A negation query engine for complex query transformations

Natural language interfaces to ontologies allow users to query the system using natural language queries. These systems take natural language query as input and transform it to formal query language equivalent to retrieve the desired information from ontologies. The existing natural language interfaces to ontologies offer support for handling negation queries; however, they offer limited support for dealing with them. This paper proposes a negation query handling engine which can handle relatively complex natural language queries than the existing systems. The proposed engine effectively understands the intent of the user query on the basis of a sophisticated algorithm, which is governed by a set of techniques and transformation rules. The proposed engine was evaluated using the Mooney data set and AquaLog dataset, and it manifested encouraging results

Natural Language Interfaces for Querying and Retrieving Information from Ontology-based Knowledge Bases

Tesis doctoral titulada “Interfaces de lenguaje natural para la consulta y recuperación de información de bases de conocimiento basadas en ontologías ", defendida por Mario Andrés Paredes Valverde en la Universidad de Murcia y elaborada bajo la dirección de los doctores Rafael Valencia García (Universidad de Murcia) y Miguel Ángel Rodríguez García (King Abdullah University of Science & Technology). La defensa tuvo lugar el 23 de mayo de 2017 ante el tribunal formado por los doctores Juan Miguel Gómez Berbís (Presidente, Universidad Carlos III de Madrid), Francisco García Sánchez (Secretario, Universidad de Murcia) y la doctora Catalina Martínez Costa (Vocal, Medical University of Graz) y la tesis obtuvo la mención Cum Laude y Doctor Internacional.Ph.D. thesis entitled “Natural language interfaces for querying and retrieving information from ontology-based knowledge bases” written by Mario Andrés Paredes Valverde at the University of Murcia under the supervision of the Ph.D. Rafael Valencia García (University of Murcia) and Ph.D. Miguel Ángel Rodríguez García (King Abdullah University of Science & Technology). The viva voice was held on the 23rd May 2017 and the members of the commission were the Ph.D. Juan Miguel Gómez Berbís (President, University Carlos III of Madrid), Ph.D. Francisco García Sánchez (Secretary, University of Murcia) and Ph.D. Catalina Martínez Costa (Vocal, University of Graz) and the thesis obtained the mention Cum Laude and International Doctor

Towards Portable Natural Language Interfaces to Knowledge Bases - The Case of the ORAKEL System -

Cimiano P, Haase P, Heizmann J, Mantel M, Studer R. Towards Portable Natural Language Interfaces to Knowledge Bases - The Case of the ORAKEL System -. Data & Knowledge Engineering (DKE). 2008;65(2):325-354

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

Ripple Down Rules for Question Answering

Recent years have witnessed a new trend of building ontology-based question answering systems. These systems use semantic web information to produce more precise answers to users' queries. However, these systems are mostly designed for English. In this paper, we introduce an ontology-based question answering system named KbQAS which, to the best of our knowledge, is the first one made for Vietnamese. KbQAS employs our question analysis approach that systematically constructs a knowledge base of grammar rules to convert each input question into an intermediate representation element. KbQAS then takes the intermediate representation element with respect to a target ontology and applies concept-matching techniques to return an answer. On a wide range of Vietnamese questions, experimental results show that the performance of KbQAS is promising with accuracies of 84.1% and 82.4% for analyzing input questions and retrieving output answers, respectively. Furthermore, our question analysis approach can easily be applied to new domains and new languages, thus saving time and human effort.Comment: V1: 21 pages, 7 figures, 10 tables. V2: 8 figures, 10 tables; shorten section 2; change sections 4.3 and 5.1.2. V3: Accepted for publication in the Semantic Web journal. V4 (Author's manuscript): camera ready version, available from the Semantic Web journal at http://www.semantic-web-journal.ne

An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.This paper has been partially supported by the MESOLAP (TIN2010-14860), GEODAS-BI (TIN2012-37493-C03-03), LEGOLANGUAGE (TIN2012-31224) and DIIM2.0 (PROMETEOII/2014/001) projects from the Spanish Ministry of Education and Competitivity. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

Класифікація засобів та методів семантичного пошуку в Web

Розглянуто проблеми, пов’язані з удосконаленням пошуку інформації у відкритому середовищі, обґрунтована потреба в його семантизації. Проаналізовано сучасний стан та перспективи розвитку систем семантичного пошуку, орієнтованих на обробку інформаційних ресурсів Web, розглянуто критерії класифікації таких систем. В цьому аналізі значна увага приділяється використанню у семантичному пошуку онтологій, що містять знання щодо предметної області пошуку та користувача, для якого виконується пошук. На основі аналізу властивостей існуючих систем семантичного пошуку з точки зору цих критеріїв виділені області подальшого вдосконалення цих систем, запропоновано їх реалізацію у системі семантичного пошуку “МАІПС”.Рассмотрены проблемы, связанные с усовершенствованием поиска информации в открытой среде, обоснована потребность в ее семантизации. Проанализировано современное состояние и перспективы развития систем семантического поиска, ориентированных на обработку информационных ресурсов Web, рассмотрены критерии классификации таких систем. В этом анализе значительное внимание отводится использованию в семантическом поиске онтологий, которые содержат знания относительно предметной области поиска и относительно пользователя, для которого выполняется поиск. На основе анализа свойств существующих систем семантического поиска с точки зрения этих критериев выделенные области дальнейшего усовершенствования этих систем, предложена их реализация в системе семантического поиска "МАИПС".Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability

Interrogation des sources de données hétérogènes : une approche pour l'analyse des requêtes

No english summary availableLe volume des données structurées produites devient de plus en plus considérable. Plusieurs aspects concourent à l’accroissement du volume de données structurées. Au niveau du Web, le Web de données (Linked Data) a permis l’interconnexion de plusieurs jeux de données disponibles créant un gigantesque hub de données. Certaines applications comme l’extraction d’informations produisent des données pour peupler des ontologies. Les capteurs et appareils (ordinateur, smartphone, tablette) connectés produisent de plus en plus de données. Les systèmes d’information d’entreprise sont également affectés. Accéder à une information précise devient de plus en plus difficile. En entreprise, des outils de recherche ont été mis au point pour réduire la charge de travail liée à la recherche d’informations, mais ces outils génèrent toujours des volumes importants. Les interfaces en langage naturel issues du Traitement Automatique des Langues peuvent être mises à contribution pour permettre aux utilisateurs d’exprimer naturellement leurs besoins en informations sans se préoccuper des aspects techniques liés à l’interrogation des données structurées. Les interfaces en langage naturel permettent également d’avoir une réponse concise sans avoir besoin de fouiller d’avantage dans une liste de documents. Cependant actuellement, ces interfaces ne sont pas assez robustes pour être utilisées par le grand public ou pour répondre aux problèmes de l’hétérogénéité ou du volume de données. Nous nous intéressons à la robustesse de ces systèmes du point de vue de l’analyse de la question. La compréhension de la question de l’utilisateur est une étape importante pour retrouver la réponse. Nous proposons trois niveaux d’interprétation pour l’analyse d’une question : domaine abstrait, domaine concret et la relation domaine abstrait/concret. Le domaine abstrait s’intéresse aux données qui sont indépendantes de la nature des jeux de données. Il s’agit principalement des données de mesures. L’interprétation s’appuie sur la logique propre à ces mesures. Le plus souvent cette logique a été bien décrite dans les autres disciplines, mais la manière dont elle se manifeste en langage naturel n’a pas fait l’objet d’une large investigation pour les interfaces en langage naturel basées sur des données structurées. Le domaine concret couvre le domaine métier de l’application. Il s’agit de bien interpréter la logique métier. Pour une base de données, il correspond au niveau applicatif (par opposition à la couche des données). La plupart des interfaces en langage naturel se focalisent principalement sur la couche des données. La relation domaine abstrait/concret s’intéresse aux interprétations qui chevauchent les deux domaines. Du fait de l’importance de l’analyse linguistique, nous avons développé l’infrastructure pour mener cette analyse. L’essentiel des interfaces en langage naturel qui tentent de répondre aux problématiques du Web de données (Linked Data) ont été développées jusqu’ici pour la langue anglaise et allemande. Notre interface tente d’abord de répondre à des questions en françai

Learning Automatic Question Answering from Community Data

Although traditional search engines can retrieval thousands or millions of web links related to input keywords, users still need to manually locate answers to their information needs from multiple returned documents or initiate further searches. Question Answering (QA) is an effective paradigm to address this problem, which automatically finds one or more accurate and concise answers to natural language questions. Existing QA systems often rely on off-the-shelf Natural Language Processing (NLP) resources and tools that are not optimized for the QA task. Additionally, they tend to require hand-crafted rules to extract properties from input questions which, in turn, means that it would be time and manpower consuming to build comprehensive QA systems. In this thesis, we study the potentials of using the Community Question Answering (cQA) archives as a central building block of QA systems. To that end, this thesis proposes two cQA-based query expansion and structured query generation approaches, one employed in Text-based QA and the other in Ontology-based QA. In addition, based on above structured query generation method, an end-to-end open-domain Ontology-based QA is developed and evaluated on a standard factoid QA benchmark