EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs

Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or as independent parallel tasks. In this paper, we propose a framework called EARL, which performs entity linking and relation linking as a joint task. EARL implements two different solution strategies for which we provide a comparative analysis in this paper: The first strategy is a formalisation of the joint entity and relation linking tasks as an instance of the Generalised Travelling Salesman Problem (GTSP). In order to be computationally feasible, we employ approximate GTSP solvers. The second strategy uses machine learning in order to exploit the connection density between nodes in the knowledge graph. It relies on three base features and re-ranking steps in order to predict entities and relations. We compare the strategies and evaluate them on a dataset with 5000 questions. Both strategies significantly outperform the current state-of-the-art approaches for entity and relation linking.Comment: International Semantic Web Conference 201

Learning SPARQL Queries from Expected Results

We present LSQ, an algorithm learning SPARQL queries from a subset of expected results. The algorithm leverages grouping, aggregates and inline values of SPARQL 1.1 in order to move most of the complex computations to a SPARQL endpoint. It operates by building and testing hypotheses expressed as SPARQL queries and uses active learning to collect a small number of learning examples from the user. We provide an open-source implementation and an on-line interface to test the algorithm. In the experimental evaluation, we use real queries posed in the past to the official DBpedia SPARQL endpoint, and we show that the algorithm is able to learn them, 82 % of them in less than a minute and asking the user just once

From natural language questions to SPARQL queries: a pattern-based approach

Linked Data knowledge bases are valuable sources of knowledge which give insights, reveal facts about various relationships and provide a large amount of metadata in well-structured form. Although the format of semantic information – namely as RDF(S) – is kept simple by representing each fact as a triple of subject, property and object, the access to the knowledge is only available using SPARQL queries on the data. Therefore, Question Answering (QA) systems provide a user-friendly way to access any type of knowledge base and especially for Linked Data sources to get insight into the semantic information. As RDF(S) knowledge bases are usually structured in the same way and provide per se semantic metadata about the contained information, we provide a novel approach that is independent from the underlying knowledge base. Thus, the main contribution of our proposed approach constitutes the simple replaceability of the underlying knowledge base. The algorithm is based on general question and query patterns and only accesses the knowledge base for the actual query generation and execution. This paper presents the proposed approach and an evaluation in comparison to state-of-the-art Linked Data approaches for challenges of QA systems

QAnswer -Enhanced Entity Matching for Question Answering over Linked Data

Abstract. QAnswer is a question answering system that uses DBpedia as a knowledge base and converts natural language questions into a SPARQL query. In order to improve the match between entities and relations and natural language text, we make use of Wikipedia to extract lexicalizations of the DBpedia entities and then match them with the question. These entities are validated on the ontology, while missing ones can be inferred. The proposed system was tested in the QALD-5 challenge and it obtained a F1 score of 0.30, which placed QAnswer in the second position in the challenge, despite the fact that the system used only a small subset of the properties in DBpedia, due to the long extraction process

Efecto de los estrógenos en las funciones cognitivas: lenguaje

Llamamos Estrógenos a una familia de hormonas de índole sexual que presentan la gran mayoría de reinos de seres vivos. Pertenecen al grupo químico de los esteroides, sustancias orgánicas con núcleo ciclopentanoperhidrofenantreno o Esterano que suman un total de 17 átomos de carbono. Los estrógenos son hormonas sexuales que prevalecen sobre todo en la fisiología femenina, aunque también podemos encontrarlos en la masculina. Son responsables principalmente de la diferenciación sexual en etapas tempranas. Todas las hormonas sexuales surgen a su vez de un Esteroide, el colesterol, y mantienen su facilidad para atravesar la membrana plasmática celular por sus cualidades liposolubles. El siguiente eslabón es el grupo de los Andrógenos donde se integran las hormonas sexuales de prevalencia masculina, siendo los estrógenos el producto de aromatizar estos últimos. Aromatización, por tanto, es el paso de Andrógeno a Estrógeno por acción de la enzima Aromatasa (P450), ganando un carbono en el cambio y sumando un total de 18C por molécula. A cada hormona andrógeno le corresponde su homólogo estrógeno; de esta forma ambos sexos disponen siempre de mecanismos de autorregulación para balancear sus niveles hormonales según su etapa vital y fisiología. En concreto, la diferenciación sexual y los caracteres sexuales secundarios. La escisión entre sexos es fruto del par de cromosomas XX o XY, que regulan a su vez la distribución en tejidos de cada grupo hormonal.Grado en Logopedi

Calculating Error Bars on Inferences from Web Data

Tietzmann e Silva José Antônio. La politique nationale de mobilité urbaine au Brésil. In: Revue Juridique de l'Environnement, numéro spécial, 2015. Les dynamiques urbaines au prisme des sciences humaines. pp. 81-94

Towards Dynamic Composition of Question Answering Pipelines

Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction. DBpedia has been the most prominently used knowledge graph in this setting. QA systems implement a pipeline connecting a sequence of QA components for translating an input question into its corresponding formal query (e.g. SPARQL); this query will be executed over a knowledge graph in order to produce the answer of the question. Recent empirical studies have revealed that albeit overall effective, the performance of QA systems and QA components depends heavily on the features of input questions, and not even the combination of the best performing QA systems or individual QA components retrieves complete and correct answers. Furthermore, these QA systems cannot be easily reused, extended, and results cannot be easily reproduced since the systems are mostly implemented in a monolithic fashion, lack standardised interfaces and are often not open source or available as Web services. All these drawbacks of the state of the art that prevents many of these approaches to be employed in real-world applications. In this thesis, we tackle the problem of QA over knowledge graph and propose a generic approach to promote reusability and build question answering systems in a collaborative effort. Firstly, we define qa vocabulary and Qanary methodology to develop an abstraction level on existing QA systems and components. Qanary relies on qa vocabulary to establish guidelines for semantically describing the knowledge exchange between the components of a QA system. We implement a component-based modular framework called "Qanary Ecosystem" utilising the Qanary methodology to integrate several heterogeneous QA components in a single platform. We further present Qaestro framework that provides an approach to semantically describing question answering components and effectively enumerates QA pipelines based on a QA developer requirements. Qaestro provides all valid combinations of available QA components respecting the input-output requirement of each component to build QA pipelines. Finally, we address the scalability of QA components within a framework and propose a novel approach that chooses the best component per task to automatically build QA pipeline for each input question. We implement this model within FRANKENSTEIN, a framework able to select QA components and compose pipelines. FRANKENSTEIN extends Qanary ecosystem and utilises qa vocabulary for data exchange. It has 29 independent QA components implementing five QA tasks resulting 360 unique QA pipelines. Each approach proposed in this thesis (Qanary methodology, Qaestro, and FRANKENSTEIN) is supported by extensive evaluation to demonstrate their effectiveness. Our contributions target a broader research agenda of offering the QA community an efficient way of applying their research to a research field which is driven by many different fields, consequently requiring a collaborative approach to achieve significant progress in the domain of question answering