Bootstrapping Uncertainty in Schema Covering

Schema covering is the process of representing large and complex schemas by easily comprehensible common objects. This task is done by identifying a set of common concepts from a repository called concept repository and generating a cover to describe the schema by the concepts. Traditional schema covering approach has two shortcomings: it does not model the uncertainty in the covering process, and it requires user to state an ambiguity constraint which is hard to define. We remedy this problem by incorporating probabilistic model into schema covering to generate probabilistic schema cover. The integrated probabilities not only enhance the coverage of cover results but also eliminate the need of defining the ambiguity parameter. Experiments on real-datasets show the competitive performance of our approach

Collaboration in the Production of a Massively Multilingual Lexicon

This paper discusses the multiple approaches to collaboration that the Kamusi Project is employing in the creation of a massively multilingual lexical resource. The project’s data structure enables the inclusion of large amounts of rich data within each sense-specific entry, with transitive concept-based links across languages. Data collection involves mining existing data sets, language experts using an online editing system, crowdsourcing, and games with a purpose. The paper discusses the benefits and drawbacks of each of these elements, and the steps the project is taking to account for those. Special attention is paid to guiding crowd members with targeted questions that produce results in a specific format. Collaboration is seen as an essential method for generating large amounts of linguistic data, as well as for validating the data so it can be considered trustworthy

Probabilistic Schema Covering

Schema covering is the process of representing large and complex schemas by easily comprehensible common objects. This task is done by identifying a set of common concepts from a repository called concept repository and generating a cover to describe the schema by the concepts. Traditional schema covering approach has two shortcomings: it does not model the uncertainty in the covering process, and it requires user to state an ambiguity constraint which is hard to define. We remedy this problem by incorporating probabilistic model into schema covering to generate probabilistic schema cover. The integrated probabilities not only enhance the coverage of cover results but also eliminate the need of defining the ambiguity parameter. Both probabilistic schema covering and traditional schema covering run on top of a concept repository. Experiments on real-datasets show the competitive performance of our approach

Team Integration

We leverage theoretical advances and the multi-user nature of \emph{argumentation}. The overall contributions of our work are as follows. We model the schema matching network and the reconciliation process, where we relate the experts' assertions and the constraints of the matching network to an \emph{argumentation framework}. Our representation not only captures the experts' belief and their explanations, but also enables to reason about these captured inputs. On top of this representation, we develop support techniques for experts to detect conflicts in a set of their assertions. Then we guide the conflict resolution by offering two primitives: \emph{conflict-structure interpretation} and \emph{what-if analysis}. While the former presents meaningful interpretations for the conflicts and various heuristic metrics, the latter can greatly help the experts to understand the consequences of their own decisions as well as those of others. Last but not least, we implement an argumentation-based negotiation support tool for schema matching (ArgSM), which realizes our methods to help the experts in the collaborative task

Multilingual Lexicography with a Focus on Less-Resourced Languages: Data Mining, Expert Input, Crowdsourcing, and Gamification

This paper looks at the challenges that the Kamusi Project faces for acquiring open lexical data for less-resourced languages (LRLs), of a range, depth, and quality that can be useful within Human Language Technology (HLT). These challenges include accessing and reforming existing lexicons into interoperable data, recruiting language specialists and citizen linguists, and obtaining large volumes of quality input from the crowd. We introduce our crowdsourcing model, specifically (1) motivating participation using a “play to pay” system, games, social rewards, and material prizes; (2) steering the crowd to contribute structured and reliable data via targeted questions; and (3) evaluating participants’ input through crowd validation and statistical analysis to ensure that only trust-worthy material is incorporated into Kamusi’s master database. We discuss the mobile application Kamusi has developed for crowd participation that elicits high-quality structured data directly from each language’s speakers through narrow questions that can be answered with a minimum of time and effort. Through the integration of existing lexicons, expert input, and innovative methods of acquiring knowledge from the crowd, an accurate and reliable multilingual dictionary with a focus on LRLs will grow and become available as a free public resource

A Survey of Privacy on Data Integration

This survey is an integrated view of other surveys on privacy preserving for data integration. First, we review the database context and challenges and research questions. Second, we formulate the privacy problems for schema matching and data matching. Next, we introduce the elements of privacy models. Then, we summarize the existing privacy techniques and the analysis (proofs) of privacy guarantees. Finally, we describe the privacy frameworks and their applications

Crowdsourcing for Top-K Query Processing over Uncertain Data

Querying uncertain data has become a prominent application due to the proliferation of user-generated content from social media and of data streams from sensors. When data ambiguity cannot be reduced algorithmically, crowdsourcing proves a viable approach, which consists of posting tasks to humans and harnessing their judgment for improving the confidence about data values or relationships. This paper tackles the problem of processing top- K queries over uncertain data with the help of crowdsourcing for quickly converging to the realordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and contrasted on both synthetic and real data sets, with the aim of minimizing the crowd interactions necessary to find the realordering of the result set

Provenance-based Reconciliation In Conflicting Data

Data fusion is the process of resolving conflicting data from multiple data sources. As the data sources are inherently heterogenous, there is a need for an expert to resolve the conflicting data. Traditional approach requires the expert to resolve a considerable amount of conflicts in order to acquire a high quality dataset. In this project, we consider how to acquire a high quality dataset while maintaining the expert effort minimal. At first, we achieve this goal by building a model which leverages the provenance of the data in reconciling conflicting data. Secondly, we improve our model by taking the dependency between data sources into account. In the end, we empirically show that our solution can significantly reduce the user effort while it can obtain a high quality dataset in comparison with traditional method