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The design and delivering of platforms for online education is fostering increasingly intense research. Scaling up education online brings new emerging needs related with hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely, as examples. However, with the impressive progress of the data mining and machine learning fields, combined with the large amounts of learning-related data and high-performance computing, it has been possible to gain a deeper understanding of the nature of learning and teaching online. Methods at the analytical and algorithmic levels are constantly being developed and hybrid approaches are receiving an increasing attention. Recent methods are analyzing not only the online traces left by students a posteriori, but also the extent to which this data can be turned into actionable insights and models, to support the above needs in a computationally efficient, adaptive and timely way. In this paper, we present relevant open challenges lying at the intersection between the machine learning and educational communities, that need to be addressed to further develop the field of intelligent systems for online education. Several areas of research in this field are identified, such as data availability and sharing, time-wise and multi-modal data modelling, generalizability, fairness, explainability, interpretability, privacy, and ethics behind models delivered for supporting education. Practical challenges and recommendations for possible research directions are provided for each of them, paving the way for future advances in this field.

Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task.

Scientific knowledge has been traditionally disseminated and preserved through research articles published in journals, conference proceedings, and online archives. However, this article-centric paradigm has been often criticized for not allowing to automatically process, categorize, and reason on this knowledge. An alternative vision is to generate a semantically rich and interlinked description of the content of research publications. In this paper, we present the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 820K research entities. AI-KG includes about 14M RDF triples and 1.2M reified statements extracted from 333K research publications in the field of AI, and describes 5 types of entities (tasks, methods, metrics, materials, others) linked by 27 relations. AI-KG has been designed to support a variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and helping to inform decision-making in funding bodies and research policymakers. AI-KG has been generated by applying an automatic pipeline that extracts entities and relationships using three tools: DyGIE++, Stanford CoreNLP, and the CSO Classifier. It then integrates and filters the resulting triples using a combination of deep learning and semantic technologies in order to produce a high-quality knowledge graph. This pipeline was evaluated on a manually crafted gold standard, yielding competitive results. AI-KG is available under CC BY 4.0 and can be downloaded as a dump or queried via a SPARQL endpoint.

Most Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages. These descriptions of entities provide valuable information that may not be explicitly represented in the structured part of the KG. Based on this fact, some link prediction methods which make use of the information presented in the textual descriptions of entities have been proposed to learn representations of (monolingual) KGs. However, these methods use entity descriptions in only one language and ignore the fact that descriptions given in different languages may provide complementary information and thereby also additional semantics. In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link prediction in KGs will be discussed along with potential solutions to the problem.

Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, the completeness and correctness of the Knowledge Graphs (KGs) are vital. Most of these KGs are mostly created either via an automated information extraction from Wikipedia snapshots or information accumulation provided by the users or using heuristics. However, it has been observed that the type information of these KGs is often noisy, incomplete, and incorrect. To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings. We compare our approach with the current state-of-the-art type prediction method and report on experiments with the KGs.

This poster introduces a new tool named Apollo which analyzes textual information in the geo-tagged twitter streams of trending hashtags using sliding time window. It performs sentiment analysis as well as emotion detection of the opinions of the masses about a trending world wide topic such as #COVID-19, #ClimateChange, #Black-LivesMatter, etc. based on Knowledge Graphs. Apollo currently pro- vides an interactive visualization of the analysis of the trending hashtag #COVID-19.

This poster focuses on a visual analysis of the tweets related to European migration crisis. It uses TweetsKB as a starting point and then formulates a search criteria for extracting tweets by enriching semantic entities and hashtags starting from the seed word \Refugee". It combines European migration statistics with the information obtained by the tweets and provides visual analysis from different perspectives.

It is beyond human capabilities to analyze a huge amount of short text produced on the World Wide Web in the form of search queries, social media platforms, etc. Due to many difficulties underlying short text for automated processing, i.e, sparsity and insufficient context, the traditional text classification approaches cannot easily be applied to short text. This study discusses a Convolutional Neural Network (CNN) based approach for short text classification. Given a short text, the model generates the text representation by leveraging words together with the entities. To validate the effectiveness of the model, several experiments have been conducted on different datasets. The results suggest that the proposed model is capable of performing short text classification with a high accuracy and outperforms the baseline.

Document exploration in archives is often challenging due to the lack of organization in topic-based categories. Moreover, archival records only provide short text which is often insufficient for capturing the semantic. This paper proposes and explores a dataless categorization approach that utilizes word embeddings and TF-IDF to categorize archival documents. Additionally, it introduces a visual approach built on top of the word embeddings to enhance the exploration of data. Preliminary results suggest that current vector representations alone do not provide enough external knowledge to solve this task.

Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations. In addition to the structured information KGs help in facilitating interconnectivity and interoperability between different resources represented in the Linked Data Cloud. KGs have been used in a variety of applications such as entity linking, question answering, recommender systems, etc. However, KG applications suffer from high computational and storage costs. Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information. This paper conducts a survey of KG embedding models which not only consider the structured information contained in the form of entities and relations in a KG but also its unstructured information represented as literals such as text, numerical values, images, etc. Along with a theoretical analysis and comparison of the methods proposed so far for generating KG embeddings with literals, an empirical evaluation of the different methods under identical settings has been performed for the general task of link prediction.

Archives today are publishing their cultural heritage data on the Web for exploration. However, for archive novices the traditional archival structures often are not intuitive and difficult to understand, and thus challenges data access and consumption. To tackle this problem, Linked Stage Graph was developed, a knowledge graph (KG) on the foundation of historical data about the Stuttgart State Theater. The data was made available by the Baden-Wuerttemberg State Archives for the Coding da Vinci hackathon. This demo paper contributes the KG,a SPARQL endpoint, named entity extraction and linking to existing authoritative KGs as well as a dedicated user interface for exploration.

Text classification is an important and challenging task due to its application in various domains such as document organization and news filtering. Several supervised learning approaches have been proposed for text classification. However, most of them require a significant amount of training data. Manually labeling such data can be very time-consuming and costly. To overcome the problem of labeled data, we demonstrate TECNE, a knowledge-based text classification method using network embeddings. The proposed system does not require any labeled training data to classify an arbitrary text. Instead, it relies on the semantic similarity between entities appearing in a given text and a set of predefined categories to determine a category which the given document belongs to.

Vocabularies, Schemas, Ontologies.- Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment.- RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration.- VocBench: a Web Application for Collaborative Development of Multilingual Thesauri.- Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning.- Reasoning.- A Context-Based Semantics for SPARQL Property Paths over the Web.- Distributed and Scalable OWL EL Reasoning.- Large scale rule-based Reasoning using a Laptop.- RDF Digest: Efficient Summarization of RDF/S KBs.- Linked Data.- A Comparison of Data Structures to Manage URIs on the Web of Data.- Heuristics for Fixing Common Errors in Deployed schema.org Microdata.- Semantic Web and Web Science.- LOD-based Disambiguation of Named Entities in @tweets through Context enrichment.- Knowledge Enabled Approach to Predict the Location of Twitter Users.- Semantic Data Management, Big data, Scalability.- A Compact In-Memory Dictionary for RDF data.- Quality Assessment of Linked Datasets using Probabilistic Approximations.- Cooperative Techniques for SPARQL Query Relaxation in RDF Databases.- HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce.- Processing Aggregate Queries in a Federation of SPARQL Endpoints.- A survey of HTTP caching implementations on the open Semantic Web.- Query Execution Optimization for Clients of Triple Pattern Fragments.- Natural Language Processing and Information Retrieval LIME: the Metadata Module for OntoLex.- Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text.- HAWK Hybrid Question Answering using Linked Data.- Machine Learning.- Automating RDF Dataset Transformation and Enrichment.- Semi-supervised Instance Matching Using Boosted Classifiers.- Assigning Semantic Labels to Data Sources.- Inductive Classification through Evidence-based Models and their Ensembles.- Mobile Web, Internet of Things and Semantic Streams.- Standardized and Efficient RDF Encoding for Constrained Embedded Networks.- Services, Web APIs, and the Web of Things SPSC: Efficient Composition of Semantic Services in Unstructured P2P Networks.- Data as a Service: The Semantic Web Redeployed.- Cognition and Semantic Web.- On Coherent Indented Tree Visualization of RDF Graphs.- Gagg: A Graph Aggregation Operator.- FrameBase: Representing N-ary Relations using Semantic Frames.- Human Computation and Crowdsourcing.- Towards hybrid NER: a study of content and crowdsourcing-related performance factors.- Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets.- In-Use Industrial Track.- Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry.- Using Ontologies For Modeling Virtual Reality Scenarios.- Supporting Open Collaboration in Science through Explicit and Linked Semantic Description of Processes.- Crowdmapping Digital Social Innovation with Linked data.- Desperately searching for travel offers? Formulate better queries with some help from Linked Data.- Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing.- From Symptoms to Diseases - Creating the Missing Link.- Using semantic web technologies for enterprise architecture analysis.- PADTUN - Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain.

The growing amount of documents in archives and blogs results in an increasing challenge for curators and authors to tag, present, and recommend their content to the user. refer comprises a set of powerful tools focusing on Named Entity Linking (NEL) which help authors and curators to semi-automatically analyze a platform’s textual content and semantically annotate it based on Linked Open Data. In refer automated NEL is complemented by manual semantic annotation supported by sophisticated autosuggestion of candidate entities, implemented as publicly available Wordpress plugin. In addition, refer visualizes the semantically enriched documents in a novel navigation interface for improved exploration of the entire content across the platform. The efficiency of the presented approach is supported by a qualitative evaluation of the user interfaces.

We present GERBIL, an evaluation framework for semantic entity annotation. The rationale behind our framework is to provide developers, end users and researchers with easy-to-use interfaces that allow for the agile, fine-grained and uniform evaluation of annotation tools on multiple datasets. By these means, we aim to ensure that both tool developers and end users can derive meaningful insights pertaining to the extension, integration and use of annotation applications. In particular, GERBIL provides comparable results to tool developers so as to allow them to easily discover the strengths and weaknesses of their implementations with respect to the state of the art. With the permanent experiment URIs provided by our framework, we ensure the reproducibility and archiving of evaluation results. Moreover, the framework generates data in machine-processable format, allowing for the efficient querying and post-processing of evaluation results. Finally, the tool diagnostics provided by GERBIL allows deriving insights pertaining to the areas in which tools should be further refined, thus allowing developers to create an informed agenda for extensions and end users to detect the right tools for their purposes. GERBIL aims to become a focal point for the state of the art, driving the research agenda of the community by presenting comparable objective evaluation results.

Link Prediction aims at tackling Knowledge Graph incompleteness by inferring new facts based on the existing, already known ones. Nowadays most Link Prediction systems rely on Machine Learning and Deep Learning approaches; this results in inherent opaque models in which assessing the robustness to data biases is not trivial. We define 3 specific types of Sample Selection Bias and estimate their presence in the 5 best-established Link Prediction datasets. We then verify how these biases affect the behaviour of 9 systems representative for every major family of Link Prediction models. We find that these models do indeed learn and incorporate each of the presented biases, with a heavily negative effect on their behaviour. We thus advocate for the creation of novel more robust datasets and of more effective evaluation practices.

euzenat2014c; International audience; Alignments between ontologies may be established through agents holding such ontologies attempting at communicating and taking appropriate action when communication fails. This approach, that we call cultural repair, has the advantage of not assuming that everything should be set correctly before trying to communicate and of being able to overcome failures. We test here the adaptation of this approach to alignment repair, i.e., the improvement of incorrect alignments. For that purpose, we perform a series of experiments in which agents react to mistakes in alignments. The agents only know about their ontologies and alignments with others and they act in a fully decentralised way. We show that cultural repair is able to converge towards successful communication through improving the objective correctness of alignments. The obtained results are on par with a baseline of a priori alignment repair algorithms.

We present two novel semantics for i¾?-calculus. The first allows one to observe on which channel a synchronization is performed, while the second allows concurrent actions, provided that they do not compete for resources. We present both a reduction and a labeled semantics, and show that they induce the same behavioral equivalence. As our main result we show that bisimilarity is a congruence for the concurrent semantics. This important property fails for the standard semantics.