1,720,999 research outputs found
Towards a representation of temporal data in archival records: Use cases and requirements
Get PDFArchival records are essential sources of information for historians and digital humanists to understand history. For modern information systems they are often analysed and integrated into Knowledge Graphs for better access, interoperability and re-use. However, due to restrictions of the representation of RDF predicates temporal data within archival records is a challenge to model. This position paper explains requirements for modeling temporal data in archival records based on running research projects in which archival records are analysed and integrated in Knowledge Graphs for research and exploration
Functional models and extending strategies for ecological networks
Get PDFComplex network analysis is rising as an essential tool to understand properties of ecological landscape networks, and as an aid to land management. The most common methods to build graph models of ecological networks are based on representing functional connectivity with respect to a target species. This has provided good results, but the lack of a model able to capture general properties of the network may be seen as a shortcoming when the activity involves the proposal for modifications in land use. Similarity scores, calculated between nature protection areas, may act as a building block for a graph model intended to carry a higher degree of generality. The present work compares several design choices for similarity-based graphs, in order to determine which is most suitable for use in land management
Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections
No full textMoving towards the next generation of personalized learning environments requires intelligent approaches powered by analytics for advanced learning contexts with enriched digital content. Micro-Learning through Massive Open Online Courses is riding the wave of popularity as a novel paradigm for delivering short educational videos in small pre-organized chunks over time, so that learners can get knowledge in a manageable way. However, with the ever-increasing number of videos, it has become challenging to arrange and search them according to specific categories. In this paper, we get around the problem by bridging Learning Analytics and Cognitive Computing to analyze the content of large video collections, going over traditional term-based methods. We propose an efficient and effective approach to automatically classify a collection of educational videos on pre-existing categories which uses (i) a Speech-to-Text tool to get video transcripts, (ii) Natural Language Processing and Cognitive Computing methods to extract semantic concepts and keywords from video transcripts for their representation, and (iii) Apache Spark as Big Data technology for scalability. Several classifiers are trained on the feature vectors extracted by Cognitive Computing tools. Then, we compared our approach with other combinations of state-of-the-art feature types and classifiers over a large-scale dataset we collected from Coursera. Considering the experimental results, we expect our approach can facilitate the development of Learning Analytics tools powered by Cognitive Computing to support content managers on micro-learning video management while improving how learners search videos
DDB-EDM to FaBiO: The case of the German Digital Library
Get PDFCultural heritage portals have the goal of providing users with seamless access to all their resources. This paper introduces initial e_orts for a user-oriented restructuring of the German Digital Library (DDB). At present, cultural heritage objects (CHOs) in the DDB are modeled using an extended version of the Europeana Data Model (DDBEDM), which negatively impacts usability and exploration. These challenges can be addressed by leveraging ontologies, and building a knowledge graph from the DDB's voluminous collection. Towards this goal, an alignment of bibliographic metadata from DDB-EDM to FRBR-Aligned Bibliographic Ontology (FaBiO) is presented
Leveraging cognitive computing for multi-class classification of e-learning videos
No full textWe present our work on developing a supervised multi-label classification system based on automatic content-based exploration of
large sets of video lectures. The system integrates emerging cognitive tools to extract features from video transcripts and text embedded in visual frames, going beyond simple word frequencies. Preliminary results promise an improvement in terms of precision and recall. Moreover, the system is highly-customizable in terms of feature types and classification algorithms to be easily tailored to different contexts and applications. Preliminary results demonstrate the effectiveness, unique capabilities and future challenges of this novel system
Modeling and extending ecological networks using land similarity
Get PDFComplex network analysis is being applied on topological models of ecological networks, to extrapolate their advanced properties and as part of the activity of land management. Commonly employed methods tend to focus on single target species. This is satisfactory for cognitive analysis, but the limited view provided by these models results in a lack of general information needed for land planning. Similarity scores computed for pairs of nature protection areas are proposed as a building block of a general model to address this shortcoming
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Understanding Class Representations : An Intrinsic Evaluation of Zero-Shot Text Classification
Get PDFFrequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance
DeepOntoNLP & X-SENTIMENT 2021: Advances in Semantics and Explainability for NLP: Joint proceedings of the DeepOntoNLP and X-SENTIMENT Workshops
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