Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL

The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.Comment: Journal Pape

KnowledgePuzzle: a browsing tool to adapt the web navigation process to the learner's mental model

This article presents KnowledgePuzzle, a browsing tool for knowledge construction from the web. It aims to adapt the structure of web content to the learner’s information needs regardless of how the web content is originally delivered. Learners are provided with a meta-cognitive space (eg, a concept mapping tool) that enables them to plan navigation paths and visualize the semantic processing of knowledge in their minds. Once the learner’s viewpoint becomes visually represented, it will be transformed to a layer of informative hyperlinks and annotations over previously visited pages. The attached layer causes the web content to be explicitly structured to accommodate the learner’s interests by interlinking and annotating chunks of information that make up the learner’s knowledge. Finally, a hypertext version of the whole knowledge is generated to enable fast and easy reviewing. A discussion about the

Technologies to enhance self-directed learning from hypertext

With the growing popularity of the World Wide Web, materials presented to learners in the form of hypertext have become a major instructional resource. Despite the potential of hypertext to facilitate access to learning materials, self-directed learning from hypertext is often associated with many concerns. Self-directed learners, due to their different viewpoints, may follow different navigation paths, and thus they will have different interactions with knowledge. Therefore, learners can end up being disoriented or cognitively-overloaded due to the potential gap between what they need and what actually exists on the Web. In addition, while a lot of research has gone into supporting the task of finding web resources, less attention has been paid to the task of supporting the interpretation of Web pages. The inability to interpret the content of pages leads learners to interrupt their current browsing activities to seek help from other human resources or explanatory learning materials. Such activity can weaken learner engagement and lower their motivation to learn. This thesis aims to promote self-directed learning from hypertext resources by proposing solutions to the above problems. It first presents Knowledge Puzzle, a tool that proposes a constructivist approach to learn from the Web. Its main contribution to Web-based learning is that self-directed learners will be able to adapt the path of instruction and the structure of hypertext to their way of thinking, regardless of how the Web content is delivered. This can effectively reduce the gap between what they need and what exists on the Web. SWLinker is another system proposed in this thesis with the aim of supporting the interpretation of Web pages using ontology based semantic annotation. It is an extension to the Internet Explorer Web browser that automatically creates a semantic layer of explanatory information and instructional guidance over Web pages. It also aims to break the conventional view of Web browsing as an individual activity by leveraging the notion of ontology-based collaborative browsing. Both of the tools presented in this thesis were evaluated by students within the context of particular learning tasks. The results show that they effectively fulfilled the intended goals by facilitating learning from hypertext without introducing high overheads in terms of usability or browsing efforts

USING LINGUISTIC ANALYSIS TO TRANSLATE ARABIC NATURAL LANGUAGE QUERIES TO SPARQL

ABSTRAC

Assessing Research Data Output Management at the Managerial Levels at Partner PS HEIs

This report presents the findings of a needs assessment survey that was carried out with research managers in four Palestinian Higher Education Institutions (PS HEIs) between December 2016 and February 2017. The four participating institutions include: The Islamic University of Gaza (IUG) Al-Quds Open University (QOU) Birzeit University (BZU) Palestine Technical University-Kadoori (KAD) The survey data will be used to: Identify the size, formats and scopes of research volumes and digital holdings for which each partner PS HEI assumes preservation responsibility. Review the current RDM practices and activities adopted at the institutional level. Review the current situation in PS HEIs as regards IRs, open access publishing and institutional support for RDM. Determine the current shortcomings and future priorities in RDM from the institution's perspective. In general, this survey targeted the administration and management staff who were responsible for, or directly involved, in RDM in the four partner PS HEIs. Since partner PS Universities might have different organizational structures and administrative departments, the selection process of participants from each university could not be the same. Project coordinators at partner PS Universities were asked to choose eligible persons based on the university's structure and pertinent administrative positions. They were urged to select participants from department/units/centres that were in charge of RDM activities such as scientific research, University library, IT unit, etc

AR2SPARQL: An Arabic Natural Language Interface for the Semantic Web

With the growing interest in supporting the Arabic language on the Semantic Web (SW), there is an emerging need to enable Arab users to query ontologies and RDF stores without being challenged with the formal logic of the SW. In the domain of English language, several efforts provided Natural Language (NL) interfaces to enable ordinary users to query ontologies using NL queries. However, none of these efforts were designed to support the Arabic language which has different morphological and semantic structures. As a step towards supporting Arabic Question Answering (QA) on the SW, this work presents AR2SPARQL, a NL interface that takes questions expressed in Arabic and returns answers drawn from an ontology-based knowledge base. The core of AR2SPARQL is the approach we propose to translate Arabic questions into triples which are matched against RDF data to retrieve an answer. The system uses both linguistic and semantic features to resolve ambiguity when matching words to the ontology content. To overcome the limited support for Arabic Natural Language Processing (NLP), the system does not make intensive use of sophisticated linguistic methods. Instead, it relies more on the knowledge defined in the ontology and the grammar rules we define to capture the structures of Arabic questions and to construct an adequate RDF representations. AR2SPARQL has been tested with two different datasets and results have shown that it achieves a good retrieval performance in terms of precision and recall

PalAST: A Cross-Platform Mobile Application for Automated Disk Diffusion Antimicrobial Susceptibility Testing

Antibiotic resistance is the ability of bacteria to resist the effects of antibiotics, making infections more difficult to treat and increasing the risk of complications and death. One way to fight antibiotic resistance is by identifying the most effective antibiotics for treating bacterial infections. This can be done through a laboratory test called AST, which is used to determine the susceptibility of bacteria to antibiotics. However, manual AST has several limitations that include time delay, limited accuracy, limited testing capacity, and subjective interpretation of results. Therefore, there is an emergent need for a more reliable and efficient alternative to manual AST. Recently, few works have tried to automate disk diffusion AST through AI-based solutions and mobile applications. However, these works do not support advanced analysis and interpretation of results, do not present evaluation of detection performance, or are not publicly available to download and use. This work proposes PalAST, a cross-platform mobile application that supports automated disk diffusion AST. The application enables biologists to take AST photos and analyze them in real time with minimal human intervention. It uses image processing and a pre-trained machine learning model to detect antibiotic disks in the agar plate and predict bounding circles for inhibition zones. Then, it provides an interpretation of results including the diameters of the inhibition zones, the labels on the antibiotic disks, and the rating of the bacteria as susceptible, intermediate, or resistant to each antibiotic. PalAST also stores the results of tests, allowing users to access and review past test results. PalAST was tested using a number of real AST photos, and the detection performance was evaluated by using common metrics, i.e. precision, recall, and Intersection over Union. We also used expert evaluation through a questionnaire to assess the usability and ease of use of PalAST

Towards a teacher-centric approach for multi-touch surfaces in classrooms

The potential of tabletops to enable simultaneous interaction and face-to-face collaboration can provide novel learning opportunities. Despite significant research in the area of collaborative learning around tabletops, little attention has been paid to the integration of multi-touch surfaces into classroom layouts and how to employ this technology to facilitate teacher-learner dialogue and teacher-led activities across multi-touch surfaces. While most existing techniques focus on the collaboration between learners, this work aims to gain a better understanding of practical challenges that need to be considered when integrating multi-touch surfaces into classrooms. It presents a multi-touch interaction technique, called TablePortal, which enables teachers to manage and monitor collaborative learning on students' tables. Early observations of using the proposed technique within a novel classroom consisting of networked

Leveraging semantic web technologies to enhance individual and collaborative learning

Despite the popularity of the World Wide Web as a resource of hypertext-based learning materials, web-based learning is often associated with many challenges. One of these challenge is the ability to find learning material that can best match the user interests with minimal effort. This paper presents SWLinker, a distributed system that leverages ontological engineering to enable users to access complementary and in-depth knowledge resources through a standard Web browser. The proposed approach supports real-time interpretation of any web pages existing on the Internet by attaching semantic layers of knowledge chunks. It also enables learners to discover domain terms in a wider context by embedding portals that offer a grand vision of all instructionally-related concepts and sub topics. Learners are still able to query knowledge bases and associate knowledge with what they are currently browsing but