Bridging DBpedia Categories and DL-Concept Definitions using Formal Concept Analysis

International audienceThe popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud. Particularly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions. In this work we propose a novel technique based on Formal Concept Analysis which organizes RDF data into a concept lattice. This allows the discovery of implications, which are used to automatically detect missing information and then to complete RDF data

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach

Chord Sequence patterns in OWL

This thesis addresses the representation of and reasoning on musical knowledge in the Semantic Web. The Semantic Web is an evolving extension of the World Wide Web that aims at describing information that is distributed on the web in a machine-processable form. Existing approaches to modelling musical knowledge in the context of the Semantic Web have focused on metadata. The description of musical content and reasoning as well as integration of content descriptions and metadata are yet open challenges. This thesis discusses the possibilities of representing musical knowledge in the Web Ontology Language (OWL) focusing on chord sequence representation and presents and evaluates a newly developed solution. The solution consists of two main components. Ontological modelling patterns for musical entities such as notes and chords are introduced in the (MEO) ontology. A sequence pattern language and ontology (SEQ) has been developed that can express patterns in a form resembling regular expressions. As MEO and SEQ patterns both rewrite to OWL they can be combined freely. Reasoning tasks such as instance classification, retrieval and pattern subsumption are then executable by standard Semantic Web reasoners. The expressiveness of SEQ has been studied, in particular in relation to grammars. The complexity of reasoning on SEQ patterns has been studied theoretically and empirically, and optimisation methods have been developed. There is still great potential for improvement if specific reasoning algorithms were developed to exploit the sequential structure, but the development of such algorithms is outside the scope of this thesis. MEO and SEQ have also been evaluated in several musicological scenarios. It is shown how patterns that are characteristic of musical styles can be expressed and chord sequence data can be classified, demonstrating the use of the language in web retrieval and as integration layer for different chord patterns and corpora. Furthermore, possibilities of using SEQ patterns for harmonic analysis are explored using grammars for harmony; both a hybrid system and a translation of limited context-free grammars into SEQ patterns have been developed. Finally, a distributed scenario is evaluated where SEQ and MEO are used in connection with DBpedia, following the Linked Data approach. The results show that applications are already possible and will benefit in the future from improved quality and compatibility of data sources as the Semantic Web evolves

Workshop NotesInternational Workshop ``What can FCA do for Artificial Intelligence?'' (FCA4AI 2015)

International audienceThis volume includes the proceedings of the fourth edition of the FCA4AI --What can FCA do for Artificial Intelligence?-- Workshop co-located with the IJCAI 2015 Conference in Buenos Aires (Argentina). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications) which can be used for many AI needs, e.g. knowledge discovery, learning, knowledge representation, reasoning, ontology engineering, as well as information retrieval and text processing. There are many ``natural links'' between FCA and AI, and the present workshop is organized for discussing about these links and more generally for improving the links between knowledge discovery based on FCA and knowledge management in artificial intelligence

Reusing Ontologies to Enrich Semantically User Content in Web2.0: A Case Study on Folksonomies

Semantic Web and Web2.0 emerged during the past decade promising to achieve new frontiers for the Web. On the one hand, the Semantic Web is an interlinked web of data, supported by ontological semantics and allowing for intelligent applications such as semantic search and integration of heterogeneous content across systems and applications. On the other hand, Web2.0 represents the new technologies and paradigms that revolutionised the user engagement in content creation and introduced novel means towards social interaction. Bridging the gap between Web2.0 and the Semantic Web has been proposed as a means to better manage and interact with the large amounts of user contributed content, which is a new challenge for Web2.0. This thesis focuses on a popular paradigm of Web2.0, folksonomies. In particular, we investigate the semantic enrichment of folksonomy tagspaces by reusing ontologies available in the Semantic Web. We identify the need for methods that automatically apply semantic descriptions to user generated content without requiring user intervention or alteration of the current tagging paradigm. We use an iterative approach in order to identify the characteristics of folksonomies and the attributes of knowledge sources that influence the semantic enrichment of tagspaces. We build on the results of our experimental studies to implement a folksonomy enrichment algorithm, that given an input tagspace, automatically creates a semantic structure that describes the meaning and relations of tags. We introduce measures for the evaluation of enriched tagspaces and finally, we propose a search algorithm that exploits the semantic structures to improve folksonomy search

A knowledge-based framework for information extraction and exploration

Harnessing insights from the colossal amount of online information requires the computerised processing of unstructured text in order to satisfy the information need of particular applications such as recommender systems and sentiment analysis. The increasing availability of online documents that describe domain-specific information provides an opportunity in employing a knowledge-based approach in extracting information from Web data. In this thesis, a novel comprehensive knowledge-based framework is proposed to construct and exploit a domain-specific semantic knowledgebase. The proposed framework introduces a methodology for linking several components of different techniques and tools. It focuses on providing reusable and configurable data and application templates, which allow developers to apply it in diversity of domains. The objectives of this framework are: extracting information from unstructured data, constructing a semantic knowledgebase from the extracted information, enriching the resultant semantic knowledgebase by sourcing appropriate semi-structured and structured datasets, and consuming the resultant semantic knowledgebase to facilitate the intelligent exploration and search of information. For the purpose of investigating the challenges of extracting and modelling information in a specific domain, the financial domain was employed as a use-case in the context of a stock investment motivating scenario. The developed knowledge-based approach exploits the semantic and syntactic characteristics of the problem domain knowledge in implementing a hybrid approach of Rule-based and Machine Learning based relation classification. The rule-based approach is adopted in the Natural Language Processing tasks associated with linguistic and structural features, Named Entity Recognition, instances labelling and feature generation processes. The results of these tasks are used to classify the relations between the named entities by employing the Machine Learning based relation classification. In addition, the domain knowledge is analysed to benefit knowledge modelling by translating the domain key concepts into a formal ontology. This ontology is employed in constructing semantic knowledgebase from unstructured online data of a specific domain, enriching the resulting semantic knowledgebase by sourcing semi-structured and structured online data sources and applying advanced classifications and inference technologies to infer new and interesting facts to improve the decision-making and intelligent exploration activities. However, most relations are non-binary in the problem domain knowledge because of its specific characteristic hence an appropriate N-ary relation patterns technique were adopted and investigated. A serious of a novel experiments were conducted to implement and configure a Machine Learning based relation classification. The experimental evaluation evidenced that the developed knowledge-assisted ML relation classification model, which was further boosted by our implementation of GAs to reduce the feature space, has resulted in significant improvement in the process of relation extraction. The experimental results also indicate that amongst the implemented ML algorithms, SVM exhibited the best relation classification accuracy in the majority of the training datasets, while retaining acceptable levels of accuracy in the rest in the remaining training datasets. Web Ontology Language (OWL) reasoning and rule-based reasoning on the resultant semantic knowledgebase were applied to derive stock investment specific recommendations. In addition, SPARQL query language was employed to explore the semantic knowledgebase. Moreover, taking into consideration the problem domain's requirements for modelling non-binary relations, a relation-as-class N-ary relations pattern was implemented, and the reasoning axioms and query language were adjusted to fit the intermediate resources in the N-ary relations requirements. In this thesis also the experience on addressing the challenges of implementing the proposed knowledge-based framework for constructing and exploiting a semantic knowledgebase were summarised. These challenges can be considered by domain experts and knowledge engineers as a novel methodology for employing the Semantic Web Technologies for the knowledge user to intelligently exploit knowledge in similar problem domains. The evaluation of knowledge accessibility by utilising Semantic Web Technologies in the developed application includes the ability of data retrieval to obtain either the entire or some portion of the data from the semantic knowledgebase for a particular use-case scenario. Investigating the tasks of reasoning, accessing and querying the semantic knowledgebase evidences that Semantic Web Technologies can perform an accurate and complex knowledge representation to share Knowledge from a diversity of data sources and, improve the decision‑making process and the intelligent exploration of the semantic knowledgebase

Linked Data Quality Assessment and its Application to Societal Progress Measurement

In recent years, the Linked Data (LD) paradigm has emerged as a simple mechanism for employing the Web as a medium for data and knowledge integration where both documents and data are linked. Moreover, the semantics and structure of the underlying data are kept intact, making this the Semantic Web. LD essentially entails a set of best practices for publishing and connecting structure data on the Web, which allows publish- ing and exchanging information in an interoperable and reusable fashion. Many different communities on the Internet such as geographic, media, life sciences and government have already adopted these LD principles. This is confirmed by the dramatically growing Linked Data Web, where currently more than 50 billion facts are represented. With the emergence of Web of Linked Data, there are several use cases, which are possible due to the rich and disparate data integrated into one global information space. Linked Data, in these cases, not only assists in building mashups by interlinking heterogeneous and dispersed data from multiple sources but also empowers the uncovering of meaningful and impactful relationships. These discoveries have paved the way for scientists to explore the existing data and uncover meaningful outcomes that they might not have been aware of previously. In all these use cases utilizing LD, one crippling problem is the underlying data quality. Incomplete, inconsistent or inaccurate data affects the end results gravely, thus making them unreliable. Data quality is commonly conceived as fitness for use, be it for a certain application or use case. There are cases when datasets that contain quality problems, are useful for certain applications, thus depending on the use case at hand. Thus, LD consumption has to deal with the problem of getting the data into a state in which it can be exploited for real use cases. The insufficient data quality can be caused either by the LD publication process or is intrinsic to the data source itself. A key challenge is to assess the quality of datasets published on the Web and make this quality information explicit. Assessing data quality is particularly a challenge in LD as the underlying data stems from a set of multiple, autonomous and evolving data sources. Moreover, the dynamic nature of LD makes assessing the quality crucial to measure the accuracy of representing the real-world data. On the document Web, data quality can only be indirectly or vaguely defined, but there is a requirement for more concrete and measurable data quality metrics for LD. Such data quality metrics include correctness of facts wrt. the real-world, adequacy of semantic representation, quality of interlinks, interoperability, timeliness or consistency with regard to implicit information. Even though data quality is an important concept in LD, there are few methodologies proposed to assess the quality of these datasets. Thus, in this thesis, we first unify 18 data quality dimensions and provide a total of 69 metrics for assessment of LD. The first methodology includes the employment of LD experts for the assessment. This assessment is performed with the help of the TripleCheckMate tool, which was developed specifically to assist LD experts for assessing the quality of a dataset, in this case DBpedia. The second methodology is a semi-automatic process, in which the first phase involves the detection of common quality problems by the automatic creation of an extended schema for DBpedia. The second phase involves the manual verification of the generated schema axioms. Thereafter, we employ the wisdom of the crowds i.e. workers for online crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) to assess the quality of DBpedia. We then compare the two approaches (previous assessment by LD experts and assessment by MTurk workers in this study) in order to measure the feasibility of each type of the user-driven data quality assessment methodology. Additionally, we evaluate another semi-automated methodology for LD quality assessment, which also involves human judgement. In this semi-automated methodology, selected metrics are formally defined and implemented as part of a tool, namely R2RLint. The user is not only provided the results of the assessment but also specific entities that cause the errors, which help users understand the quality issues and thus can fix them. Finally, we take into account a domain-specific use case that consumes LD and leverages on data quality. In particular, we identify four LD sources, assess their quality using the R2RLint tool and then utilize them in building the Health Economic Research (HER) Observatory. We show the advantages of this semi-automated assessment over the other types of quality assessment methodologies discussed earlier. The Observatory aims at evaluating the impact of research development on the economic and healthcare performance of each country per year. We illustrate the usefulness of LD in this use case and the importance of quality assessment for any data analysis

Lexicalização de ontologias : o relacionamento entre conteúdo e significado no contexto da Recuperação da Informação

Esta proposta visa representar a linguagem natural na forma adequada às ontologias e vice-versa. Para tanto, propõe-se à criação semiautomática de base de léxicos em português brasileiro, contendo informações morfológicas, sintáticas e semânticas apropriadas para a leitura por máquinas, permitindo vincular dados estruturados e não estruturados, bem como integrar a leitura em modelo de recuperação da informação para aumentar a precisão. Os resultados alcançados demonstram a utilização da metodologia, no domínio de risco financeiro em português, para a elaboração da ontologia, da base léxico-semântica e da proposta do modelo de recuperação da informação semântica. Para avaliar a performance do modelo proposto, foram selecionados documentos contendo as principais definições do domínio de risco financeiro. Esses foram indexados com e sem anotação semântica. Para possibilitar a comparação entre as abordagens, foram criadas duas bases, a primeira representando a busca tradicional, e a segunda contendo o índice construído, a partir dos textos com as anotações semânticas para representar a busca semântica. A avaliação da proposta é baseada na revocação e na precisão. As consultas submetidas ao modelo mostram que a busca semântica supera o desempenho da tradicional e validam a metodologia empregada. O procedimento, embora adicione complexidade em sua elaboração, pode ser reproduzido em qualquer outro domínio.The proposal presented in this study seeks to properly represent natural language to ontologies and vice-versa. Therefore, the semi-automatic creation of a lexical database in Brazilian Portuguese containing morphological, syntactic, and semantic information that can be read by machines was proposed, allowing the link between structured and unstructured data and its integration into an information retrieval model to improve precision. The results obtained demonstrated that the methodology can be used in the risco financeiro (financial risk) domain in Portuguese for the construction of an ontology and the lexical-semantic database and the proposal of a semantic information retrieval model. In order to evaluate the performance of the proposed model, documents containing the main definitions of the financial risk domain were selected and indexed with and without semantic annotation. To enable the comparison between the approaches, two databases were created based on the texts with the semantic annotations to represent the semantic search. The first one represents the traditional search and the second contained the index built based on the texts with the semantic annotations to represent the semantic search. The evaluation of the proposal was based on recall and precision. The queries submitted to the model showed that the semantic search outperforms the traditional search and validates the methodology used. Although more complex, the procedure proposed can be used in all kinds of domains

Fusing Automatically Extracted Annotations for the Semantic Web

This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination. Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories