Concept indexing

Journal of Computer and Knowledge Engineering, 2020

One of the most challenging aspects of developing information systems is the processing and management of large volumes of information. One way to overcome this problem is to implement efficient data indexing and classification systems. As large volumes of generated data comprise of non-structured textual data, developing text processing, management and indexing frameworks can play an important role in providing users with accurate information according to their preferences. In this paper, a novel method of semantic information processing, management and indexing is introduced. The main goals of this study is to integrate structured knowledge of ontology and Knowledge Bases (KBs) in the core components of the method, to enrich the contents of the documents, to have multi-level semantic network representation of textual resources, to introduce a hybrid weighting schema (salient score) and finally to propose a hybrid method of semantic similarity computation. The structured knowledge of ontology and KBs are integrated from all aspects of the proposed method. The obtained results indicate the accuracy and optimal performance of the proposed framework. The obtained results suggest that using knowledge-based models leads to higher performance and accuracy in identifying and classifying documents according to user preferences; however, if learning-based models are not provided with sufficient amount of training data, they cannot yield satisfying results. The results also demonstrate that the complete integration of ontology and KBs in information systems can significantly contribute to a better representation of documents and evidently superior functionality of information processing, management and indexing systems.

1st International Workshop on New Developments in Digital Libraries at ICEIS-2001, 2001

We discuss the task of selection of the concepts that describe the contents of a given document. We propose to use a large hierarchical concept dictionary (thesaurus) for this task. A statistical method of document indexing driven by such a dictionary is proposed. The problem of handling non-terminal nodes in the hierarchy is discussed. Common sense-complaint methods of automatically assigning the weights to the nodes and links in the hierarchy are presented. The application of the method in a system Classifier is discussed.

A document index represents a concise ordered compilation of the document's most important topics. It provides direct and fast access to the document parts related to the index information. Together with structural knowledge of the document itself in connection with general knowledge about indexing a 2-layered Index Graph is defined that is further mapped to an ontology representation. By defining suitable metrics it is shown how the Index Graph can be utilised to augment semantic applications. We have developed a system for supporting the author of a document in the process of index compilation. Other possible applications include document visualisation, and semantic document annotation.

2nd IEEE Conference on Intelligent Information Processing …, 2

Database and Expert Systems Applications, 1992

In the access to the unstructured information, the representation of the relevant concepts is a fundamental issue for the sharing of the knowledge between indexers and users. The browsing of the concepts can play an important role in the user interface. We present an implementation of an interface that will give the possibility of interacting with a conceptual structure of the documents, and of making a graphical navigation on the thesauri available on the different fields.

2005

Given a large hierarchical concept dictionary (thesaurus, or ontology), the task of selection of the concepts that describe the contents of a given document is considered. A statistical method of document indexing driven by such a dictionary is proposed. The method is insensible to inaccuracies in the dictionary, which allow for semi-automatic translation of the hierarchy into different languages. The problem of handling non-terminal and especially top-level nodes in the hierarchy is discussed. Common sense-complaint methods of automatically assigning the weights to the nodes and links in the hierarchy are presented. The application of the method in the Classifier system is discussed. Keywords: Document Characterization, Document Comparison, Ontology, Statistical Methods."""

1992

ABSTRACT We describe the use of our knowledge management system, CODE2, as an aid to documenters of a complex software system called Telos. CODE2 was first used by the designers of Telos to clarify their design concepts and terminology.

With rapid growth of the Internet, how to get information from this huge information space becomes an even important problem. In this paper, An

2002

The Web is a colossal document repository that is nowadays processed by humans only. Machines' role is limited to transmission and layout processing, barely being able to do something else with contents of documents. Therefore, information retrieval in the current Web is a difficult task where many results provide little relevance. The Semantic Web tries to solve this and other problems by means of new technologies to build ontologies and to annotate semantically the documents.

2010

The paper presents an ontology-driven approach to semantic annotation, indexing and retrieval of fine-grained units of document data. In this approach, the document units and the user query are both represented by weighted vectors of ontological concepts. To determine the relevance of the document units to given query, we measure the similarity between their concept vectors. Results of the preliminary evaluation conducted with a prototype implementation are promising. We present brief analysis of these results.