Lionel Messi needs no introduction. Even folks who don't follow football have heard about the brilliance of one of the greatest players to have graced the sport. We have text, tons of hyperlinks, and even an audio clip. The possibilities of putting this into a use case are endless. However, there is a slight problem. This is not an ideal source of data to feed to our machines. Can we find a way to make this text data readable for machines?

Abstract: Replacing static word embeddings with contextualized word representations has yielded significant improvements on many NLP tasks. However, just how contextual are the contextualized representations produced by models such as ELMo and BERT? Are there infinitely many context-specific representations for each word, or are words essentially assigned one of a finite number of word-sense representations? For one, we find that the contextualized representations of all words are not isotropic in any layer of the contextualizing model. While representations of the same word in different contexts still have a greater cosine similarity than those of two different words, this self-similarity is much lower in upper layers.

How the new advances in semantics can help us be better at Machine Learning. Deep learning on graphs is taking more importance by the day. I've been talking about the data fabric in general, and giving some concepts of Machine Learning and Deep Learning in the data fabric. The Data Fabric is the platform that supports all the data in the company. How it's managed, described, combined and universally accessed.

Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems. In real world, knowledge graphs (KGs) are dynamic and evolve over time with addition or deletion of triples. However, most existing models focus on embedding static KGs while neglecting dynamics. To adapt to the changes in a KG, these models need to be re-trained on the whole KG with a high time cost. In this paper, to tackle the aforementioned problem, we propose a new context-aware Dynamic Knowledge Graph Embedding (DKGE) method which supports the embedding learning in an online fashion. DKGE introduces two different representations (i.e., knowledge embedding and contextual element embedding) for each entity and each relation, in the joint modeling of entities and relations as well as their contexts, by employing two attentive graph convolutional networks, a gate strategy, and translation operations. This effectively helps limit the impacts of a KG update in certain regions, not in the entire graph, so that DKGE can rapidly acquire the updated KG embedding by a proposed online learning algorithm. Furthermore, DKGE can also learn KG embedding from scratch. Experiments on the tasks of link prediction and question answering in a dynamic environment demonstrate the effectiveness and efficiency of DKGE.

Lionel Messi needs no introduction. Even folks who don't follow football have heard about the brilliance of one of the greatest players to have graced the sport. We have text, tons of hyperlinks, and even an audio clip. The possibilities of putting this into a use case are endless. However, there is a slight problem. This is not an ideal source of data to feed to our machines.

Stardog really means it when they say you can act as if the walls of the silos are not there. Debuting in version 7 is what they call Virtual Transparency, which appropriately abstracts away from the user, whether that's an application developer, a business analyst, data scientist, or anyone else who needs data, the unnecessary details of where the data is or how it's stored. Data consumers simple write a logical query, i.e. "get all purchases made in the last 30 days in the mid-atlantic, within the Electronics department for more than $100" and Stardog's platform handles the details of accessing the data where it resides and returning a single, coherent, and most important, complete result.

The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Many recent works have demonstrated the benefits of knowledge graph embedding on knowledge graph completion task, such as relation extraction. However, we observe that: 1) existing method just take direct relations between entities into consideration and fails to express high-order structural relationship between entities; 2) these methods just leverage relation triples of KGs while ignoring a large number of attribute triples that encoding rich semantic information. To overcome these limitations, this paper propose a novel knowledge graph embedding method, named KANE, which is inspired by the recent developments of graph convolutional networks (GCN). KANE can capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner under the graph convolutional networks framework. Empirical results on three datasets show that KANE significantly outperforms seven state-of-arts methods. Further analysis verify the efficiency of our method and the benefits brought by the attention mechanism.

Automatic KB completion for commonsense knowledge graphs (e.g., ATOMIC and ConceptNet) poses unique challenges compared to the much studied conventional knowledge bases (e.g., Freebase). Commonsense knowledge graphs use free-form text to represent nodes, resulting in orders of magnitude more nodes compared to conventional KBs (18x more nodes in ATOMIC compared to Freebase (FB15K-237)). Importantly, this implies significantly sparser graph structures - a major challenge for existing KB completion methods that assume densely connected graphs over a relatively smaller set of nodes. In this paper, we present novel KB completion models that can address these challenges by exploiting the structural and semantic context of nodes. Specifically, we investigate two key ideas: (1) learning from local graph structure, using graph convolutional networks and automatic graph densification and (2) transfer learning from pre-trained language models to knowledge graphs for enhanced contextual representation of knowledge. We describe our method to incorporate information from both these sources in a joint model and provide the first empirical results for KB completion on ATOMIC and evaluation with ranking metrics on ConceptNet. Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1.5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency. Further analysis on model predictions shines light on the types of commonsense knowledge that language models capture well.

Web pages indexed by Google (plus gazillion of temporal snapshots) Embedded markup (RDFa, Microdata, Microformats) for annotation of Web pages Supports Web search & interpretation Pushed by Google, Yahoo, Bing et al (schema.org Factual errors, annotation errors (see also [Meusel et al, ESWC2015]) o Ambiguity & coreferences. Relevance: supervised coreference resolution 2.) Quality & redundancy: data fusion through supervised fact classification (SVM, knn, RF, LR, NB), diverse feature set (authority, relevance etc), considering source- (eg PageRank), entity-, & fact-level KnowMore: data fusion on markup 02/10/19 11 1. Relevance: supervised coreference resolution 2.) Quality & redundancy: data fusion through supervised fact classification (SVM, knn, RF, LR, NB), diverse feature set (authority, relevance etc), considering source- (eg PageRank), entity-, & fact-level KnowMore: data fusion on markup 02/10/19 12 1. Rich Context & Coleridge Initiative building (yet another) KG of scholarly resources & datasets 13Stefan Dietze Context/corpus: publications (currently: social sciences, SAGE Publishing) Tasks: I. Extraction/disambiguation of dataset mentions II.

Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-shot adapt to updated, unseen knowledge graphs. This paper proposes a new task about how to apply dynamic knowledge graphs in neural conversation model and presents a novel TV series conversation corpus (DyKgChat) for the task. Also, we propose a preliminary model that selects an output from two networks at each time step: a sequence-to-sequence model (Seq2Seq) and a multi-hop reasoning model, in order to support dynamic knowledge graphs. To benchmark this new task and evaluate the capability of adaptation, we introduce several evaluation metrics and the experiments show that our proposed approach outperforms previous knowledge-grounded conversation models. The proposed corpus and model can motivate the future research directions 1 . 1 Introduction In the chitchat dialogue generation, neural conversation models (Sutskever et al., 2014; Sordoni et al., 2015; Vinyals and Le, 2015) have emerged for its capability to be fully data-driven and end-to-end trained. While the generated responses are often reasonable but general (without useful information), recent work proposed knowledge-grounded models (Eric et al., 2017; Ghazvinine-jad et al., 2018; Zhou et al., 2018b; Qian et al., 2018) to incorporate external facts in an end-to- end fashion without handcrafted slot filling. Effectively combining text and external knowledge1 The data and code are available in https://github. Nonetheless, prior work rarely analyzed the model capability of zero-shot adaptation to dynamic knowledge graphs, where the states/entities and their relations are temporal and evolve as a single time scale process.