Work out the semantic web search: The cooperative way

In this paper we propose a Cooperative Question Answering System that takes as input natural language queries and is able to return a cooperative answer based on semantic web resources, more specifically DBpedia represented in OWL/RDF as knowledge base and WordNet to build similar questions. Our system resorts to ontologies not only for reasoning but also to find answers and is independent of prior knowledge of the semantic resources by the user. The natural language question is translated into its semantic representation and then answered by consulting the semantics sources of information. The system is able to clarify the problems of ambiguity and helps finding the path to the correct answer. If there are multiple answers to the question posed (or to the similar questions for which DBpedia contains answers), they will be grouped according to their semantic meaning, providing a more cooperative and clarified answer to the user

Sistemas de pergunta-resposta para a Web semântica

O aumento significativo de informação não estruturada e heterogénea, disponível na web, e a necessidade no acesso em tempo real promovem o desenvolvimento de ferramentas, capazes de "dialogar" com o utilizador e que respondam de forma eficaz às suas exigências. O foco desta tese está no desenvolvimento de um sistema cooperativo de Pergunta-Resposta para a Web Semântica. As principais contribuições consistem no enriquecimento do sistema com um Controlador do Discurso que, através da análise e da representação semântica da questão, do tipo de resposta esperada, e da estrutura do discurso, permite fornecer respostas objetivas, informativas e justificadas. Acrescido de capacidades cooperativas, capacidade de interação com o utilizador, o sistema obtém informação necessária para esclarecer ambiguidades e conduzi-lo no caminho para a resposta correta. O desempenho do sistema é avaliado por comparação com outros sistemas semelhantes. Embora o conjunto de testes seja de pequena dimensão, os resultados obtidos são bastante promissores; ### Question Answering for the Semantic Web Abstract: The significant increase of unstructured and heterogeneous information, available on the web, and the need of real-time access promote the development of engines able to \dialogue" with the user and to answer effectively to their requirements. The focus of this thesis is in the development of a cooperative Question-Answering system for the Semantic Web. The main contributions consist in enriching the system with a Discourse Controller that, through the analysis and the semantic representation of the question, the type of expected answer and the structure of the discourse, can provide accurate, informative and justified answers. Increased with cooperative skills, ability to interact with the user, the system obtains information necessary to clarify ambiguities and lead on the path to the correct answer. The system performance is evaluated by comparing with similar question answering systems. Although the test suite has slight dimensions, the results obtained are very promising

A Review on Cooperative Question-Answering Systems

The Question-Answering (QA) systems fall in the study area of Information Retrieval (IR) and Natural Language Processing (NLP). Given a set of documents, a QA system tries to obtain the correct answer to the questions posed in Natural Language (NL). Normally, the QA systems comprise three main components: question classification, information retrieval and answer extraction. Question classification plays a major role in QA systems since it classifies questions according to the type in their entities. The techniques of information retrieval are used to obtain and to extract relevant answers in the knowledge domain. Finally, the answer extraction component is an emerging topic in the QA systems. This module basically classifies and validates the candidate answers. In this paper we present an overview of the QA systems, focusing on mature work that is related to cooperative systems and that has got as knowledge domain the Semantic Web (SW). Moreover, we also present our proposal of a cooperative QA for the SW

Fact Checking in Community Forums

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of cQA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.Comment: AAAI-2018; Fact-Checking; Veracity; Community-Question Answering; Neural Networks; Distributed Representation

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Arabic Cooperative Answer Generation via Wikipedia Article Infoboxes

[EN] The typical question-answering system is facing many challenges related to the processing of questions and information resources in the extraction and generation of adequate answers. These challenges increase when the requested answer is cooperative and its language is Arabic. In this paper, we propose an original approach to generate cooperative answers for user-definitional questions designed to be integrated in a question-answering system. This approach is mainly based on the exploitation of the semi-structured Web knowledge which consists in using features derived from Wikipedia article infoboxes to generate cooperative answers. It is globally independent of a particular language, which gives it the ability to be integrated in any definitional question-answering system. We have chosen to integrate and experiment it in a definitional question-answering system dealing with the Arabic language entitled DefArabicQA. The results showed that this system has a significant impact on the approach efficiency regarding the improvement of the quality of the answer.The work of the third author was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) under the SomEMBED research project (TIN2015-71147-C2-1-P) and by the Generalitat Valenciana under the grant ALMAMATER (PrometeoII/2014/030).Trigui, O.; Belguith, L.; Rosso, P. (2017). Arabic Cooperative Answer Generation via Wikipedia Article Infoboxes. Research in Computing Science. 132:129-153. http://hdl.handle.net/10251/103731S12915313