• Novel Two-Stage Framework: Introducing a novel two-stage approach for enhancing Urdu document retrieval, combining dense feature representation and learning techniques, to address the challenges of low-resource language processing. • Empirical Evaluation on Benchmark Collections: Rigorous evaluation on benchmark Urdu IR collections, showcasing significant performance gains using the proposed framework compared to baseline methods. • Re-ranking with SVMrank: Incorporating a novel feature ensemble technique utilizing SVMrank for document re-ranking, enhancing the retrieval system's precision and ranking effectiveness. Research on document retrieval has mainly focused on high-resource languages like English, with limited attention to low-resource languages such as Urdu . Urdu, the 10th-most spoken language globally, with 230 million speakers, faces significant IR challenges due to its complex script, word variations, and lack of standardization. This study introduces U-RR² (Urdu Document Retrieval & Ranking Framework), a two-stage retrieval framework integrating traditional IR models with advanced feature representation techniques to improve Urdu document retrieval. In the first stage, documents and queries are represented using a combination of TF-IDF and embedding methods such as Word2Vec, FastText, and multilingual BERT (mBERT). After that, multiple retrieval models—Vector Space Model, BM25, DFR, and Jelinek-Mercer smoothing—are evaluated, and the best-performing model is selected for initial retrieval. The second stage refines rankings using SVMrank with a feature ensemble technique for improved relevance. Evaluation on CURE, ROSHNI, and UIR_21 benchmarks demonstrates our framework's effectiveness. Our weighted Word2Vec approach achieves MAP scores of 0.78, 0.81, and 0.80, and P@10 scores of 0.79, 0.82, and 0.81 . The feature ensemble further improves performance, reaching F 1 scores of 0.89, 0.90, and 0.81 , significantly outperforming baselines and establishing a new state-of-the-art for Urdu document retrieval.

Machine Reading Comprehension (MRC) is a key task in Natural Language Understanding that enables automated systems to answer questions based on textual input. While MRC has made substantial progress for high-resource languages, low-resource languages pose significant challenges due to their complex linguistic features. This article presents a comprehensive human-annotated dataset for Urdu MRC, comprising 20,000 question-answer pairs derived from 1,540 articles across seven domains. Unlike previous translation-based datasets, this dataset contains question-answer pairs created through rigorous crowd-sourcing and expert annotation. The dataset encompasses diverse question types, including both answerable and unanswerable questions, with answers ranging from single words to complete sentences, effectively capturing Urdu’s morphological richness and syntactic diversity. To address the limitations of traditional evaluation metrics like Exact Match (EM) and \(F_1\) in assessing Urdu answers, we propose Semantic Match (SM), a metric designed to measure semantic equivalence between predicted and ground-truth answers. Our evaluation demonstrates the dataset’s increased complexity, with state-of-the-art models achieving only 0.82% SM accuracy. Together, the dataset and evaluation metric establish a robust framework for advancing Urdu MRC research, bridging critical gaps in both dataset quality and evaluation methodology.

The link traversal strategies to query Linked Data over WWW can retrieve up-to-date results using a recursive URI lookup process in real-time. The downside of this approach comes with the query patterns having subject unbound (i.e. ?S rdf:type:Class). Such queries fail to start up the traversal process as the RDF pages are subject-centric in nature. Thus, zero-knowledge link traversal leads to the empty query results for these queries. In this paper, the authors analyze a large corpus of real-world SPARQL query logs and identify the Most Frequent Predicates (MFPs) occurring in these queries. The knowledge of these MFPs helps in finding and indexing a limited number of triples from the original data set. Additionally, the authors propose a Hybrid Query Execution (HQE) approach to execute the queries over this index for initial data source selection followed by link traversal process to fetch complete results. The evaluation of HQE on the latest real data benchmarks reveals that it retrieves at least five times more results than the existing approaches.

This research uses breast cancer data from the Surveillance, Epidemiology, and End Results (SEER) dataset's (1973-2010), which contains 684394 records. It is cleaned using several data pre-processing techniques. Survivability predictions are proposed using two different methods. In the first method, 14 variables are used as suggested by Delen et al[1], and in second method 14 variables are reduced to 5 variables (Principal Components) using a statistical technique called Principal Component Analysis (PCA), which captures 98% of total variance. The results of both of the methods propose almost same level of accuracy, thereby reducing the number of variables to be taken into account for the analysis of data.

Performance evaluation of faculty members in a higher education is a serious challenge for management. Several qualitative and quantitative factors are considered leading to targeted professional growth of an organization to evaluate the performance, such as acquiring research grants, research profile, student evaluation, publications, academic responsibilities etc. However, to quantify such information is a difficult task. Usually standard “Key Performance Indicators” (KPIs) are used to evaluate bare level workload of a faculty, however gauging quality always remain a challenge. In this paper, we propose an ontology based faculty performance evaluation system where the ontology is used to conceptualize the qualitative factors of the faculty as well as the set of rules that together make a system. The Semantic Web Rule Language (SWRL) is used to represent rules in ontology and built-in reasoning support help to deduce new knowledge. The presented work has been tested by applying it on real data set of a Public Sector University in Pakistan, and analyzes the strengths and weaknesses of the proposed system. It must be mentioned that the use of ontology makes the system easily modifiable due to the availability of the sophisticated ontology editors and APIs as well as provides complete automatic system.

Guided Personal Learning Environment concept (gPLEc) is a novel PLE-building methodology which seeks to fulfill emergent learning needs of a learner through incorporating teachers' guidance element in learners' PLE-building activity. Using Social Semantic Web (SSW) and Recommender System (RS) technologies the gPLEc integrates teachers' PLE-based learning competencies and learners' social web and interaction history data to generate personalized recommendations for each learner. The results, achieved so far, confirm importance of teachers' guidance element for successful implementation of the PLE vision. The expected outcome of this exploratory research is to gain knowledge to construct personalized elearning systems.

Multimedia has become technically easier to create (e.g. recording lectures) but while users can easily bookmark, search, link to, or tag the WHOLE of a podcast or video recording available on the web they cannot easily find, or associate their notes or resources with, PART of that recording. This paper describes the development of a web based application that makes multimedia web resources (e.g. podcasts) easier to access, search, manage, and exploit for learners, teachers and other users through the creation of notes, bookmarks, tags, links, images and text captions synchronized to any part of the recording.

This paper describes scenarios developed as part of the Ed-Scene project which aims to provide intelligent services to the academic stakeholders (teachers, students, administrators, employers) by semantically managing learning resources in order to provide a value-added semantics layer where semantic annotation, query and reasoning can be carried out to support management requirements in Ed-Scene scenarios, such as teaching module allocation among lecturers, interpretation of studentspsila transcripts in career advice.

No abstract available.