Lexical platform – the first step towards user-centred integration of lexical resources

Lexical platform – the first step towards user-centred integration of lexical resources Lexical platform – the first step towards user-centred integration of lexical resources The paper describes the Lexical Platform - a means for lightweight integration of independent lexical resources. Lexical resources (LRs) are represented as web components that implement a minimal set of predefined programming interfaces. These provide functionality for querying and generate a simple, common presentation format. Therefore, a common data format is not needed and the identity of component LRs is preserved. Users can search, browse and navigate via resources on the basis of a limited set of anchor elements such as base form, word form and synset id.   Platforma leksykalna – pierwszy krok w kierunku integracji zasobów leksykalnych zorientowanej na użytkowników Artykuł opisuje Platformę Leksykalną – sposób na lekką integrację niezależnych zasobów leksykalnych. Zasoby leksykalne są na niej reprezentowane jako komponenty webowe, które implementują minimalny zestaw predefiniowanych interfejsów programistycznych. Interfejsy te dostarczają funkcjonalność do przeszukiwania oraz generują prosty, jednolity format prezentacji zasobów. W związku z tym wspólny format danych nie jest konieczny i tożsamość składowych zasobów leksykalnych jest zachowana. Użytkownicy mogą przeszukiwać zasoby na podstawie ograniczonego zbioru odwołań takich jak forma podstawowa, forma wyrazowa i identyfikator synsetu

Information fusion for automated question answering

Until recently, research efforts in automated Question Answering (QA) have mainly focused on getting a good understanding of questions to retrieve correct answers. This includes deep parsing, lookups in ontologies, question typing and machine learning of answer patterns appropriate to question forms. In contrast, I have focused on the analysis of the relationships between answer candidates as provided in open domain QA on multiple documents. I argue that such candidates have intrinsic properties, partly regardless of the question, and those properties can be exploited to provide better quality and more user-oriented answers in QA.Information fusion refers to the technique of merging pieces of information from different sources. In QA over free text, it is motivated by the frequency with which different answer candidates are found in different locations, leading to a multiplicity of answers. The reason for such multiplicity is, in part, the massive amount of data used for answering, and also its unstructured and heterogeneous content: Besides am¬ biguities in user questions leading to heterogeneity in extractions, systems have to deal with redundancy, granularity and possible contradictory information. Hence the need for answer candidate comparison. While frequency has proved to be a significant char¬ acteristic of a correct answer, I evaluate the value of other relationships characterizing answer variability and redundancy.Partially inspired by recent developments in multi-document summarization, I re¬ define the concept of "answer" within an engineering approach to QA based on the Model-View-Controller (MVC) pattern of user interface design. An "answer model" is a directed graph in which nodes correspond to entities projected from extractions and edges convey relationships between such nodes. The graph represents the fusion of information contained in the set of extractions. Different views of the answer model can be produced, capturing the fact that the same answer can be expressed and pre¬ sented in various ways: picture, video, sound, written or spoken language, or a formal data structure. Within this framework, an answer is a structured object contained in the model and retrieved by a strategy to build a particular view depending on the end user (or taskj's requirements.I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence, inclusion, aggregation and alternative. Quantitatively, answer candidate modeling im¬ proves answer extraction accuracy. It also proves to be more robust to incorrect answer candidates than traditional techniques. Qualitatively, models provide meta-information encoded by relationships that allow shallow reasoning to help organize and generate the final output

Improving search engines with open Web-based SKOS vocabularies

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaThe volume of digital information is increasingly larger and even though organiza-tions are making more of this information available, without the proper tools users have great difficulties in retrieving documents about subjects of interest. Good infor-mation retrieval mechanisms are crucial for answering user information needs. Nowadays, search engines are unavoidable - they are an essential feature in docu-ment management systems. However, achieving good relevancy is a difficult problem particularly when dealing with specific technical domains where vocabulary mismatch problems can be prejudicial. Numerous research works found that exploiting the lexi-cal or semantic relations of terms in a collection attenuates this problem. In this dissertation, we aim to improve search results and user experience by inves-tigating the use of potentially connected Web vocabularies in information retrieval en-gines. In the context of open Web-based SKOS vocabularies we propose a query expan-sion framework implemented in a widely used IR system (Lucene/Solr), and evaluated using standard IR evaluation datasets. The components described in this thesis were applied in the development of a new search system that was integrated with a rapid applications development tool in the context of an internship at Quidgest S.A.Fundação para a Ciência e Tecnologia - ImTV research project, in the context of the UTAustin-Portugal collaboration (UTA-Est/MAI/0010/2009); QSearch project (FCT/Quidgest

Inducing discourse marker inventories from lexical knowledge graphs

Discourse marker inventories are important tools for the development of both discourse parsers and corpora with discourse annotations. In this paper we explore the potential of massively multilingual lexical knowledge graphs to induce multilingual discourse marker lexicons using concept propagation methods as previously developed in the context of translation inference across dictionaries. Given one or multiple source languages with discourse marker inventories that discourse relations as senses of potential discourse markers, as well as a large number of bilingual dictionaries that link them – directly or indirectly – with the target language, we specifically study to what extent discourse marker induction can benefit from the integration of information from different sources, the impact of sense granularity and what limiting factors may need to be considered. Our study uses discourse marker inventories from nine European languages normalized against the discourse relation inventory of the Penn Discourse Treebank (PDTB), as well as three collections of machine-readable dictionaries with different characteristics, so that the interplay of a large number of factors can be studied

Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

Text mining and natural language processing for the early stages of space mission design

Final thesis submitted December 2021 - degree awarded in 2022A considerable amount of data related to space mission design has been accumulated since artificial satellites started to venture into space in the 1950s. This data has today become an overwhelming volume of information, triggering a significant knowledge reuse bottleneck at the early stages of space mission design. Meanwhile, virtual assistants, text mining and Natural Language Processing techniques have become pervasive to our daily life. The work presented in this thesis is one of the first attempts to bridge the gap between the worlds of space systems engineering and text mining. Several novel models are thus developed and implemented here, targeting the structuring of accumulated data through an ontology, but also tasks commonly performed by systems engineers such as requirement management and heritage analysis. A first collection of documents related to space systems is gathered for the training of these methods. Eventually, this work aims to pave the way towards the development of a Design Engineering Assistant (DEA) for the early stages of space mission design. It is also hoped that this work will actively contribute to the integration of text mining and Natural Language Processing methods in the field of space mission design, enhancing current design processes.A considerable amount of data related to space mission design has been accumulated since artificial satellites started to venture into space in the 1950s. This data has today become an overwhelming volume of information, triggering a significant knowledge reuse bottleneck at the early stages of space mission design. Meanwhile, virtual assistants, text mining and Natural Language Processing techniques have become pervasive to our daily life. The work presented in this thesis is one of the first attempts to bridge the gap between the worlds of space systems engineering and text mining. Several novel models are thus developed and implemented here, targeting the structuring of accumulated data through an ontology, but also tasks commonly performed by systems engineers such as requirement management and heritage analysis. A first collection of documents related to space systems is gathered for the training of these methods. Eventually, this work aims to pave the way towards the development of a Design Engineering Assistant (DEA) for the early stages of space mission design. It is also hoped that this work will actively contribute to the integration of text mining and Natural Language Processing methods in the field of space mission design, enhancing current design processes

Constructive Ontology Engineering

The proliferation of the Semantic Web depends on ontologies for knowledge sharing, semantic annotation, data fusion, and descriptions of data for machine interpretation. However, ontologies are difficult to create and maintain. In addition, their structure and content may vary depending on the application and domain. Several methods described in literature have been used in creating ontologies from various data sources such as structured data in databases or unstructured text found in text documents or HTML documents. Various data mining techniques, natural language processing methods, syntactical analysis, machine learning methods, and other techniques have been used in building ontologies with automated and semi-automated processes. Due to the vast amount of unstructured text and its continued proliferation, the problem of constructing ontologies from text has attracted considerable attention for research. However, the constructed ontologies may be noisy, with missing and incorrect knowledge. Thus ontology construction continues to be a challenging research problem. The goal of this research is to investigate a new method for guiding a process of extracting and assembling candidate terms into domain specific concepts and relationships. The process is part of an overall semi automated system for creating ontologies from unstructured text sources and is driven by the user’s goals in an incremental process. The system applies natural language processing techniques and uses a series of syntactical analysis tools for extracting grammatical relations from a list of text terms representing the parts of speech of a sentence. The extraction process focuses on evaluating the subject predicate-object sequences of the text for potential concept-relation-concept triples to be built into an ontology. Users can guide the system by selecting seedling concept-relation-concept triples to assist building concepts from the extracted domain specific terms. As a result, the ontology building process develops into an incremental one that allows the user to interact with the system, to guide the development of an ontology, and to tailor the ontology for the user’s application needs. The main contribution of this work is the implementation and evaluation of a new semi- automated methodology for constructing domain specific ontologies from unstructured text corpus

Predicate Matrix: an interoperable lexical knowledge base for predicates

183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas