In this paper, we introduce an expressive ontology-based model for representing resources with respect to a domain ontology. Our resource model is based on semantic web standards as well as established ontologies and metadata schemas such as SUMO, MPEG-7 and Dublin Core to provide a reference model for ontology-based information retrieval. Based on this expressive resource model, the user can d...

Distributed information retrieval is a well-known approach for accessing heterogeneous, highly autonomous sources of unstructured information. Selecting and querying only a number of relevant sources can help improve its performance, but most resource selection algorithms are limited to syntactic comparisons. We present a framework for applying resource selection in the context of a semantic fe...

Uniform resource locators (URLs), which mark the address of a resource on the World Wide Web, are often human-readable and can indicate metadata about a resource. This paper explores the mining of URLs to yield categoric metadata about web resources via a three-phase pipeline of word segmentation, abbreviation expansion and classification. I apply this approach to the problem of subject metadat...

Uniform resource locators (URLs), which mark the address of a resource on the World Wide Web, are often human-readable and can indicate metadata about a resource. This paper explores the mining of URLs to yield categoric metadata about web resources via a three-phase pipeline of word segmentation, abbreviation expansion and classification. I apply this approach to the problem of subject metadat...

Key resource page is one of the most important search target pages for Web search users. Decision tree learning is one of the most widely-used and practical methods for inductive inference in machine learning. Because of the difficulty in uniform sampling of Web pages, there are not enough negative instances for training a key resource decision tree. To solve the problem, the original algorithm...

This paper presents a method for building concept lattices by learning concepts from RDF annotations of Web documents. It consists in extracting conceptual descriptions of the Web resources from the RDF graph gathering all the resource annotations and then forming concepts from all possible subsets of resources each such subset being associated with a set of descriptions shared by the resources...

Display grammar is a coupling of semantic and signal processing information for interactive multimedia. We propose a computational formalization binding media resource data and authoring information, including contextual data and interactive processing algorithms. We hypothesize display grammar provides a domain for mapping a range of applications of MMIE and media resource retrieval. We demons...

information retrieval can be achieved through computerized processes by generating a list of relevant responses to a query. the document processor, matching function and query analyzer are the main components of an information retrieval system. document retrieval system is fundamentally based on: boolean, vector-space, probabilistic, and language models. in this paper, a new methodology for mat...

In this paper, we present a hybrid Chinese language model based on a combination of ontology with statistical method. In this study, we determined the structure of such a Chinese language model. This structure is firstly comprised of an ontology description framework for Chinese words and a representation of Chinese lingual ontology knowledge. Subsequently, a Chinese lingual ontology knowledge ...

In this paper we propose a search approach that can process large volumes of textual data efficiently and effectively even in environments where computational resources are limited. The traditional search solution for large collections assumes availability of practically unlimited computational resources. For many applications and organization this assumption is not realistic. Empirical evaluat...