Uncertainty in Data Integration Systems: Automatic Generation of Probabilistic Relationships

Bergamaschi, Sonia; Po, Laura; Sorrentino, Serena; Corni, Alberto

doi:10.1007/978-3-7908-2404-9_26

Cited by 3 publications

References 9 publications

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PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation Using Probabilistic Methods

Abeyruwan

Visser

Lemmon

et al. 2013

Uncertainty Reasoning for the Semantic Web II

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Abstract.Formalizing an ontology for a domain manually is well-known as a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, researchers developed algorithms and systems that can help to automatize the process. Among them are systems that include text corpora for the acquisition. Our idea is also based on vast amount of text corpora. Here, we provide a novel unsupervised bottom-up ontology generation method. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. We provide a quantitative and two qualitative results illustrating our approach using a high throughput screening assay corpus and two custom text corpora. This process could also provide evidence for domain experts to build ontologies based on top-down approaches.

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PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation Using Probabilistic Methods

Abeyruwan

Visser

Lemmon

et al. 2013

Uncertainty Reasoning for the Semantic Web II

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Exploratory search framework for Web data sources

et al. 2013

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Exploratory search is an information seeking behavior where users progressively learn about one or more topics of interest; it departs quite radically from traditional keyword-based query paradigms, as it combines querying and browsing of resources, and covers activities such as investigating, evaluating, comparing, and synthesizing retrieved information. In most cases, such activities are enabled by a conceptual description of information in terms of entities and their semantic relationships. Customized Web applications, where few applicative entities and their relationships are embedded within the application logics, typically provide some support to exploratory search, which is, however, specific for a given domain. In this paper, we describe a general-purpose exploratory search framework, i.e., a framework which is neutral to the application logic. Our contribution consists of the formalization of the exploratory search paradigm over Web data sources, accessed by means of services; extracted information is described by means of an entity-relationship schema, which masks the service implementations. Exploratory interaction is supported by a general-purpose user interface including a set of widgets for data exploration, from big tables to atomic tables, visual diagrams, and geographic maps; the user interaction is translated to queries defined in SeCoQL , a SQL-like language and protocol specifically designed for supporting exploratory search over data sources. We illustrate the software architecture of our prototype, which uses the interplay of a query and result management system with an orchestrator, capable of incrementally building queries and of walking through the past navigation history. The distinctive feature of the framework is the ability to extract top solutions, which combine top-ranked entity instances. We evaluate exploratory search from the end-user perspective in the context of a cognitive model for search, by studying the user’s behavior and the effectiveness of exploratory search in terms of quality of results produced by the search process; we also compare the effectiveness of interaction in using our multi-domain search system with the use of various replicas of the system, each acting upon a single domain, and with the use of conventional search engines

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A bottom-up, knowledge-aware approach to integrating and querying web data services

2013

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As a wealth of data services is becoming available on the Web, building and querying Web applications that effectively integrate their content is increasingly important. However, schema integration and ontology matching with the aim of registering data services often requires a knowledge-intensive, tedious, and error-prone manual process. We tackle this issue by presenting a bottom-up, semi-automatic service registration process that refers to an external knowledge base and uses simple text processing techniques in order to minimize and possibly avoid the contribution of domain experts in the annotation of data services. The first by-product of this process is a representation of the domain of data services as an entity-relationship diagram, whose entities are named after concepts of the external knowledge base matching service terminology rather than being manually created to accommodate an application-specific ontology. Second, a three-layer annotation of service semantics (service interfaces, access patterns, service marts) describing how services “play” with such domain elements is also automatically constructed at registration time. When evaluated against heterogeneous existing data services and with a synthetic service dataset constructed using Google Fusion Tables, the approach yields good results in terms of data representation accuracy. We subsequently demonstrate that natural language processing methods can be used to decompose and match simple queries to the data services represented in three layers according to the preceding methodology with satisfactory results. We show how semantic annotations are used at query time to convert the user's request into an executable logical query. Globally, our findings show that the proposed registration method is effective in creating a uniform semantic representation of data services, suitable for building Web applications and answering search queries.

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Uncertainty in Data Integration Systems: Automatic Generation of Probabilistic Relationships

Cited by 3 publications

References 9 publications

PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation Using Probabilistic Methods

PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation Using Probabilistic Methods

Exploratory search framework for Web data sources

A bottom-up, knowledge-aware approach to integrating and querying web data services

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