Ontologies describe the semantics of data and provide a uniform framework of understanding between different parties. The main common reference to an ontology definition describes them as knowledge bodies, which bring a formal representation of a shared conceptualization of a domain - the objects, concepts and other entities that are assumed to exist in a certain area of interest together with the relationships holding among them. However, in open and evolving systems with decentralized nature (as, for example, the Semantic Web), it is unlikely for different parties to adopt the same ontology. The problem of ontology matching evolves from the need to align ontologies, which cover the same or similar domains of knowledge. The task is to reducing ontology heterogeneity, which can occur in different forms, not in isolation from one another. Syntactically heterogeneous ontologies are expressed in different formal languages. Terminological heterogeneity stands for variations in names when referring to the same entities and concepts. Conceptual heterogeneity refers to differences in coverage, granularity or scope when modeling the same domain of interest. Finally, prgamatic heterogeneity is about mismatches in how entities are interpreted by people in a given context. The work presented in this thesis is a contribution to the problem of reducing the terminological and conceptual heterogeneity of hierarchical ontologies (defined as ontologies, which contain a hierarchical body), populated with text documents. We make use of both intensional (structural) and extensional (instance-based) aspects of the input ontologies and combine them in order to establish correspondences between their elements. In addition, the proposed procedures yield assertions on the granularity and the extensional richness of one ontology compared to another, which is helpful at assisting a process of ontology merging. Although we put an emphasis on the application of instance-based techniques, we show that combining them with intensional approaches leads to more efficient (both conceptually and computationally) similarity judgments. The thesis is oriented towards both researchers and practitioners in the domain of ontology matching and knowledge sharing. The proposed solutions can be applied successfully to the problem of matching web-directories and facilitating the exchange of knowledge on the web-scale.

Software agents interact to solve tasks, the details of which need to be described in a language understandable by all the actors involved. Ontologies provide a formalism for defining both the domain of the task and the terminology used to describe it. However, finding a shared ontology has proved difficult: different institutions and developers have different needs and formalise them in different ontologies. In a closed environment it is possible to force all the participants to share the same ontology, while in open and distributed environments ontology mapping can provide interoperability between heterogeneous interacting actors. However, conventional mapping systems focus on acquiring static information, and on mapping whole ontologies, which is infeasible in open systems. This thesis shows a different approach to the problem of heterogeneity. It starts from the intuitive idea that when similar situations arise, similar interactions are performed. If the interactions between actors are specified in formal scripts, shared by all the participants, then when the same situation arises, the same script is used. The main hypothesis that this thesis aims to demonstrate is that by analysing different runs of these scripts it is possible to create a statistical model of the interactions, that reflect the frequency of terms in messages and of ontological relations between terms in different messages. The model is then used during a run of a known interaction to compute the probability distribution for terms in received messages. The probability distribution provides additional information, contextual to the interaction, that can be used by a traditional ontology matcher in order to improve efficiency, by reducing the comparisons to the most likely ones given the context, and possibly both recall and precision, in particular helping disambiguation. The ability to create a model that reflects real phenomena in this sort of environment is evaluated by analysing the quality of the predictions, in particular verifying how various features of the interactions, such as their non-stationarity, affect the predictions. The actual improvements to a matcher we developed are also evaluated. The overall results are very promising, as using the predictor can lower the overall computation time for matching by ten times, while maintaining or in some cases improving recall and precision.

No description available.

De nombreux jeux de données sont publiés sur le web à l’aide des technologies du web sémantique. Ces jeux de données contiennent des données qui représentent des liens vers des ressources similaires. Si ces jeux de données sont liés entre eux par des liens construits correctement, les utilisateurs peuvent facilement interroger des données à travers une interface uniforme, comme s’ils interrogeaient un jeu de données unique. Mais, trouver des liens corrects est très difficile car de nombreuses comparaisons doivent être effectuées. Plusieurs solutions ont été proposées pour résoudre ce problème : (1) l’approche la plus directe est de comparer les valeurs d’attributs d’instances pour identifier les liens, mais il est impossible de comparer toutes les paires possibles de valeurs d’attributs. (2) Une autre stratégie courante consiste à comparer les instances selon les attribut correspondants trouvés par l’alignement d’ontologies à base d’instances, qui permet de générer des correspondances d’attributs basés sur des instances. Cependant, il est difficile d’identifier des instances similaires à travers les ensembles de données car,dans certains cas, les valeurs des attributs en correspondance ne sont pas les mêmes.(3) Plusieurs méthodes utilisent la programmation génétique pour construire des modèles d’interconnexion afin de comparer différentes instances, mais elles souffrent de longues durées d’exécution.Dans cette thèse, une méthode d’interconnexion est proposée pour relier les instances similaires dans différents ensembles de données, basée à la fois sur l’apprentissage statistique et sur l’apprentissage symbolique. L’entrée est constituée de deux ensembles de données, des correspondances de classes sur les deux ensembles de données et un échantillon de liens “positif” ou “négatif” résultant d’une évaluation de l’utilisateur. La méthode construit un classifieur qui distingue les bons liens des liens incorrects dans deux ensembles de données RDF en utilisant l’ensemble des liens d’échantillons évalués. Le classifieur est composé de correspondances d’attributs entre les classes correspondantes et de deux ensembles de données,qui aident à comparer les instances et à établir les liens. Le classifieur est appelé motif d’interconnexion dans cette thèse. D’une part, notre méthode découvre des correspondances potentielles entre d’attributs pour chaque correspondance de classe via une méthode d’apprentissage statistique : l’algorithme de regroupement K-medoids,en utilisant des statistiques sur les valeurs des instances. D’autre part, notre solution s’appuie sur un modèle d’interconnexion par une méthode d’apprentissage symbolique: l’espace des versions, basée sur les correspondances d’attributs potentielles découvertes et l’ensemble des liens de l’échantillon évalué. Notre méthode peut résoudre la tâche d’interconnexion quand il n’existe pas de motif d’interconnexion combiné qui couvre tous les liens corrects évalués avec un format concis.L’expérimentation montre que notre méthode d’interconnexion, avec seulement1% des liens totaux dans l’échantillon, atteint une F-mesure élevée (de 0,94 à 0,99). / There are many data sets being published on the web with Semantic Web technology. The data sets usually contain analogous data which represent the similar resources in the world. If these data sets are linked together by correctly identifying the similar instances, users can conveniently query data through a uniform interface, as if they are connecting a single database. However, finding correct links is very challenging because web data sources usually have heterogeneous ontologies maintained by different organizations. Many existing solutions have been proposed for this problem. (1) One straight-forward idea is to compare the attribute values of instances for identifying links, yet it is impossible to compare all possible pairs of attribute values. (2) Another common strategy is to compare instances with correspondences found by instance-based ontology matching, which can generate attribute correspondences based on overlapping ranges between two attributes, while it is easy to cause incomparable attribute correspondences or undiscovered comparable attribute correspondences. (3) Many existing solutions leverage Genetic Programming to construct interlinking patterns for comparing instances, however the running times of the interlinking methods are usually long. In this thesis, an interlinking method is proposed to interlink instances for different data sets, based on both statistical learning and symbolic learning. On the one hand, the method discovers potential comparable attribute correspondences of each class correspondence via a K-medoids clustering algorithm with instance value statistics. We adopt K-medoids because of its high working efficiency and high tolerance on irregular data and even incorrect data. The K-medoids classifies attributes of each class into several groups according to their statistical value features. Groups from different classes are mapped when they have similar statistical value features, to determine potential comparable attribute correspondences. The clustering procedure effectively narrows the range of candidate attribute correspondences. On the other hand, our solution also leverages a symbolic learning method, called Version Space. Version Space is an iterative learning model that searches for the interlinking pattern from two directions. Our design can solve the interlinking task that does not have a single compatible conjunctive interlinking pattern that covers all assessed correct links with a concise format. The interlinking solution is evaluated with large-scale real-world data from IM@OAEI and CKAN. Experiments confirm that the solution with only 1% of sample links already reaches a high accuracy (up to 0.94-0.99 on F-measure). The F-measure quickly converges improving on other state-of-the-art approaches, by nearly 10 percent of their F-measure values.

No description available.

Schema and ontology matching play an important part in the field of data integration and semantic web. Given two heterogeneous data sources, meta data matching usually constitutes the first step in the data integration workflow, which refers to the analysis and comparison of two input resources like schemas or ontologies. The result is a list of correspondences between the two schemas or ontologies, which is often called mapping or alignment. Many tools and research approaches have been proposed to automatically determine those correspondences. However, most match tools do not provide any information about the relation type that holds between matching concepts, for the simple but important reason that most common match strategies are too simple and heuristic to allow any sophisticated relation type determination. Knowing the specific type holding between two concepts, e.g., whether they are in an equality, subsumption (is-a) or part-of relation, is very important for advanced data integration tasks, such as ontology merging or ontology evolution. It is also very important for mappings in the biological or biomedical domain, where is-a and part-of relations may exceed the number of equality correspondences by far. Such more expressive mappings allow much better integration results and have scarcely been in the focus of research so far. In this doctoral thesis, the determination of the correspondence types in a given mapping is the focus of interest, which is referred to as semantic mapping enrichment. We introduce and present the mapping enrichment tool STROMA, which obtains a pre-calculated schema or ontology mapping and for each correspondence determines a semantic relation type. In contrast to previous approaches, we will strongly focus on linguistic laws and linguistic insights. By and large, linguistics is the key for precise matching and for the determination of relation types. We will introduce various strategies that make use of these linguistic laws and are able to calculate the semantic type between two matching concepts. The observations and insights gained from this research go far beyond the field of mapping enrichment and can be also applied to schema and ontology matching in general. Since generic strategies have certain limits and may not be able to determine the relation type between more complex concepts, like a laptop and a personal computer, background knowledge plays an important role in this research as well. For example, a thesaurus can help to recognize that these two concepts are in an is-a relation. We will show how background knowledge can be effectively used in this instance, how it is possible to draw conclusions even if a concept is not contained in it, how the relation types in complex paths can be resolved and how time complexity can be reduced by a so-called bidirectional search. The developed techniques go far beyond the background knowledge exploitation of previous approaches, and are now part of the semantic repository SemRep, a flexible and extendable system that combines different lexicographic resources. Further on, we will show how additional lexicographic resources can be developed automatically by parsing Wikipedia articles. The proposed Wikipedia relation extraction approach yields some millions of additional relations, which constitute significant additional knowledge for mapping enrichment. The extracted relations were also added to SemRep, which thus became a comprehensive background knowledge resource. To augment the quality of the repository, different techniques were used to discover and delete irrelevant semantic relations. We could show in several experiments that STROMA obtains very good results w.r.t. relation type detection. In a comparative evaluation, it was able to achieve considerably better results than related applications. This corroborates the overall usefulness and strengths of the implemented strategies, which were developed with particular emphasis on the principles and laws of linguistics.

Product information search has become one of the most important application areas of the Web. Especially considering pricey technical products, consumers tend to carry out intensive research activities previous to the actual acquisition for creating an all-embracing view on the product of interest. Federated search backed by ontology-based product information representation shows great promise for easing this research process. The topic of this thesis is to develop a comprehensive technique for locating, extracting, and integrating information of arbitrary technical products in a widely unsupervised manner. The resulting homogeneous information sets allow a potential consumer to effectively compare technical products based on an appropriate federated product information system. / Die Produktinformationssuche hat sich zu einem der bedeutendsten Themen im Web entwickelt. Speziell im Bereich kostenintensiver technischer Produkte führen potenzielle Konsumenten vor dem eigentlichen Kauf des Produkts langwierige Recherchen durch um einen umfassenden Überblick für das Produkt von Interesse zu erlangen. Die föderierte Suche in Kombination mit ontologiebasierter Produktinformationsrepräsentation stellt eine mögliche Lösung dieser Problemstellung dar. Diese Dissertation stellt Techniken vor, die das automatische Lokalisieren, Extrahieren und Integrieren von Informationen für beliebige technische Produkte ermöglichen. Die resultierenden homogenen Produktinformationen erlauben einem potenziellen Konsumenten, zugehörige Produkte effektiv über ein föderiertes Produktinformationssystem zu vergleichen.

The abundance of data at our disposal empowers data-driven applications and decision making. The knowledge captured in the data, however, has not been utilized to full potential, as it is only accessible to human interpretation and data are distributed in heterogeneous repositories. Ontologies are a key technology unlocking the knowledge in the data by providing means to model the world around us and infer knowledge implicitly captured in the data. As data are hosted by independent organizations we often need to use several ontologies and discover the relationships between them in order to support data and knowledge transfer. Broadly speaking, while ontologies provide formal representations and thus the basis, ontology alignment supplies integration techniques and thus the means to turn the data kept in distributed, heterogeneous repositories into valuable knowledge. While many automatic approaches for creating alignments have already been developed, user input is still required for obtaining the highest-quality alignments. This thesis focuses on supporting users during the cognitively intensive alignment process and makes several contributions. We have identified front- and back-end system features that foster user involvement during the alignment process and have investigated their support in existing systems by user interface evaluations and literature studies. We have further narrowed down our investigation to features in connection to the, arguably, most cognitively demanding task from the users’ perspective—manual validation—and have also considered the level of user expertise by assessing the impact of user errors on alignments’ quality. As developing and aligning ontologies is an error-prone task, we have focused on the benefits of the integration of ontology alignment and debugging. We have enabled interactive comparative exploration and evaluation of multiple alignments at different levels of detail by developing a dedicated visual environment—Alignment Cubes—which allows for alignments’ evaluation even in the absence of reference alignments. Inspired by the latest technological advances we have investigated and identified three promising directions for the application of large, high-resolution displays in the field: improving the navigation in the ontologies and their alignments, supporting reasoning and collaboration between users.

Schema and ontology matching play an important part in the field of data integration and semantic web. Given two heterogeneous data sources, meta data matching usually constitutes the first step in the data integration workflow, which refers to the analysis and comparison of two input resources like schemas or ontologies. The result is a list of correspondences between the two schemas or ontologies, which is often called mapping or alignment. Many tools and research approaches have been proposed to automatically determine those correspondences. However, most match tools do not provide any information about the relation type that holds between matching concepts, for the simple but important reason that most common match strategies are too simple and heuristic to allow any sophisticated relation type determination. Knowing the specific type holding between two concepts, e.g., whether they are in an equality, subsumption (is-a) or part-of relation, is very important for advanced data integration tasks, such as ontology merging or ontology evolution. It is also very important for mappings in the biological or biomedical domain, where is-a and part-of relations may exceed the number of equality correspondences by far. Such more expressive mappings allow much better integration results and have scarcely been in the focus of research so far. In this doctoral thesis, the determination of the correspondence types in a given mapping is the focus of interest, which is referred to as semantic mapping enrichment. We introduce and present the mapping enrichment tool STROMA, which obtains a pre-calculated schema or ontology mapping and for each correspondence determines a semantic relation type. In contrast to previous approaches, we will strongly focus on linguistic laws and linguistic insights. By and large, linguistics is the key for precise matching and for the determination of relation types. We will introduce various strategies that make use of these linguistic laws and are able to calculate the semantic type between two matching concepts. The observations and insights gained from this research go far beyond the field of mapping enrichment and can be also applied to schema and ontology matching in general. Since generic strategies have certain limits and may not be able to determine the relation type between more complex concepts, like a laptop and a personal computer, background knowledge plays an important role in this research as well. For example, a thesaurus can help to recognize that these two concepts are in an is-a relation. We will show how background knowledge can be effectively used in this instance, how it is possible to draw conclusions even if a concept is not contained in it, how the relation types in complex paths can be resolved and how time complexity can be reduced by a so-called bidirectional search. The developed techniques go far beyond the background knowledge exploitation of previous approaches, and are now part of the semantic repository SemRep, a flexible and extendable system that combines different lexicographic resources. Further on, we will show how additional lexicographic resources can be developed automatically by parsing Wikipedia articles. The proposed Wikipedia relation extraction approach yields some millions of additional relations, which constitute significant additional knowledge for mapping enrichment. The extracted relations were also added to SemRep, which thus became a comprehensive background knowledge resource. To augment the quality of the repository, different techniques were used to discover and delete irrelevant semantic relations. We could show in several experiments that STROMA obtains very good results w.r.t. relation type detection. In a comparative evaluation, it was able to achieve considerably better results than related applications. This corroborates the overall usefulness and strengths of the implemented strategies, which were developed with particular emphasis on the principles and laws of linguistics.

Product information search has become one of the most important application areas of the Web. Especially considering pricey technical products, consumers tend to carry out intensive research activities previous to the actual acquisition for creating an all-embracing view on the product of interest. Federated search backed by ontology-based product information representation shows great promise for easing this research process. The topic of this thesis is to develop a comprehensive technique for locating, extracting, and integrating information of arbitrary technical products in a widely unsupervised manner. The resulting homogeneous information sets allow a potential consumer to effectively compare technical products based on an appropriate federated product information system.:1. Introduction 1.1. Online Product Information Research 1.1.1. Current Online Product Information Research 1.1.2. Aspired Online Product Information Research 1.2. Federated Shopping Portals 1.3. Research Questions 1.4. Approach and Theses 1.4.1. Approach 1.4.2. Theses 1.4.3. Requirements 1.5. Goals and Non-Goals 1.5.1. Goals 1.5.2. Non-Goals 1.6. Contributions 1.7. Structure 2. Federated Information Systems 2.1. Information Access 2.1.1. Document Retrieval 2.1.2. Federated Search 2.1.3. Federated Ranking 2.2. Information Extraction 2.2.1. Information Extraction from Structured Sources 2.2.2. Information Extraction from Unstructured Sources 2.2.3. Information Extraction from Semi-structured Sources 2.3. Information Integration 2.3.1. Ontologies 2.3.2. Ontology Matching 2.4. Information Presentation 2.5. Product Information 2.5.1. Product Information Source Characteristics 2.5.2. Product Information Source Types 2.5.3. Product Information Integration Types 2.5.4. Product Information Types 2.6. Conclusions 3. A Federated Product Information System 3.1. Finding Basic Product Information 3.2. Enriching Product Information 3.3. Administrating Product Information 3.4. Displaying Product Information 3.5. Conclusions 4. Product Information Extraction from the Web 4.1. Vendor Product Information Search 4.1.1. Vendor Product Information Ranking 4.1.2. Vendor Product Information Extraction 4.2. Producer Product Information Search 4.2.1. Producer Product Document Retrieval 4.2.2. Producer Product Information Extraction 4.3. Third-Party Product Information Search 4.4. Conclusions 5. Product Information Integration for the Web 5.1. Product Representation 5.1.1. Domain Product Ontology 5.1.2. Application Product Ontology 5.1.3. Product Ontology Management 5.2. Product Categorization 5.3. Product Specifications Matching 5.3.1. General Procedure 5.3.2. Elementary Matchers 5.3.3. Evolutionary Matcher 5.3.4. Naïve Bayes Matcher 5.3.5. Result Selection 5.4. Product Specifications Normalization 5.4.1. Product Specifications Atomization 5.4.2. Product Specifications Value Normalization 5.5. Product Comparison 5.6. Conclusions 6. Evaluation 6.1. Implementation 6.1.1. Offers Service 6.1.2. Products Service 6.1.3. Snippets Service 6.1.4. Fedseeko 6.1.5. Fedseeko Browser Plugin 6.1.6. Fedseeko Mobile 6.1.7. Lessons Learned 6.2. Evaluation 6.2.1. Evaluation Measures 6.2.2. Gold Standard 6.2.3. Product Document Retrieval 6.2.4. Product Specifications Extraction 6.2.5. Product Specifications Matching 6.2.6. Comparison with Competitors 6.3. Conclusions 7. Conclusions and Future Work 7.1. Summary 7.2. Conclusions 7.3. Future Work A. Pseudo Code and Extraction Properties A.1. Pseudo Code A.2. Extraction Algorithm Properties A.2.1. Clustering Properties A.2.2. Purging Properties A.2.3. Dropping Properties B. Fedseeko Screenshots B.1. Offer Search B.2. Product Comparison / Die Produktinformationssuche hat sich zu einem der bedeutendsten Themen im Web entwickelt. Speziell im Bereich kostenintensiver technischer Produkte führen potenzielle Konsumenten vor dem eigentlichen Kauf des Produkts langwierige Recherchen durch um einen umfassenden Überblick für das Produkt von Interesse zu erlangen. Die föderierte Suche in Kombination mit ontologiebasierter Produktinformationsrepräsentation stellt eine mögliche Lösung dieser Problemstellung dar. Diese Dissertation stellt Techniken vor, die das automatische Lokalisieren, Extrahieren und Integrieren von Informationen für beliebige technische Produkte ermöglichen. Die resultierenden homogenen Produktinformationen erlauben einem potenziellen Konsumenten, zugehörige Produkte effektiv über ein föderiertes Produktinformationssystem zu vergleichen.:1. Introduction 1.1. Online Product Information Research 1.1.1. Current Online Product Information Research 1.1.2. Aspired Online Product Information Research 1.2. Federated Shopping Portals 1.3. Research Questions 1.4. Approach and Theses 1.4.1. Approach 1.4.2. Theses 1.4.3. Requirements 1.5. Goals and Non-Goals 1.5.1. Goals 1.5.2. Non-Goals 1.6. Contributions 1.7. Structure 2. Federated Information Systems 2.1. Information Access 2.1.1. Document Retrieval 2.1.2. Federated Search 2.1.3. Federated Ranking 2.2. Information Extraction 2.2.1. Information Extraction from Structured Sources 2.2.2. Information Extraction from Unstructured Sources 2.2.3. Information Extraction from Semi-structured Sources 2.3. Information Integration 2.3.1. Ontologies 2.3.2. Ontology Matching 2.4. Information Presentation 2.5. Product Information 2.5.1. Product Information Source Characteristics 2.5.2. Product Information Source Types 2.5.3. Product Information Integration Types 2.5.4. Product Information Types 2.6. Conclusions 3. A Federated Product Information System 3.1. Finding Basic Product Information 3.2. Enriching Product Information 3.3. Administrating Product Information 3.4. Displaying Product Information 3.5. Conclusions 4. Product Information Extraction from the Web 4.1. Vendor Product Information Search 4.1.1. Vendor Product Information Ranking 4.1.2. Vendor Product Information Extraction 4.2. Producer Product Information Search 4.2.1. Producer Product Document Retrieval 4.2.2. Producer Product Information Extraction 4.3. Third-Party Product Information Search 4.4. Conclusions 5. Product Information Integration for the Web 5.1. Product Representation 5.1.1. Domain Product Ontology 5.1.2. Application Product Ontology 5.1.3. Product Ontology Management 5.2. Product Categorization 5.3. Product Specifications Matching 5.3.1. General Procedure 5.3.2. Elementary Matchers 5.3.3. Evolutionary Matcher 5.3.4. Naïve Bayes Matcher 5.3.5. Result Selection 5.4. Product Specifications Normalization 5.4.1. Product Specifications Atomization 5.4.2. Product Specifications Value Normalization 5.5. Product Comparison 5.6. Conclusions 6. Evaluation 6.1. Implementation 6.1.1. Offers Service 6.1.2. Products Service 6.1.3. Snippets Service 6.1.4. Fedseeko 6.1.5. Fedseeko Browser Plugin 6.1.6. Fedseeko Mobile 6.1.7. Lessons Learned 6.2. Evaluation 6.2.1. Evaluation Measures 6.2.2. Gold Standard 6.2.3. Product Document Retrieval 6.2.4. Product Specifications Extraction 6.2.5. Product Specifications Matching 6.2.6. Comparison with Competitors 6.3. Conclusions 7. Conclusions and Future Work 7.1. Summary 7.2. Conclusions 7.3. Future Work A. Pseudo Code and Extraction Properties A.1. Pseudo Code A.2. Extraction Algorithm Properties A.2.1. Clustering Properties A.2.2. Purging Properties A.2.3. Dropping Properties B. Fedseeko Screenshots B.1. Offer Search B.2. Product Comparison