuring data in programming languages. The first and common one, used for example in Pascal, can be said to derive from standard branches of mathematics. Data is organized as cartesian products (i.e. record types), disjoint sums (i.e. unions or variant types) and func ..."
Abstract - Cited by 528 (9 self) - Add to MetaCart
There are two major ways of structuring data in programming languages. The first and common one, used for example in Pascal, can be said to derive from standard branches of mathematics. Data is organized as cartesian products (i.e. record types), disjoint sums (i.e. unions or variant types

Probabilistic Latent Semantic Indexing

by Thomas Hofmann , 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
Abstract - Cited by 1225 (10 self) - Add to MetaCart
Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized

Probabilistic Latent Semantic Analysis

by Thomas Hofmann - In Proc. of Uncertainty in Artificial Intelligence, UAI’99 , 1999
"... Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two--mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Sema ..."
Abstract - Cited by 771 (9 self) - Add to MetaCart
Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two--mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent

Ontology Learning for the Semantic Web

by Er Maedche, Steffen Staab - IEEE Intelligent Systems , 2001
"... The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering o ..."
Abstract - Cited by 492 (16 self) - Add to MetaCart
The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering

Semantic similarity based on corpus statistics and lexical taxonomy

by Jay J. Jiang, David W. Conrath - Proc of 10th International Conference on Research in Computational Linguistics, ROCLING’97 , 1997
"... This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantifie ..."
Abstract - Cited by 873 (0 self) - Add to MetaCart
This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better

Unsupervised Learning by Probabilistic Latent Semantic Analysis

by Thomas Hofmann - Machine Learning , 2001
"... Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurren ..."
Abstract - Cited by 618 (4 self) - Add to MetaCart
Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co
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