Wikidata has grown to a knowledge graph with an impressive size. To date, it contains more than 17 billion triples collecting information about people, places, films, stars, publications, proteins, and many more. On the other side, most of the information on the Web is not published in highly structured data repositories like Wikidata, but rather as unstructured and semi-structured content, more concretely in HTML pages containing text and tables. Finding, monitoring, and organizing this data in a knowledge graph is requiring considerable work from human editors. The volume and complexity of the data make this task difficult and time-consuming. In this work, we present a framework that is able to identify and extract new facts that are published under multiple Web domains so that they can be proposed for validation by Wikidata editors. The framework is relying on question-answering technologies. We take inspiration from ideas that are used to extract facts from textual collections and adapt them to extract facts from Web pages. For achieving this, we demonstrate that language models can be adapted to extract facts not only from textual collections but also from Web pages. By exploiting the information already contained in Wikidata the proposed framework can be trained without the need for any additional learning signals and can extract new facts for a wide range of properties and domains. Following this path, Wikidata can be used as a seed to extract facts on the Web. Our experiments show that we can achieve a mean performance of 84.07 at F1-score. Moreover, our estimations show that we can potentially extract millions of facts that can be proposed for human validation. The goal is to help editors in their daily tasks and contribute to the completion of the Wikidata knowledge graph.

Authors publish documents in a dynamic manner. Their topic of interest and writing style might shift over time. Tasks such as author classification, author identification or link prediction are difficult to solve in such complex data settings. We propose a new representation learning model, DGEA (for Dynamic Gaussian Embedding of Authors), that is more suited to solve these tasks by capturing this temporal evolution. We formulate a general embedding framework: author representation at time t is a Gaussian distribution that leverages pre-trained document vectors, and that depends on the publications observed until t. The representations should retain some form of multi-topic information and temporal smoothness. We propose two models that fit into this framework. The first one, K-DGEA, uses a first order Markov model optimized with an Expectation Maximization Algorithm with Kalman Equations. The second, R-DGEA, makes use of a Recurrent Neural Network to model the time dependence. We evaluate our method on several quantitative tasks: author identification, classification, and co-authorship prediction, on two datasets written in English. In addition, our model is language agnostic since it only requires pre-trained document embeddings. It outperforms existing baselines by up to 18% on an author classification task on a news articles dataset.

Most people need textual or visual interfaces in order to make sense of Semantic Web data. In this paper, we investigate the problem of generating natural language summaries for Semantic Web data using neural networks. Our end-to-end trainable architecture encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We explore a set of different approaches that enable our models to verbalise entities from the input set of triples in the generated text. Our systems are trained and evaluated on two corpora of loosely aligned Wikipedia snippets with triples from DBpedia and Wikidata, with promising results.

Top-k queries over data streams is a well studied problem. There exists numerous systems allowing to process continuous queries over sliding windows. At the opposite, non-append only streams call for ad-hoc solutions, e.g. tailor-made solutions implemented in a mainstream programming language. In the meantime, the Stream API and lambda expressions have been added in Java 8, thus gaining powerful operations for data stream processing. However, the Java Collections Framework does not provide data structures to safely and conveniently support sorted collections of evolving data. In this paper, we demonstrate Upsortable, an annotation-based approach that allows to use existing sorted collections from the standard Java API for dynamic data management. Our approach relies on a combination of pre-compilation abstract syntax tree modifications and runtime analysis of bytecode. Upsortable offers the developer a safe and time-efficient solution for developing top-k queries on data streams while keeping a full compatibility with standard Java.

As more personal and interactive applications are moving to the cloud, modeling the end-user expectations and satisfaction is becoming necessary for any SaaS provider to survive and thrive in today's competitive market. However, most of existing works addressing cloud elasticity management adopt a centralized approach where user preferences are mostly overlooked. Based on evidence from the fields of customer expectation management and psychophysics, in this article we propose a personal user model to represent end-user satisfaction and her expectations. To integrate the end-user into the decision loop we develop multi-agent negotiation architecture in which the end-user model is embodied by a personal agent who negotiates on her behalf. The results of the evaluation process show that automated negotiation provides a useful platform to empower the user choices, fulfill her expectations, and maximize her satisfaction hereby outperforming centralized approaches where the provider acts in a unilateral manner.

The advent of semantic data on the Web requires efficient reasoning systems to infer RDF and OWL data. The linked nature and the huge volume of data entail efficiency and scalability challenges when designing productive inference systems. This paper presents Inferray, an implementation of RDFS, ρdf, and RDFS-Plus inference with improved performance over existing solutions. The main features of Inferray are 1) a storage layout based on vertical partitioning that guarantees sequential access and efficient sort-merge join inference; 2) efficient sorting of pairs of 64-bit integers using ad-hoc optimizations on MSD radix and a custom counting sort; 3) a dedicated temporary storage to perform efficient graph closure computation. Our measurements on synthetic and real-world datasets show improvements over competitors on RDFS-Plus, and up to several orders of magnitude for transitivity closure.

The Semantic Web has gained substantial momentum over the last decade. It contributes to the manifestation of knowledge from data, and leverages implicit knowledge through reasoning algorithms. The main drawbacks of current reasoning methods over ontologies are two-fold: first they struggle to provide scalability for large datasets, and second, the batch processing reasoners who provide the best scalability so far are unable to infer knowledge from evolving data. We contribute to solving these problems by introducing Slider, an efficient incremental reasoner. Slider goes a significant step beyond existing system, including i) performance, by more than a 70% improvement in average compared to the fastest reasoner available to the best of our knowledge, and ii) inferences on streams of semantic data, by using intrinsic features that are themselves streams-oriented. Slider is fragment agnostic and conceived to handle expanding data with a growing background knowledge base. It natively supports pdf and RDFS, and its architecture allows to extend it to more complex fragments with a minimal effort. In this demo a web-based interface allows the users to visualize the internal behaviour of Slider during the inference, to better understand its design and principles.

Microblogging websites such as Twitter produce tremendous amount of data each second. Consequently, real-time recommendation systems require very efficient algorithm to quickly proceed this massive amount of data. In this paper we propose a scalable and extensible way of building content-based user profiles. Scalability refers to the relative complexity of algorithms involved in building the users profiles with respect to state-of-the-art solutions. Extensibility consider avoiding to recompute the model for newcomers. We present a tractable algorithm to build user profiles out of their tweets. Our model is a graph of terms cooccurency, driven by the fact that user sharing similar interests will share similar terms. We then present how this model can be encoded as a binary footprint, hence boosting comparison of users. We provide an empirical study to measure how the distance between users in the hash space differs from distance between users using standard Information Retrieval techniques. This experiment is based on a Twitter dataset we crawled, and represents 25K users and 1 million tweets. Our approach is driven by real-time analysis requirements and is thus oriented on a trade-off between expressivity and efficiency. Experimental results shows that our approach outperforms vector space model by three orders of magnitude, with a precision of 58%.

In this paper we present a Friend Recommender System for micro-blogging. Traditional batch processing of massive amounts of data makes it difficult to provide a near-real time friend recommender system or even a system that can properly scale to millions of users. In order to overcome these issues, we have designed a solution that represents user-generated micro posts as a set of pseudo-cliques. These graphs are assigned a hash value using an original Concept-Sensitive Hash function, a new sub-kind of Locally-Sensitive Hash functions. Finally, since the user profiles are represented as a binary footprint, the pairwise comparison of footprints using the Hamming distance provides scalability to the recommender system. The paper goes with an online application relying on a large Twitter dataset, so that the reader can freely experiment the system.