A progressive query language and interactive reasoner for information fusion support

Ledezma

Rev. ing. univ. Medellín

2016

Large volume of data is growing because organizations are continuously capturing the collective amount of data for a better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust. ResumenHay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.

Section: Heterogeneitymentioning

confidence: 99%

A systematic review of data quality issues in knowledge discovery tasks

Ledezma

Rev. ing. univ. Medellín

2016

Information Retrieval Technology

“…There are interesting fusion based reasoning approaches in the literature. Chang et al [4] proposed an interactive reasoner abbreviated as Pequliar that applies progressive query language and interactive reasoning (cum learning) for information fusion support. However, this research is limited in that the reasoner is guided by humans to elaborate the query by means of some rule and then a query processor uses this to produce a more informative answer.…”

Section: Motivationmentioning

confidence: 99%

Topic Assisted Fusion to Re-rank Texts for Multi-faceted Information Retrieval

Prasath

Duane

O’Reilly

2013

We propose to develop a framework for an intelligent business information system with multi-faceted data analysis capabilities that supports complex decision making processes. Reasoning and Learning of contextual factors from texts of financial services data are core aspects of the proposed framework. As part of the proposed framework, we present an approach for the ordering of contextual information from textual data with the help of latent topics identified from the web corpus. The web corpus is prepared by specifically using a number of financial services sources on the web that describe various aspects of mobile payments and services. The proposed approach first performs weighting of query terms and retrieves the initial set of texts from the web corpus. We use Latent Dirichlet Allocation (LDA) on this web corpus to identify the topics that relate to the contextual features of various financial services/products. The retrieved texts are scored based on the identified topics that could cover a variety of contextual factors. We performed subjective evaluation to identify the relevance of the contextual information retrieved, and found that the proposed approach captures a variety of key contexts pertaining to user information needs in a better way with the support of topic assisted contextual factors.

2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation

“…Some of the recent activities in this respect are: conceptual [8], semantic [9,10], or ontology-based [11] query expansions. Query reformulation using automatically generated query concepts from a document space [12] is another research in this field, while the other research activity can be enumerated as using progressive query language and interactive reasoner for information fusion support [13]. It is noteworthy that, examining the effectiveness of real-time query expansion has a significant role in suitable retrieval and user satisfaction [14].…”

Section: Existing Approaches To Query Expansionmentioning

confidence: 99%

A Framework for Query Expansion Based on Viewpoint-Oriented Manipulation of the Related Concepts

Badie

Mahmoudi

Ghaderi

2010

Although search engines are quite useful for obtaining information from the World Wide Web, users still have problems obtaining the most relevant information when processing their web queries. Different methods exist for query expansion with various levels of success. A major point in query expansion techniques is to consider the relations existing between the query terms as a means for retrieving more relevant web pages. Therefore, the more comprehensible these relations are observed, the more relevance can be expected for the retrieved web pages. Taking this point into account, in this paper, a framework is represented for query expansion based on viewpoint-oriented manipulation of the related query terms. Comparison between the relevant retrieved documents, on proposed queries with Google results, illustrate how more relevant results are obtained in the situations where the very viewpoints type relations existing between the query terms are clearly detectable and in the meantime no semantic relation may at the first look exists between the query terms. In this paper, we have made use of two approaches to retrieval issue, out of which one is based on information fusion while the other is grounded on collaboration of the results in a selective way.