Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles

Domain ontologies are important information sources for knowledge-based systems. Yet, building domain ontologies from scratch is known to be a very labor-intensive process. In this study, we present our semi-automatic approach to building an ontology for the domain of wind energy which is an important type of renewable energy with a growing share in electricity generation all over the world. Related Wikipedia articles are first processed in an automated manner to determine the basic concepts of the domain together with their properties and next the concepts, properties, and relationships are organized to arrive at the ultimate ontology. We also provide pointers to other engineering ontologies which could be utilized together with the proposed wind energy ontology in addition to its prospective application areas. The current study is significant as, to the best of our knowledge, it proposes the first considerably wide-coverage ontology for the wind energy domain and the ontology is built through a semi-automatic process which makes use of the related Web resources, thereby reducing the overall cost of the ontology building process

Multilingual Schema Matching for Wikipedia Infoboxes

Recent research has taken advantage of Wikipedia's multilingualism as a resource for cross-language information retrieval and machine translation, as well as proposed techniques for enriching its cross-language structure. The availability of documents in multiple languages also opens up new opportunities for querying structured Wikipedia content, and in particular, to enable answers that straddle different languages. As a step towards supporting such queries, in this paper, we propose a method for identifying mappings between attributes from infoboxes that come from pages in different languages. Our approach finds mappings in a completely automated fashion. Because it does not require training data, it is scalable: not only can it be used to find mappings between many language pairs, but it is also effective for languages that are under-represented and lack sufficient training samples. Another important benefit of our approach is that it does not depend on syntactic similarity between attribute names, and thus, it can be applied to language pairs that have distinct morphologies. We have performed an extensive experimental evaluation using a corpus consisting of pages in Portuguese, Vietnamese, and English. The results show that not only does our approach obtain high precision and recall, but it also outperforms state-of-the-art techniques. We also present a case study which demonstrates that the multilingual mappings we derive lead to substantial improvements in answer quality and coverage for structured queries over Wikipedia content.Comment: VLDB201

Enrichment of the Phenotypic and Genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

Currently there are an overwhelming number of scientific publications in Life Sciences, especially in Genetics and Biotechnology. This huge amount of information is structured in corporate Data Warehouses (DW) or in Biological Databases (e.g. UniProt, RCSB Protein Data Bank, CEREALAB or GenBank), whose main drawback is its cost of updating that makes it obsolete easily. However, these Databases are the main tool for enterprises when they want to update their internal information, for example when a plant breeder enterprise needs to enrich its genetic information (internal structured Database) with recently discovered genes related to specific phenotypic traits (external unstructured data) in order to choose the desired parentals for breeding programs. In this paper, we propose to complement the internal information with external data from the Web using Question Answering (QA) techniques. We go a step further by providing a complete framework for integrating unstructured and structured information by combining traditional Databases and DW architectures with QA systems. The great advantage of our framework is that decision makers can compare instantaneously internal data with external data from competitors, thereby allowing taking quick strategic decisions based on richer data.This paper has been partially supported by the MESOLAP (TIN2010-14860) and GEODAS-BI (TIN2012-37493-C03-03) projects from the Spanish Ministry of Education and Competitivity. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

Finding answers to questions, in text collections or web, in open domain or specialty domains

International audienceThis chapter is dedicated to factual question answering, i.e. extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e. a query made of a list of words), and provides clues for finding precise answers. We will first focus on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. We will first present how to answer factual question in open domain. We will also present answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, we present main approaches and the remaining problems

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)

Analysis and implementation of methods for the text categorization

Text Categorization (TC) is the automatic classification of text documents under pre-defined categories, or classes. Popular TC approaches map categories into symbolic labels and use a training set of documents, previously labeled by human experts, to build a classifier which enables the automatic TC of unlabeled documents. Suitable TC methods come from the field of data mining and information retrieval, however the following issues remain unsolved. First, the classifier performance depends heavily on hand-labeled documents that are the only source of knowledge for learning the classifier. Being a labor-intensive and time consuming activity, the manual attribution of documents to categories is extremely costly. This creates a serious limitations when a set of manual labeled data is not available, as it happens in most cases. Second, even a moderately sized text collection often has tens of thousands of terms in that making the classification cost prohibitive for learning algorithms that do not scale well to large problem sizes. Most important, TC should be based on the text content rather than on a set of hand-labeled documents whose categorization depends on the subjective judgment of a human classifier. This thesis aims at facing the above issues by proposing innovative approaches which leverage techniques from data mining and information retrieval. To face problems about both the high dimensionality of the text collection and the large number of terms in a single text, the thesis proposes a hybrid model for term selection which combines and takes advantage of both filter and wrapper approaches. In detail, the proposed model uses a filter to rank the list of terms present in documents to ensure that useful terms are unlikely to be screened out. Next, to limit classification problems due to the correlation among terms, this ranked list is refined by a wrapper that uses a Genetic Algorithm (GA) to retaining the most informative and discriminative terms. Experimental results compare well with some of the top-performing learning algorithms for TC and seems to confirm the effectiveness of the proposed model. To face the issues about the lack and the subjectivity of manually labeled datasets, the basic idea is to use an ontology-based approach which does not depend on the existence of a training set and relies solely on a set of concepts within a given domain and the relationships between concepts. In this regard, the thesis proposes a text categorization approach that applies WordNet for selecting the correct sense of words in a document, and utilizes domain names in WordNet Domains for classification purposes. Experiments show that the proposed approach performs well in classifying a large corpus of documents. This thesis contributes to the area of data mining and information retrieval. Specifically, it introduces and evaluates novel techniques to the field of text categorization. The primary objective of this thesis is to test the hypothesis that: text categorization requires and benefits from techniques designed to exploit document content. hybrid methods from data mining and information retrieval can better support problems about high dimensionality that is the main aspect of large document collections. in absence of manually annotated documents, WordNet domain abstraction can be used that is both useful and general enough to categorize any documents collection. As a final remark, it is important to acknowledge that much of the inspiration and motivation for this work derived from the vision of the future of text categorization processes which are related to specific application domains such as the business area and the industrial sectors, just to cite a few. In the end, it is this vision that provided the guiding framework. However, it is equally important to understand that many of the results and techniques developed in this thesis are not limited to text categorization. For example, the evaluation of disambiguation methods is interesting in its own right and is likely to be relevant to other application fields

Analysis and implementation of methods for the text categorization

Text Categorization (TC) is the automatic classification of text documents under pre-defined categories, or classes. Popular TC approaches map categories into symbolic labels and use a training set of documents, previously labeled by human experts, to build a classifier which enables the automatic TC of unlabeled documents. Suitable TC methods come from the field of data mining and information retrieval, however the following issues remain unsolved. First, the classifier performance depends heavily on hand-labeled documents that are the only source of knowledge for learning the classifier. Being a labor-intensive and time consuming activity, the manual attribution of documents to categories is extremely costly. This creates a serious limitations when a set of manual labeled data is not available, as it happens in most cases. Second, even a moderately sized text collection often has tens of thousands of terms in that making the classification cost prohibitive for learning algorithms that do not scale well to large problem sizes. Most important, TC should be based on the text content rather than on a set of hand-labeled documents whose categorization depends on the subjective judgment of a human classifier. This thesis aims at facing the above issues by proposing innovative approaches which leverage techniques from data mining and information retrieval. To face problems about both the high dimensionality of the text collection and the large number of terms in a single text, the thesis proposes a hybrid model for term selection which combines and takes advantage of both filter and wrapper approaches. In detail, the proposed model uses a filter to rank the list of terms present in documents to ensure that useful terms are unlikely to be screened out. Next, to limit classification problems due to the correlation among terms, this ranked list is refined by a wrapper that uses a Genetic Algorithm (GA) to retaining the most informative and discriminative terms. Experimental results compare well with some of the top-performing learning algorithms for TC and seems to confirm the effectiveness of the proposed model. To face the issues about the lack and the subjectivity of manually labeled datasets, the basic idea is to use an ontology-based approach which does not depend on the existence of a training set and relies solely on a set of concepts within a given domain and the relationships between concepts. In this regard, the thesis proposes a text categorization approach that applies WordNet for selecting the correct sense of words in a document, and utilizes domain names in WordNet Domains for classification purposes. Experiments show that the proposed approach performs well in classifying a large corpus of documents. This thesis contributes to the area of data mining and information retrieval. Specifically, it introduces and evaluates novel techniques to the field of text categorization. The primary objective of this thesis is to test the hypothesis that: text categorization requires and benefits from techniques designed to exploit document content. hybrid methods from data mining and information retrieval can better support problems about high dimensionality that is the main aspect of large document collections. in absence of manually annotated documents, WordNet domain abstraction can be used that is both useful and general enough to categorize any documents collection. As a final remark, it is important to acknowledge that much of the inspiration and motivation for this work derived from the vision of the future of text categorization processes which are related to specific application domains such as the business area and the industrial sectors, just to cite a few. In the end, it is this vision that provided the guiding framework. However, it is equally important to understand that many of the results and techniques developed in this thesis are not limited to text categorization. For example, the evaluation of disambiguation methods is interesting in its own right and is likely to be relevant to other application fields

Cross-lingual question answering

Question Answering has become an intensively researched area in the last decade, being seen as the next step beyond Information Retrieval in the attempt to provide more concise and better access to large volumes of available information. Question Answering builds on Information Retrieval technology for a first touch of possible relevant data and uses further natural language processing techniques to search for candidate answers and to look for clues that accept or invalidate the candidates as right answers to the question. Though most of the research has been carried out in monolingual settings, where the question and the answer-bearing documents share the same natural language, current approaches concentrate on cross-language scenarios, where the question and the documents are in different languages. Known in this context and common with the Information Retrieval research are three methods of crossing the language barrier: by translating the question, by translating the documents or by aligning both the question and the documents to a common inter-lingual representation. We present a cross-lingual English to German Question Answering system, for both factoid and definition questions, using a German monolingual system and translating the questions from English to German. Two different techniques of translation are evaluated: • direct translation of the English input question into German and • transfer-based translation, by using an intermediate representation that captures the “meaning” of the original question and is translated into the target language. For both translation techniques two types of translation tools are used: bilingual dictionaries and machine translation. The intermediate representation captures the semantic meaning of the question in terms of Question Type (QType), Expected Answer Type (EAType) and Focus, information that steers the workflow of the question answering process. The German monolingual Question Answering system can answer both factoid and definition questions and is based on several premises: • facts and definitions are usually expressed locally at the level of a sentence and its surroundings; • proximity of concepts within a sentence can be related to their semantic dependency; • for factoid questions, redundancy of candidate answers is a good indicator of their suitability; • definitions of concepts are expressed using fixed linguistic structures such as appositions, modifiers, and abbreviation extensions. Extensive evaluations of the monolingual system have shown that the above mentioned hypothesis holds true in most of the cases when dealing with a fairly large collection of documents, like the one used in the CLEF evaluation forum.Innerhalb der letzten zehn Jahre hat sich Question Answering zu einem intensiv erforschten Themengebiet gewandelt, es stellt den nächsten Schritt des Information Retrieval dar, mit dem Bestreben einen präziseren Zugang zu großen Datenbeständen von verfügbaren Informationen bereitzustellen. Das Question Answering setzt auf die Information Retrieval-Technologie, um mögliche relevante Daten zu suchen, kombiniert mit weiteren Techniken zur Verarbeitung von natürlicher Sprache, um mögliche Antwortkandidaten zu identifizieren und diese anhand von Hinweisen oder Anhaltspunkten entsprechend der Frage als richtige Antwort zu akzeptieren oder als unpassend zu erklären. Während ein Großteil der Forschung den einsprachigen Kontext voraussetzt, wobei Frage- und Antwortdokumente ein und dieselbe Sprache teilen, konzentrieren sich aktuellere Ansätze auf sprachübergreifende Szenarien, in denen die Frage- und Antwortdokumente in unterschiedlichen Sprachen vorliegen. Im Kontext des Information Retrieval existieren drei bekannte Ansätze, die versuchen auf unterschiedliche Art und Weise die Sprachbarriere zu überwinden: durch die Übersetzung der Frage, durch die Übersetzung der Dokumente oder durch eine Angleichung von sowohl der Frage als auch der Dokumente zu einer gemeinsamen interlingualen Darstellung. Wir präsentieren ein sprachübergreifendes Question Answering System vom Englischen ins Deutsche, das sowohl für Faktoid- als auch für Definitionsfragen funktioniert. Dazu verwenden wir ein einsprachiges deutsches System und übersetzen die Fragen vom Englischen ins Deutsche. Zwei unterschiedliche Techniken der Übersetzung werden untersucht: • die direkte Übersetzung der englischen Fragestellung ins Deutsche und • die Abbildungs-basierte Übersetzung, die eine Zwischendarstellung verwendet, um die „Semantik“ der ursprünglichen Frage zu erfassen und in die Zielsprache zu übersetzen. Für beide aufgelisteten Übersetzungstechniken werden zwei Übersetzungsquellen verwendet: zweisprachige Wörterbücher und maschinelle Übersetzung. Die Zwischendarstellung erfasst die Semantik der Frage in Bezug auf die Art der Frage (QType), den erwarteten Antworttyp (EAType) und Fokus, sowie die Informationen, die den Ablauf des Frage-Antwort-Prozesses steuern. Das deutschsprachige Question Answering System kann sowohl Faktoid- als auch Definitionsfragen beantworten und basiert auf mehreren Prämissen: • Fakten und Definitionen werden in der Regel lokal auf Satzebene ausgedrückt; • Die Nähe von Konzepten innerhalb eines Satzes kann auf eine semantische Verbindung hinweisen; • Bei Faktoidfragen ist die Redundanz der Antwortkandidaten ein guter Indikator für deren Eignung; • Definitionen von Begriffen werden mit festen sprachlichen Strukturen ausgedrückt, wie Appositionen, Modifikatoren, Abkürzungen und Erweiterungen. Umfangreiche Auswertungen des einsprachigen Systems haben gezeigt, dass die oben genannten Hypothesen in den meisten Fällen wahr sind, wenn es um eine ziemlich große Sammlung von Dokumenten geht, wie bei der im CLEF Evaluationsforum verwendeten Version