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Retrieving information from the Quran is an important field for Quran scholars and Arabic researchers. There are two types of Quran searching techniques: semantic or concept-based and keyword-based. Concept-based search is a challenging task, especially in a complex corpus such as Quran.This paper presents a concept-based searching tool (QSST) for the Holy Quran. It consists of four phases. In the first phase, the Quran dataset is built by manually annotating Quran verses based on the ontology of Mushaf Al-Tajweed. The second phase is word Embedding, this phase generates features’ vectors for words by training a Continuous Bag of Words (CBOW) architecture on large Quranic and Classic Arabic corpus. The third phase includes calculating the features’ vectors of both input query and Quranic topics. Finally, retrieving the most relevant verses by computing the cosine similarity between both topic and query vectors.The performance of the proposed QSST is measured by comparing results against Mushaf Al-Tajweed. Then, precision, recall, and F-score are computed and their percentages were 76.91%, 72.23% 69.28% respectively. In addition, the results are evaluated by three Islamic experts and the average precision was 91.95%. Finally, QSST results are compared with the recent existing tools; QSST outperformed them.

Retrieving information from the Quran is an important field for Quran scholars and Arabic researchers. There are two types of Quran searching techniques: semantic or concept-based and keyword-based. Concept-based search is a challenging task, especially in a complex corpus such as Quran. This paper presents a concept-based searching tool (QSST) for the Holy Quran. It consists of four phases. In the first phase, the Quran dataset is built by manually annotating Quran verses based on the ontology of Mushaf Al-Tajweed. The second phase is word Embedding, this phase generates features' vectors for words by training a Continuous Bag of Words (CBOW) architecture on large Quranic and Classic Arabic corpus. The third phase includes calculating the features' vectors of both input query and Quranic topics. Finally, retrieving the most relevant verses by computing the cosine similarity between both topic and query vectors. The performance of the proposed QSST is measured by comparing results against Mushaf Al-Tajweed. Then, precision, recall, and F-score are computed and their percentages were 76.91%, 72.23% 69.28% respectively. In addition, the results are evaluated by three Islamic experts and the average precision was 91.95%. Finally, QSST results are compared with the recent existing tools; QSST outperformed them. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel personalized concept-based search mechanism for the Web of Data based on results categorization. The innovation of the paper comes from combining novel categorization and personalization techniques, and using categorization for providing personalization. In our approach, search results (Linked Open Data resources) are dynamically categorized into Upper Mapping and Binding Exchange Layer (UMBEL) concepts using a novel fuzzy retrieval model. Then, results with the same concepts are grouped together to form categories, which we call concept lenses . Such categorization enables concept-based browsing of the retrieved results aligned to users’ intent or interests. When the user selects a concept lens for exploration, results are immediately personalized. In particular, all concept lenses are personally re-organized according to their similarity to the selected lens. Within the selected concept lens; more relevant results are included using results re-ranking and query expansion, as well as relevant concept lenses are suggested to support results exploration. This allows dynamic adaptation of results to the user’s local choices. We also support interactive personalization; when the user clicks on a result, within the interacted lens, relevant lenses and results are included using results re-ranking and query expansion. Extensive evaluations were performed to assess our approach: (i) Performance of our fuzzy-based categorization approach was evaluated on a particular benchmark (∼10,000 mappings). The evaluations showed that we can achieve highly acceptable categorization accuracy and perform better than the vector space model. (ii) Personalized search efficacy was assessed using a user study with 32 participants in a tourist domain. The results revealed that our approach performed significantly better than a non-adaptive baseline search. (iii) Dynamic personalization performance was evaluated, which illustrated that our personalization approach is scalable. (iv) Finally, we compared our system with the existing LOD search engines, which showed that our approach is unique. [ABSTRACT FROM AUTHOR]

In this paper, we identify the problems of current semantic and hybrid search systems, which seek to bridge structure and unstructured data, and propose solutions. We introduce a novel input mechanism for hybrid semantic search that combines the clean and concise input mechanisms of keyword-based search engines with the expressiveness of the input mechanisms provided by semantic search engines. This interactive input mechanism can be used to formulate ontology-aware search queries without prior knowledge of the ontology. Furthermore, we propose a system architecture for automatically fetching relevant unstructured data, complementing structured data stored in a Knowledge Base, to create a combined index. This combined index can be used to conduct hybrid semantic searches which leverage information from structured and unstructured sources. We present the reference implementation Hybrid Semantic Search System ( $$HS^3$$ ), which uses the combined index to put hybrid semantic search into practice and implements the interactive ontology-enhanced keyword-based input mechanism. For demonstration purpose, we apply $$HS^3$$ to the tourism domain. We present performance test results and the results of a user evaluation. Finally, we provide instructions on how to apply $$HS^3$$ to arbitrary domains. [ABSTRACT FROM AUTHOR]

Mobile devices can now handle a great deal of information thanks to the convergence of diverse functionalities. Mobile environments have already shown great potential in terms of providing customized service to users because they can record meaningful and private information continually for long periods of time. The research for understanding, searching and summarizing the everyday-life of human has received increasing attention in recent years due to the digital convergence. In this paper, we propose a mobile life browser, which visualizes and searches human's mobile life based on the contents and context of lifelog data. The mobile life browser is for searching the personal information effectively collected on his/her mobile device and for supporting the concept-based searching method by using concept networks and Bayesian networks. In the experiments, we collected the real mobile log data from three users for a month and visualized the mobile lives of the users with the mobile life browser developed. Some tests on searching tasks confirmed that the result using the proposed concept-based searching method is promising. [ABSTRACT FROM AUTHOR]

Abstract: International patent corpus is a gigantic source containing today about 80million of documents. Every patent is manually analyzed by patent officers and then classified by a specific code called Patent Class (PC). Cooperative Patent Classification CPC is the new classification system introduced since January 2013 in order to standardize the classification systems of all major patent offices. Like keywords for papers, PCs point to the core of the invention, describing concisely what they contain inside. Most of patents strategies are based on PC as filter for results therefore the selection of relevant PCs is often a primary and crucial activity. This task is considered particularly challenging and only few tools have been specially developed for this purpose. The most efficient tools are provided by patent offices of EPO and WIPO. This paper analyzes their PCs search strategy (mainly based on keyword-based engines) in order to identify main limitations in terms of missing relevant PCs (recall) and non-relevant results (precision). Patents have been processed by KOM, a semantic patent search tool developed by the authors. Unlike all other PC search tools, KOM uses semantic parser and many knowledge bases for carrying out a conceptual patent search. Its functioning is described step by step through a detailed analysis pointing out the benefits of a concept-based search vis-à-vis a keyword-based search. An exemplary case is proposed dealing with CPCs describing the sterilization of contact lenses. Comparison could be likewise conducted on other PCs such as International (IPC), European (ECLA) or United States (USPC) patent classification codes. [Copyright &y& Elsevier]

Abstract: We designed and implemented a generic search engine (Vaidurya), as part of our Digital clinical-Guideline Library (DeGeL) framework. Two search methods were implemented in addition to full-text search: (1) concept-based search, which relies on pre-indexing the guidelines in a clinically meaningful fashion, and (2) context-sensitive search, which relies on first semi-structuring the guidelines according to a given ontology, then searching for terms within specific labeled text segments. The Vaidurya engine is fully functional and is used within the DeGeL system. We describe the Vaidurya ontological and algorithmic framework; we also briefly summarize the results of a detailed evaluation in the clinical-guideline domain, demonstrating that both concept-based and context-sensitive ontology-independent search are highly feasible and significantly improve on free text search retrieval performance. We conclude by analyzing the limitations and advantages of the approach, and the steps that we have started to take to extend it based on user feedback. [Copyright &y& Elsevier]

Summary: Objective: Manual classification of free-text documents within a predefined hierarchy, commonly required in the medical domain, is highly time consuming task. We present an approach based on supervised learning to automate the classification of clinical guidelines into predefined hierarchical conceptual categories. Methods and material: Given a set of hierarchically categorized documents in the training stage the learning algorithm exploits the hierarchical structure of the concepts in order to overcome the low number of training examples. The classification task is thus decomposed into a continuous decision process, unlike searching within a decision tree, which follows the concept hierarchy and makes a single decision at each node on the path, multiple paths can be chosen. Classification is based on applying a similarity function at each concept. Several evaluation measures were used, based on the intended use of the hierarchy. In addition, conservative and aggressive stop-criterion strategies for stopping the search through the concept hierarchy were formulated. An evaluation of the approach, including several training methods and multiple evaluation measures, has been performed using a training set of 1136 guidelines from the National Guideline Clearing House set. Results: Based on a test collection consisting of 1038 clinical practice guidelines (CPGs) classified along two hierarchies, of roughly 5000 concepts, in which each CPG was classified by a mean of 10 concepts, a variable precision was observed from 44% to 60% depending on the settings of the training methods. Conclusion: These results demonstrate the feasibility of the approach, especially when considering the low ratio of guidelines to classification indices (concepts) in the evaluation data set used here. [Copyright &y& Elsevier]