Could we automatically reproduce semantic relations of an information retrieval thesaurus?

A well constructed thesaurus is recognized as a valuable source of semantic information for various applications, especially for Information Retrieval. The main hindrances to using thesaurus-oriented approaches are the high complexity and cost of manual thesauri creation. This paper addresses the problem of automatic thesaurus construction, namely we study the quality of automatically extracted semantic relations as compared with the semantic relations of a manually crafted thesaurus. The vector-space model based on syntactic contexts was used to reproduce relations between the terms of a manually constructed thesaurus. We propose a simple algorithm for representing both single word and multiword terms in the distributional space of syntactic contexts. Furthermore, we propose a method for evaluation quality of the extracted relations. Our experiments show significant difference between the automatically and manually constructed relations: while many of the automatically generated relations are relevant, just a small part of them could be found in the original thesaurus

Developing Of The Related Data Search Lsa-based Algorithm And Its Programmed Realization

In this article let\u27s consider the theoretical basis of the data search in large data ordered arrays based on the context of the search request and tracking of semantic relationships. Also the first steps towards the practical implementation of this task are proposed. Simple program to check author\u27s thoughts has been developed. All the researches have been made with the VK social network. Internal API VK was used as retrieving data tool. The final results say that the VK\u27s content has many opportunities to make them more useful and searchable, which means that it is possible to use this ‘property\u27 to create our own, more user-friendly way to search and get important data, in the first, for example, buying-selling information, from many kinds of data sources (official pages, users\u27 profiles etc.). That feature never been presented (and probably won\u27t) in other social networks like Facebook or Instagram. The material in this article will be used later while the author\u27s PhD thesis writing

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Metadata impact on research paper similarity

While collaborative filtering and citation analysis have been well studied for research paper recommender systems, content-based approaches typically restrict themselves to straightforward application of the vector space model. However, various types of metadata containing potentially useful information are usually available as well. Our work explores several methods to exploit this information in combination with different similarity measures

Structured textual data monitoring based on a rough set classifier

Text is frequently stored in structures that are frequently complex and sometimes too large to be fully understood and/or apprehended. This problem has concerned the data mining community for many years as well as the information's community. Many algorithms have been proposed with the objective of obtaining better answers to the queries made and to obtain better queries that can respond to the questions that are in the users mind. Some of those algorithms are based on the relations between the concepts. But some of those relations are also dynamic and are, themselves, relevant information. This paper describes and adaptation of one of those methods, based on the Rough Sets theory, in order to detect changes in the existing relations between the stored concepts and, through that, to detect new relevant aspects of the data.- (undefined

Getting the knowledge to the agent : the Rough Sets approach

For a query in a Research Information System (CRIS) to return adequate results, it is necessary that the system can “understand” the intention of the enquiring agent. One possible approach to guarantee the success of this communication is to create an intermediate module, responsible for the knowledge discovery processes, that can define concepts translatable in the languages used by the different agents involved in the use of a CRIS, enhance the queries, construct information over the available information and construct knowledge about the knowledge available in the CRIS and about its use. The Rough Set theory is a powerful tool that can set a path to achieve this goal. This paper describes in what way that is achievable, while describing the approach that is being followed by the portuguese CRIS, Degóis

A Four Layer Bayesian Network for Product Model Based Information Mining

Business and engineering knowledge in AEC/FM is captured mainly implicitly in project and corporate document repositories. Even with the increasing integration of model-based systems with project information spaces, a large percentage of the information exchange will further on rely on isolated and rather poorly structured text documents. In this paper we propose an approach enabling the use of product model data as a primary source of engineering knowledge to support information externalisation from relevant construction documents, to provide for domain-specific information retrieval, and to help in re-organising and re-contextualising documents in accordance to the user’s discipline-specific tasks and information needs. Suggested is a retrieval and mining framework combining methods for analysing text documents, filtering product models and reasoning on Bayesian networks to explicitly represent the content of text repositories in personalisable semantic content networks. We describe the proposed basic network that can be realised on short-term using minimal product model information as well as various extensions towards a full-fledged added value integration of document-based and model-based information