Nowadays, journalism is facilitated by the existence of large amounts of digital data sources, including many Open Data ones. Such data sources are extremely heterogeneous, ranging from highly struc-tured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to de ne and deploy custom extract-transform-load work ows. These are di cult to set up not only for arbitrary heterogeneous inputs , but also given that users may want to add (or remove) datasets to (from) the corpus. We describe a complete approach for integrating dynamic sets of heterogeneous data sources along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments.

Data Science Fails – There's No Such Thing as a Free Lunch While this latest DSC podcast isn't about sandwiches, it is related to lunch, specifically the no free lunch theorem. In short, the theorem states that no algorithm can be equally good at learning everything, which means that you can't know in advance which algorithm will work best on your data. Data Science Fails – There's No Such Thing as a Free Lunch While this latest DSC podcast isn't about sandwiches, it is related to lunch, specifically the no free lunch theorem. In short, the theorem states that no algorithm can be equally good at learning everything, which means that you can't know in advance which algorithm will work best on your data.

The search based on Raw Data File is brute-force search which compares the distances between query vectors and origin vectors, and computes the nearest k vectors. Search efficiency can be greatly increased if the search is based on Index File where vectors are indexed. Building index requires additional disk space and is usually time-consuming. So what are the differences between Raw Data Files and Index Files? To put it simple, Raw Data File records every single vector together with their unique ID while Index File records vector clustering results such as index type, cluster centroids, and vectors in each cluster.

Elasticsearch is a feature-rich, open-source search-engine built on top of Apache Lucene, one of the most important full-text search engines on the market. Elasticsearch is best known for the expansive and versatile REST API experience it provides, including efficient wrappers for full-text search, sorting and aggregation tasks, making it a lot easier to implement such capabilities in existing backends without the need for complex re-engineering. Ever since its introduction in 2010, Elasticsearch gained a lot of traction in the software engineering domain and by 2016 it became the most popular enterprise search-engine software stack according to DBMS knowledge base DB-engines, surpassing the industry-standard Apache Solr (which is also built on top of Lucene). One of the things that makes Elasticsearch so popular is the ecosystem it generated. Engineers across the world developed open-source Elasticsearch integrations and extensions, and many of these projects were absorbed by Elastic (the company behind the Elasticsearch project) as part of their stack.

To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, \textbf{COVID-KG} to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures and knowledge subgraphs as evidence. All of the data, KGs, reports, resources and shared services are publicly available.

TF-IDF, short for Term Frequency–Inverse Document Frequency, is a numerical statistic that is intended to reflect how important a word is to a document, in a collection or Corpus(Paragraph).It is often used as a Weighing Factor in searches of information retrieval, Text Mining, and User Modelling. The TF-IDF value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general. TF-IDF is much more preferred than Bag-Of-Words, in which every word, is represented as 1 or 0, every time it gets appeared in each Sentence, while, in TF-IDF, gives weightage to each Word separately, which in turn defines the importance of each word than others. Let's Consider these Three sentences: Let's assume a word "Good", in sentence 1, as we know, TF(t) (Number of times term t appears in a document) / (Total number of terms in the document). So, Number of times the word "Good" appears in Sentence 1 is, 1 Time, and the Total number of times the word "Good", appears in all three Sentences is 3 times, so the TF(Term Frequency) value of word "Good" is, TF("Good") 1/3 0.333.

In November 2017, Sridhar Ramaswamy--the head of Google's $95 billion advertising arm--left the company after a scandal concerning advertisements for major corporations found on YouTube videos that put children in questionable situations. Ramaswamy told The New York Times that shortly after that incident, he decided that he needed to do something different in his life--because "an ad-supported model had limitations." This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. Ramaswamy's startup company, Neeva, is that "something different"--and though it, too, is a search engine, it seeks to sidestep some of Google's problems by avoiding the ads altogether. Ramaswamy says that the new engine won't show ads and won't collect or profit from user data--instead, it will charge its users a subscription fee.

We have been looking into Facebook's open-sourced conversational offering, Blender Bot. In Part-1 we went over in detail about the DataSets used in the pre-training and fine-tuning of it and the failure cases as well as limitations of Blender. And in Part-2 we studied the more generic problem setting of "Multi-Sentence Scoring", the Transformer architectures used for such a task and learnt about the Poly-Encoders in particular -- which will be used to provide the encoder representations in Blender. In this 3rd and final part, we return from our respite with Poly-Encoders, back to Blender. We shall go over the different Model Architectures, their respective training objectives, the Evaluation methods and performance of Blender in comparison to Meena.

Privacy-focused search engine DuckDuckGo has reported that its service is not working in India. "To our users in India: We've received many reports our search engine is unreachable by much of India right now and have confirmed it is not due to us," the company tweeted. "We're actively talking to Internet providers to get to the bottom of it ASAP. Thank you for your patience." It is unclear why DuckDuckGo would be unavailable in the country.

The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to the lack of information and knowledge. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time. Our system leverages both supervised and unsupervised approaches to provide informative answers using DenSPI (Seo et al., 2019) and BEST (Lee et al., 2016). Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on facts about COVID-19. We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19 but for future pandemics as well.