NOUS: Construction and Querying of Dynamic Knowledge Graphs

The ability to construct domain specific knowledge graphs (KG) and perform question-answering or hypothesis generation is a transformative capability. Despite their value, automated construction of knowledge graphs remains an expensive technical challenge that is beyond the reach for most enterprises and academic institutions. We propose an end-to-end framework for developing custom knowledge graph driven analytics for arbitrary application domains. The uniqueness of our system lies A) in its combination of curated KGs along with knowledge extracted from unstructured text, B) support for advanced trending and explanatory questions on a dynamic KG, and C) the ability to answer queries where the answer is embedded across multiple data sources.Comment: Codebase: https://github.com/streaming-graphs/NOU

The Conditional Electoral Connection in the European Parliament

This paper introduces a model of the electoral connection in the European Parliament. Emphasizing the problem of common agency – wherein agents are beholden to multiple principals who cannot coordinate – it assumes that national parties, European party groups, and voters are “latent principals” that differentially constrain members of the European Parliament (MEPs). The model proposes that the degree to which each of these principals constrain MEPs depends upon signals that MEPs receive from the national political arena about their electoral vulnerability. Re-election seeking MEPs will in turn cultivate closer connections with the principal whose support is most important for reducing electoral vulnerability. Drawing on the second-order election model, signals about MEP vulnerability are measured as a national party‟s success in the most recent national election, given the party‟s average size, governing status, and time remaining until the European election. The model predicts three broad outcomes. First, MEPs from large or governing parties will generally be more vulnerable as their party label suffers in European elections. Expecting losses, they should cultivate closer connections to their constituents by emphasizing personal record rather than party affiliation. Second, MEPs from small or opposition parties will generally be less vulnerable as their party label is more successful in European elections. Expecting gains, these MEPs will seek to appeal to their party leaders in order to secure the safest (often only the top) place on the electoral list. Finally, the model predicts that systemic-level attributes such as voters‟ right to re-order the ballot should contribute to variation in the first two outcomes. The model‟s propositions are tested empirically with qualitative and quantitative evidence from 30 interviews with MEPs in 2008 and an original dataset of MEPs‟ non-roll-call position taking in plenary sessions during the 6th European Parliament term

Cluster-based information retrieval using pattern mining

This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information retrieval. For this purpose, a novel cluster-based information retrieval approach is proposed, named Cluster-based Retrieval using Pattern Mining (CRPM). This approach integrates various clustering and pattern mining algorithms. First, it generates clusters of objects that contain similar objects. Three clustering algorithms based on k-means, DBSCAN (Density-based spatial clustering of applications with noise), and Spectral are suggested to minimize the number of shared terms among the clusters of objects. Second, frequent and high-utility pattern mining algorithms are performed on each cluster to extract the pattern bases. Third, the clusters of objects are ranked for every query. In this context, two ranking strategies are proposed: i) Score Pattern Computing (SPC), which calculates a score representing the similarity between a user query and a cluster; and ii) Weighted Terms in Clusters (WTC), which calculates a weight for every term and uses the relevant terms to compute the score between a user query and each cluster. Irrelevant information derived from the pattern bases is also used to deal with unexpected user queries. To evaluate the proposed approach, extensive experiments were carried out on two use cases: the documents and tweets corpus. The results showed that the designed approach outperformed traditional and cluster-based information retrieval approaches in terms of the quality of the returned objects while being very competitive in terms of runtime.publishedVersio