Filling the Gaps Among DBpedia Multilingual Chapters for Question Answering

International audienceTo publish information extracted from multilingual pages of Wikipedia in a structured way, the Semantic Web community has started an effort of internationalization of DBpedia. Multilingual chapters of DBpedia can in fact contain different information with respect to the English version, in particular they provide more specificity on certain topics, or fill information gaps. DBpedia multilingual chapters are well connected through instance interlinking, extracted from Wikipedia. An alignment between properties is also carried out by DBpedia contributors as a mapping from the terms used in Wikipedia to a common ontology, enabling the exploitation of information coming from the multilingual chapters of DBpedia. However, the mapping process is currently incomplete, it is time consuming since it is manually per- formed, and may lead to the introduction of redundant terms in the ontology, as it becomes difficult to navigate through the existing vocabulary. In this paper we propose an approach to automatically extend the existing alignments, and we integrate it in a question answering system over linked data. We report on experiments carried out applying the QAKiS (Question Answering wiKiframework-based) system on the English and French DBpedia chapters, and we show that the use of such approach broadens its coverage

Determinants of quality, latency, and amount of Stack Overflow answers about recent Android APIs.

Stack Overflow is a popular crowdsourced question and answer website for programming-related issues. It is an invaluable resource for software developers; on average, questions posted there get answered in minutes to an hour. Questions about well established topics, e.g., the coercion operator in C++, or the difference between canonical and class names in Java, get asked often in one form or another, and answered very quickly. On the other hand, questions on previously unseen or niche topics take a while to get a good answer. This is particularly the case with questions about current updates to or the introduction of new application programming interfaces (APIs). In a hyper-competitive online market, getting good answers to current programming questions sooner could increase the chances of an app getting released and used. So, can developers anyhow, e.g., hasten the speed to good answers to questions about new APIs? Here, we empirically study Stack Overflow questions pertaining to new Android APIs and their associated answers. We contrast the interest in these questions, their answer quality, and timeliness of their answers to questions about old APIs. We find that Stack Overflow answerers in general prioritize with respect to currentness: questions about new APIs do get more answers, but good quality answers take longer. We also find that incentives in terms of question bounties, if used appropriately, can significantly shorten the time and increase answer quality. Interestingly, no operationalization of bounty amount shows significance in our models. In practice, our findings confirm the value of bounties in enhancing expert participation. In addition, they show that the Stack Overflow style of crowdsourcing, for all its glory in providing answers about established programming knowledge, is less effective with new API questions

FVQA: Fact-based Visual Question Answering

Visual Question Answering (VQA) has attracted a lot of attention in both Computer Vision and Natural Language Processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example. Here we introduce FVQA, a VQA dataset which requires, and supports, much deeper reasoning. FVQA only contains questions which require external information to answer. We thus extend a conventional visual question answering dataset, which contains image-question-answerg triplets, through additional image-question-answer-supporting fact tuples. The supporting fact is represented as a structural triplet, such as . We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting facts.Comment: 16 page