PREFERENCE BASED TERM WEIGHTING FOR ARABIC FIQH DOCUMENT RANKING

In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with α value (IPFα). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by α. IPFα combined with the existing weighting methods become TF.IDF.IBF.IPFα. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively

A review of approaches to solving the problem of BIM search: towards intelligence-assisted design

Due to the growing adoption of BIM and the rising popularity of cloud computing, BIM models are increasingly stored in central cloud repositories or Common Data Environments. Effective management and exploitation of these models creates the requirement for BIM retrieval systems. Thus far, the BIM industry has utilized general-purpose, text-based search techniques that operate on BIM metadata. This paper highlights the need for a domain-specific BIM search engine and reviews various approaches to address the problem of BIM search. Three main approaches were identified as context-, geometry-, and content-based BIM retrieval. For a comprehensive BIM retrieval system, all three approaches need to be utilized. Literature about geometry- and content-based retrieval was scarce, and about context-based retrieval was almost non-existent. Context-based retrieval is a special approach that is relevant here due to the project-based and goal-oriented nature of architectural design and needs support from stakeholders in the AECO industry

An Approach of a Personalized Information Retrieval Model based on Contents Semantic Analysis

En este trabajo se presenta una primera aproximación de un modelo de recuperación de información personalizada basado en el procesamiento semántico del contenido. El modelo propuesto reduce la sobrecarga de información innecesaria para los usuarios y mejora los resultados recuperados mediante la combinación de un procesamiento semántico de contenido aplicado a las consultas y documentos indexados, y la información de los perfiles de usuarios. La aplicabilidad de la propuesta fue evaluada en el contexto de un motor de búsqueda real, a través de consultas diseñadas por expertos en diferentes dominios y la medición de su rendimiento. Los resultados obtenidos fueron comparados con los del motor de búsqueda puesto a prueba, lográndose mejoras en cuanto a la precisión y exhaustividad.In this paper, an approach of a personalized information retrieval model based on the semantic processing of the content is proposed. The proposed model reduces the unnecessary information overload for users and improves the retrieval results through combining a content semantic processing applied to the queries and indexed documents, and information user processing from different perspectives. The applicability of the proposal was evaluated in the context of a real web search engine, through several queries designed by experts and associated to differents topics, and the measurement of their performance. The results were compared to those obtained by the search engine put to the test, achieving improvements the retrieval results.Este trabajo ha sido parcialmente financiado por el proyecto METODOS RIGUROSOS PARA EL INTERNET DEL FUTURO (MERINET), financiado por el Fondo Europeo de Desarrollo Regional (FEDER) y el Ministerio de Economía y Competitividad (MINECO), Ref. TIN2016-76843-C4-2-R

Knowledge Extraction from Open Data Repository

The explosion of affluent social networks, online communities, and jointly generated information resources has accelerated the convergence of technological and social networks producing environments that reveal both the framework of the underlying information arrangements and the collective formation of their members. In studying the consequences of these developments, we face the opportunity to analyze the POD repository at unprecedented scale levels and extract useful information from query log data. This chapter aim is to improve the performance of a POD repository from a different point of view. Firstly, we propose a novel query recommender system to help users shorten their query sessions. The idea is to find shortcuts to speed up the user interaction with the open data repository and decrease the number of queries submitted. The proposed model, based on pseudo-relevance feedback, formalizes exploiting the knowledge mined from query logs to help users rapidly satisfy their information need

Modelo para la recuperación de información con expansión de consulta y perfil de preferencia de los usuarios

Comprender la intención de búsqueda del usuario permite identificar y extraer los resultados de búsqueda más relevantes y personalizados de la información disponible según sus necesidades. En el presente artículo se plantea un algoritmo para la recuperación de información relevante que combina las preferencias del perfil del usuario y la expansión de consulta para obtener resultados de búsqueda relevantes y personalizados. El proceso de recuperación de información se valida mediante las métricas de Precision, Recall y Mean Average Precision (MAP) aplicadas a un conjunto de datos que contiene los documentos estandarizados y los perfiles de preferencias. Los resultados permitieron demostrar que el algoritmo mejora el proceso de recuperación de información al arrojar documentos con mejor calidad y relevancia según las necesidades de los usuarios

Context-based understanding of food-related queries using a culinary knowledge model

Dietary practices are governed by a mix of ethnographic aspects, such as social, cultural and environmental factors. These aspects need to be taken into consideration during an analysis of food-related queries. Queries are usually ambiguous. It is essential to understand, analyse and refine the queries for better search and retrieval. The work is focused on identifying the explicit, implicit and hidden facets of a query, taking into consideration the context – culinary domain. This article proposes a technique for query understanding, analysis and refinement based on a domain specific knowledge model. Queries are conceptualised by mapping the query term to concepts defined in the model. This allows an understanding of the semantic point of view of a query and an ability to determine the meaning of its terms and their interrelatedness. The knowledge model acts as a backbone providing the context for query understanding, analysis and refinement and outperforms other models, such as Schema.org, BBC Food Ontology and Recipe Ontology

Open Data

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data’s integrity and transparency. This book describes how data retrieved from public open data repositories can improve the learning qualities of digital networking, particularly performance and reliability. Chapters address such topics as knowledge extraction, Open Government Data (OGD), public dashboards, intrusion detection, and artificial intelligence in healthcare

A WEB PERSONALIZATION ARTIFACT FOR UTILITY-SENSITIVE REVIEW ANALYSIS

Online customer reviews are web content voluntarily posted by the users of a product (e.g. camera) or service (e.g. hotel) to express their opinions about the product or service. Online reviews are important resources for businesses and consumers. This dissertation focuses on the important consumer concern of review utility, i.e., the helpfulness or usefulness of online reviews to inform consumer purchase decisions. Review utility concerns consumers since not all online reviews are useful or helpful. And, the quantity of the online reviews of a product/service tends to be very large. Manual assessment of review utility is not only time consuming but also information overloading. To address this issue, review helpfulness research (RHR) has become a very active research stream dedicated to study utility-sensitive review analysis (USRA) techniques for automating review utility assessment. Unfortunately, prior RHR solution is inadequate. RHR researchers call for more suitable USRA approaches. Our current research responds to this urgent call by addressing the research problem: What is an adequate USRA approach? We address this problem by offering novel Design Science (DS) artifacts for personalized USRA (PUSRA). Our proposed solution extends not only RHR research but also web personalization research (WPR), which studies web-based solutions for personalized web provision. We have evaluated the proposed solution by applying three evaluation methods: analytical, descriptive, and experimental. The evaluations corroborate the practical efficacy of our proposed solution. This research contributes what we believe (1) the first DS artifacts to the knowledge body of RHR and WPR, and (2) the first PUSRA contribution to USRA practice. Moreover, we consider our evaluations of the proposed solution the first comprehensive assessment of USRA solutions. In addition, this research contributes to the advancement of decision support research and practice. The proposed solution is a web-based decision support artifact with the capability to substantially improve accurate personalized webpage provision. Also, website designers can apply our research solution to transform their works fundamentally. Such transformation can add substantial value to businesses

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen