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 multi-touch surfaces are discussed. The aim was to explore the extent to which our design choices facilitate teacher-learner dialogue and assist the management of classroom activity.
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.
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.
The use of human fingers as an object selection and manipulation tool has raised significant challenges when interacting with direct-touch tabletop displays. This is particularly an issue when manipulating remote objects in 3D environments as finger presses can obscure objects at a distance that are rendered very small. Techniques to support remote manipulation either provide absolute mappings between finger presses and object transformation or rely on tools that support relative mappings to selected objects. This paper explores techniques to manipulate remote 3D objects on direct-touch tabletops using absolute and relative mapping modes. A user study was conducted to compare absolute and relative mappings in support of a rotation task. Overall results did not show a statistically significant difference between these two mapping modes on both task completion time and the number of touches. However, the absolute mapping mode was found to be less efficient than the relative mapping mode when rotating a small object. Also participants preferred relative mapping for small objects. Four mapping techniques were then compared for perceived ease of use and learnability. Touchpad, voodoo doll and telescope techniques were found to be comparable for manipulating remote objects in a 3D scene. A flying camera technique was considered too complex and required increased effort by participants. Participants preferred an absolute mapping technique augmented to support small object manipulation, e.g. the voodoo doll technique.
Abstract:Given the enormous amount of unstructured texts available on the Web, there has been an emerging need to increase discoverability of and accessibility to these texts. One of the proposed solutions is to annotate texts with information extracted from background knowledge. Wikipedia, the free encyclopedia, has been recently exploited as a background knowledge to annotate text with complementary information. Given any piece of text, the main challenge is how to determine the most relevant information from Wikipedia with the least effort and time. While Wikipedia-based annotation has mainly targeted the English and Latin versions of Wikipedia, little effort has been devoted to annotate Arabic text using the Arabic version of Wikipedia. In addition, the annotation of short text presents further challenges due to the inability to apply statistical or machine learning techniques that are commonly used with long text. This work proposes an approach for automatic linking of Arabic short texts to articles drawn from Wikipedia. It reports on the several challenges associated with the design and implementation of the linking approach including the processing of the Wikipedia's enormous content, the mapping of texts to Wikipedia articles, the problem of article disambiguation, and the time efficiency. The proposed approach was tested on a dataset of 100 short texts gathered from online Arabic articles. The annotations generated by the approach were compared with the annotations generated by two human subjects. The approach achieved 71.79% accuracy, 74.70% average precision, and 82.63 % average recall. A thorough analysis and discussion of the evaluation results are also presented to address the limitations, strengths as well as recommendations for future improvements.
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