8 research outputs found
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks
Existing question answering methods infer answers either from a knowledge
base or from raw text. While knowledge base (KB) methods are good at answering
compositional questions, their performance is often affected by the
incompleteness of the KB. Au contraire, web text contains millions of facts
that are absent in the KB, however in an unstructured form. {\it Universal
schema} can support reasoning on the union of both structured KBs and
unstructured text by aligning them in a common embedded space. In this paper we
extend universal schema to natural language question answering, employing
\emph{memory networks} to attend to the large body of facts in the combination
of text and KB. Our models can be trained in an end-to-end fashion on
question-answer pairs. Evaluation results on \spades fill-in-the-blank question
answering dataset show that exploiting universal schema for question answering
is better than using either a KB or text alone. This model also outperforms the
current state-of-the-art by 8.5 points.\footnote{Code and data available
in \url{https://rajarshd.github.io/TextKBQA}}Comment: ACL 2017 (short
Knowledge Graph based Question and Answer System for Cosmetic Domain
With the development of E-commerce, the requirements of customers for products become more detailed, and the workload of customer service consultants will increase massively. However, the manufacturer is not obliged to provide specific product ingredients on the website. Therefore, it is necessary to construct a KBQA system to relieve the pressure of online customer service and effectively help customers to find suitable skincare production. For the cosmetic filed, the different basic cosmetics may have varied effects depending on its ingredients. In this paper, we utilize CosDNA website and online cosmetic websites to construct a cosmetic product knowledge graph to broaden the relationship between cosmetics, ingredients, skin type, and effects. Besides, we build the question answering system based on the cosmetic knowledge graph to allow users to understand product details directly and make the decision quickly
A Corpus for Hybrid Question Answering Systems
International audienceQuestion answering has been the focus of a lot of researches and evaluation campaigns, either for text-based systems (TREC and CLEF evaluation campaigns for example), or for knowledge-based systems (QALD, BioASQ). Few systems have effectively combined both types of resources and methods in order to exploit the fruitful- ness of merging the two kinds of information repositories. The only evaluation QA track that focuses on hybrid QA is QALD since 2014. As it is a recent task, few annotated data are available (around 150 questions). In this paper, we present a question answering dataset that was constructed to develop and evaluate hybrid question an- swering systems. In order to create this corpus, we collected several textual corpora and augmented them with entities and relations of a knowledge base by retrieving paths in the knowledge base which allow to answer the questions. The resulting corpus contains 4300 question-answer pairs and 1600 have a true link with DBpedia
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
We study open-domain question answering with structured, unstructured and
semi-structured knowledge sources, including text, tables, lists and knowledge
bases. Departing from prior work, we propose a unifying approach that
homogenizes all sources by reducing them to text and applies the
retriever-reader model which has so far been limited to text sources only. Our
approach greatly improves the results on knowledge-base QA tasks by 11 points,
compared to latest graph-based methods. More importantly, we demonstrate that
our unified knowledge (UniK-QA) model is a simple and yet effective way to
combine heterogeneous sources of knowledge, advancing the state-of-the-art
results on two popular question answering benchmarks, NaturalQuestions and
WebQuestions, by 3.5 and 2.6 points, respectively
Systematic review of question answering over knowledge bases
Over the years, a growing number of semantic data repositories have been made available on the web. However, this has created new challenges in exploiting these resources efficiently. Querying services require knowledge beyond the typical userâs expertise, which is a critical issue in adopting semantic information solutions. Several proposals to overcome this dif- ficulty have suggested using question answering (QA) systems to provide userâfriendly interfaces and allow natural language use. Because question answering over knowledge bases (KBQAs) is a very active research topic, a comprehensive view of the field is essential. The purpose of this study was to conduct a systematic review of methods and systems for KBQAs to identify their main advantages and limitations. The inclusion criteria rationale was English fullâtext articles published since 2015 on methods and systems for KBQAs.info:eu-repo/semantics/publishedVersio