Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Computational Engineering
Title: @spam: the underground on 140 characters or less
Abstract: In this work we present a characterization of spam on Twitter. We find that 8% of 25 million URLs posted to the site point to phishing, malware, and scams listed on popular blacklists. We analyze the accounts that send spam and find evidence that it originates from previously legitimate accounts that have been compromised and are now being puppeteered by spammers. Using clickthrough data, we analyze spammers' use of features unique to Twitter and the degree that they affect the success of spam. We find that Twitter is a highly successful platform for coercing users to visit spam pages, with a clickthrough rate of 0.13%, compared to much lower rates previously reported for email spam. We group spam URLs into campaigns and identify trends that uniquely distinguish phishing, malware, and spam, to gain an insight into the underlying techniques used to attract users. Given the absence of spam filtering on Twitter, we examine whether the use of URL blacklists would help to significantly stem the spread of Twitter spam. Our results indicate that blacklists are too slow at identifying new threats, allowing more than 90% of visitors to view a page before it becomes blacklisted. We also find that even if blacklist delays were reduced, the use by spammers of URL shortening services for obfuscation negates the potential gains unless tools that use blacklists develop more sophisticated spam filtering.
Extracted statement — has research problem: Characterization of spam on Twitter
Research field: Computational Linguistics
Title: BioNLP Shared Task 2011 – Bacteria Gene Interactions and Renaming
Abstract: We present two related tasks of the BioNLP Shared Tasks 2011: Bacteria Gene Renaming (Rename) and Bacteria Gene Interactions (GI). We detail the objectives, the corpus specification, the evaluation metrics, and we summarize the participants' results. Both issued from PubMed scientific literature abstracts, the Rename task aims at extracting gene name synonyms, and the GI task aims at extracting genic interaction events, mainly about gene transcriptional regulations in bacteria.
Extracted statement — has research problem: Bacteria gene renaming
Research field: Computer and Systems Architecture
Title: The Ontology-based Business Architecture Engineering Framework
Abstract: Business architecture became a well-known tool for business transformations. According to a recent study by Forrester, 50 percent of the companies polled claimed to have an active business architecture initiative, whereas 20 percent were planning to engage in business architecture work in the near future. However, despite the high interest in BA, there is not yet a common understanding of the main concepts. There is a lack for the business architecture framework which provides a complete metamodel, suggests methodology for business architecture development and enables tool support for it. The ORG- Master framework is designed to solve this problem using the ontology as a core of the metamodel. This paper describes the ORG-Master framework, its implementation and dissemination.
Extracted statement — has research problem: Business architecture development
Research field: Computer and Systems Architecture
Title: Towards a Large Corpus of Richly Annotated Web Tables for Knowledge Base Population
Abstract: Web Table Understanding in the context of Knowledge Base Population and the Semantic Web is the task of i) linking the content of tables retrieved from the Web to an RDF knowledge base, ii) of building hypotheses about the tables' structures and contents, iii) of extracting novel information from these tables, and iv) of adding this new information to a knowledge base. Knowledge Base Population has gained more and more interest in the last years due to the increased demand in large knowledge graphs which became relevant for Artificial Intelligence applications such as Question Answering and Semantic Search. In this paper we describe a set of basic tasks which are relevant for Web Table Understanding in the mentioned context. These tasks incrementally enrich a table with hypotheses about the table's content. In doing so, in the case of multiple interpretations, selecting one interpretation and thus deciding against other interpretations is avoided as much as possible. By postponing these decision, we enable table understanding approaches to decide by themselves, thus increasing the usability of the annotated table data. We present statistics from analyzing and annotating 1.000.000 tables from the Web Table Corpus 2015 and make this dataset as well as our code available online.
Extracted statement — has research problem: Web Table Understanding
Research field: Computer Engineering
Title: Contribution of big data in E-learning. A methodology to process academic data from heterogeneous sources
Abstract: Big Data covers a wide spectrum of technologies, which tends to support the processing of big amounts of heterogeneous data. The paper identifies the powerful benefits and the application areas of Big Data in the on-line education context. Considering the boom of academic services on-line, and the free access to the educative content, a great amount of data is being generated in the formal educational field as well as in less formal contexts. In this sense, Big Data can help stakeholders, involved in education decision making, to reach the objective of improving the quality of education and the learning outcomes. In this paper, a methodology is proposed to process big amounts of data coming from the educational field. The current study ends with a specific case study where the data of a well-known Ecuadorian institution that has more than 80 branches is analyzed.
Extracted statement — has research problem: Big Data
Research field: Computer Engineering
Title: LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
Abstract: Entity linking (EL) is the task of disambiguating mentions appearing in text by linking them to entities in a knowledge graph, a crucial task for text understanding, question answering or conversational systems. In the special case of short-text EL, which poses additional challenges due to limited context, prior approaches have reached good performance by employing heuristics-based methods or purely neural approaches. Here, we take a different, neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. Even though constrained to use rules, we show that we reach competitive or better performance with SoTA black-box neural approaches. Furthermore, our framework has the benefits of extensibility and transferability. We show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even with scores resulting from previous EL methods, thus improving on such methods. As an example of improvement, on the LC-QuAD-1.0 dataset, we show more than 3% increase in F1 score relative to previous SoTA. Finally, we show that the inductive bias offered by using logic results in a set of learned rules that transfers from one dataset to another, sometimes without finetuning, while still having high accuracy.
Extracted statement — has research problem: Entity Linking
Research field: Computer Engineering
Title: Exploring Hate Speech Detection in Multimodal Publications
Abstract: In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research.
Extracted statement — has research problem: hate speech detection
Research field: Computer Engineering
Title: Harvesting Information from Captions for Weakly Supervised Semantic Segmentation
Abstract: Since acquiring pixel-wise annotations for training convolutional neural networks for semantic image segmentation is time-consuming, weakly supervised approaches that only require class tags have been proposed. In this work, we propose another form of supervision, namely image captions as they can be found on the Internet. These captions have two advantages. They do not require additional curation as it is the case for the clean class tags used by current weakly supervised approaches and they provide textual context for the classes present in an image. To leverage such textual context, we deploy a multi-modal network that learns a joint embedding of the visual representation of the image and the textual representation of the caption. The network estimates text activation maps (TAMs) for class names as well as compound concepts, i.e. combinations of nouns and their attributes. The TAMs of compound concepts describing classes of interest substantially improve the quality of the estimated class activation maps which are then used to train a network for semantic segmentation. We evaluate our method on the COCO dataset where it achieves state of the art results for weakly supervised image segmentation.
Extracted statement — has research problem: image segmentation
Research field: Computer Engineering
Title: Linked open knowledge organization systems
Abstract: The Web is the most used Internet's service to create and share information. In large information collections, Knowledge Organization plays a key role in order to classify and to find valuable information. Likewise, Linked Open Data is a powerful approach for linking different Web datasets. Today, several Knowledge Organization Systems are published by using the design criteria of linked data, it facilitates the automatic processing of them. In this paper, we address the issue of traversing open Knowledge Organization Systems, considering difficulties associated with their dynamics and size. To fill this issue, we propose a method to identify irrelevant nodes on an open graph, thus reducing the time and the scope of the graph path and maximizing the possibilities of finding more relevant results. The approach for graph reduction is independent of the domain or task for which the open system will be used. The preliminary results of the proof of concept lead us to think that the method can be effective when the coverage of the concept of interest increases.
Extracted statement — has research problem: Knowledge Organization
Research field: Computer Engineering
Title: Application of ontologies in higher education: A systematic mapping study
Abstract: Universities and higher education institutes continually create knowledge. Hence it is necessary to keep a record of the academic and administrative information generated. Considering the vast amount of information managed by higher education institutions and the diversity of heterogeneous systems that can coexist within the same institution, it becomes necessary to use technologies for knowledge representation. Ontologies facilitate access to knowledge allowing the adequate exchange of information between people and between heterogeneous systems. This paper aims to identify existing research on the use and application of ontologies in higher education. From a set of 2792 papers, a study based on systematic mapping was conducted. A total of 52 research papers were reviewed and analyzed. Our results contribute key findings regarding how ontologies are used in higher education institutes, what technologies and tools are applied for the development of ontologies and what are the main vocabularies reused in the application of ontologies.
Extracted statement — has research problem: Knowledge Representation
Research field: Computer Engineering
Title: The use of ontologies for syllabus representation
Abstract: A syllabus is an important document for teachers, students, and institutions. It represents what and how a course will be conducted. Some authors have researched regarding the components for writing a good syllabus. However, there is not a standard format universally accepted by all educators. Even inside the same university, there are syllabuses written in different type of files like PDF, DOC, HTML, for instance. These kind of files are easily readable by humans but it is not the same by machines. On the other hand, ontologies are technologies of knowledge representation that allow setting information understandable for both humans and machines. In this paper, we present a literature review regarding the use of ontologies for syllabus representation. The objective of this paper is to know the use of ontologies to represent a syllabus semantically and to determine their score according to the five-stars of Linked Data vocabulary use scale. Our results show that some researchers have used ontologies for many purposes, widely concerning with learning objectives. Nevertheless, the ontologies created by the authors do not fulfill with the five-stars rating and do not have all components of a well-suited syllabus.
Extracted statement — has research problem: Knowledge Representation
Research field: Computer Engineering
Title: Towards a learning analytics approach for supporting discovery and reuse of OER an approach based on Social Networks Analysis and Linked Open Data
Abstract: The OER movement poses challenges inherent to discovering and reuse digital educational materials from highly heterogeneous and distributed digital repositories. Search engines on today's Web of documents are based on keyword queries. Search engines don't provide a sufficiently comprehensive solution to answer a query that permits personalization of open educational materials. To find OER on the Web today, users must first be well informed of which OER repositories potentially contain the data they want and what data model describes these datasets, before using this information to create structured queries. Learning analytics requires not only to retrieve the useful information and knowledge about educational resources, learning processes and relations among learning agents, but also to transform the data gathered in actionable e interoperable information. Linked Data is considered as one of the most effective alternatives for creating global shared information spaces, it has become an interesting approach for discovering and enriching open educational resources data, as well as achieving semantic interoperability and re-use between multiple OER repositories. In this work, an approach based on Semantic Web technologies, the Linked Data guidelines, and Social Network Analysis methods are proposed as a fundamental way to describing, analyzing and visualizing knowledge sharing on OER initiatives.
Extracted statement — has research problem: Learning Analytics
Research field: Computer Engineering
Title: EMadrid project: MOOCs and learning analytics
Abstract: Both, MOOCs and learning analytics, are two emergent topics in the field of educational technology. This paper shows the main contributions of the eMadrid network in these two topics during the last years (2014-2016), as well as the planned future works in the network. The contributions in the field of the MOOCs include the design and authoring of materials, the improvement of the peer review process or experiences about teaching these courses and institutional adoption. The contributions in the field of learning analytics include the inference of higher level information, the development of dashboards, the evaluation of the learning process, or the prediction and clustering.
Extracted statement — has research problem: Learning Analytics
Research field: Computer Engineering
Title: Application of data anonymization in Learning Analytics
Abstract: Thanks to the proliferation of academic services on the Web and the opening of educational content, today, students can access a large number of free learning resources, and interact with value-added services. In this context, Learning Analytics can be carried out on a large scale thanks to the proliferation of open practices that promote the sharing of datasets. However, the opening or sharing of data managed through platforms and educational services, without considering the protection of users' sensitive data, could cause some privacy issues. Data anonymization is a strategy that should be adopted during lifecycle of data processing to reduce security risks. In this research, we try to characterize how much and how the anonymization techniques have been used in learning analytics proposals. From an initial exploration made in the Scopus database, we found that less than 6% of the papers focused on LA have also covered the privacy issue. Finally, through a specific case, we applied data anonymization and learning analytics to demonstrate that both technique can be integrated, in a reliably and effectively way, to support decision making in educational institutions.
Extracted statement — has research problem: Learning Analytics
Research field: Computer Engineering
Title: A rating system that open-data repositories must satisfy to be considered OER: Reusing open data resources in teaching
Abstract: The re-use of digital content is an essential part of the knowledge-based economy. The online accessibility of open materials will make possible for teachers, students and self-learners to use them for leisure, studies or work. Recent studies have focused on the use or reuse of digital resources with specific reference to the open data field. Moreover, open data can be reused — for both commercial and non-commercial purposes — for uses such as developing learning and educational content, with full respect for copyright and related rights. This work presents a rating system for open data as OER is proposed. This rating system present a framework to search, download and re-use digital resources by teachers and students. The rating system proposed is built from the ground upwards on Open data principles and Semantic Web technologies. By following open data best practices and Linked Data principles, open data initiative ensures that data hosted can be fully connected into a Web of Linked Data. This work aims at sharing good practices for administrative/academics/researchers on gathering and disseminating good quality data, using interoperable standards and overcoming legal obstacles. In this way, open data becomes a tool for teaching and educational environments to improve engagement and student learning. Designing an open data repository that manages and shares information of different catalogues and an evaluation tool to OER will allow teachers and students to increase educational contents and to improve relationship between open data initiatives and education context.
Extracted statement — has research problem: Open Data
Research field: Computer Engineering
Title: OCW-S: Enablers for building sustainable open education evolving OCW and MOOC
Abstract: MOOCs (Massive Open Online Courses) promote mass education through collaboration scenarios between participants. The purpose of this paper is to analyze the characteristics of MOOCs that can be incorporated into environments such as OpenCourseWare. We develop a study on how the concept of OCW evolves nowadays: focused on the distribution of open educational resources, towards a concept of collaborative open massive training (as with the MOOC), that not all current learning platforms provide. This new generation of social OCW will be called OCW-S.
Extracted statement — has research problem: Open Education
Research field: Computer Engineering
Title: Open educational practices and resources based on social software, UTPL experience
Abstract: Open Educational Resources (OER) are a direct reaction to knowledge privatization; they foment their exchange to the entire world with the aim of increase the human intellectual capacity.In this document, we describe the committment of Universidad Técnica Particular de Loja (UTPL), Ecuador, in the promotion of open educational practices and resources and their impact in society and knowledge economy through the use of Social Software.
Extracted statement — has research problem: Open Education
Research field: Computer Engineering
Title: Impact of open educational resources in higher education institutions in spain and latin americas through social network analysis
Abstract: The modernization of the European education system is allowing undertake new missions such as university responsibility. Higher education institutions have felt the need to open and provide educational resources. OpenCourseWare (OCW) and Open Educational Resources (OER) in general contribute to the dissemination of knowledge and public construction, providing a social good. Openness also means sharing, reuse information and create content in an open environment to improve and maintain the quality of education, at the same time provide an advertising medium for higher education institutions. OpenCourseWare Consortium himself and other educational organizations stress the importance of taking measurement and evaluation programs for an institution OCW. Two main reasons are argued:  Allow monitoring the usefulness and usability of Open Educational Resources (OER) and the efficiency of the publishing process, helping to identify and implement improvements that may be relevant over time.  Measure the use and impact of the parties involved in the OCW site helps ensure their commitment. This paper the authors evaluate social network analysis like a technique to measure the impact of open educational resources, and shows the results of applied this kind analysis in OpenCourseWare Spanish and Latin American, trying to tackle the above problems by extending the impact of resource materials in the new innovative teaching strategies and mission of university social responsibility providing updated information on the impact of OCW materials, and showing the true potential inherent in the current OCW repositories in Latin American universities. To evaluate the utility of Social Network Analysis in open educational resources, different social networks were built, using the explicit relationships between different participants of OCW initiatives, e.g. co-authorship, to show the current state of OCW resources. And through the implicit relationships, e.g. tagging, to assess the potential of OCW. To measure the impact of OCW, the social relationships, drawing from the information published by universities of Spain and Latin American, between OCW actors are described and assessed using social networks analysis techniques and metrics, the results obtained let to: present a current state of OCW in Latin America, know the informal organization behind the OCW initiatives, the folksonomies arise from using tags to describe courses, and potential collaborative networks between: universities, professors linked to production of OCW.
Extracted statement — has research problem: Open Education
Research field: Computer Engineering
Title: Open educational practices and resources based on social software: UTPL experience
Abstract: Open Educational Resources (OER) are a direct reaction to knowledge privatization; they foment their exchange to the entire world with the aim of increase the human intellectual capacity.In this document, we describe the committment of Universidad Técnica Particular de Loja (UTPL), Ecuador, in the promotion of open educational practices and resources and their impact in society and knowledge economy through the use of Social Software.
Extracted statement — has research problem: Open Education