Given the growing number of structured and evolving online repositories, the need for lightweight information integration has increased in the past years. We have developed an integration approach which relies on partial mappings for query rewriting and combines them with a controlled way of relaxation. In this paper we propose a novel approach for ranking results of such rewritten and relaxed ...

In this paper, deriving the fuzzy weights of criteria from the fuzzy pair-wise comparison matrix with triangular fuzzy elements is investigated. In the proposed method we first use the ranking function to convert the triangular fuzzy data into crisp one. Then by using the goal programming method we derive the fuzzy weights of criteria. We propose two new methods, and it is shown that these meth...

Ranking theory delivers an account of iterated contraction; each ranking function induces a specific iterated contraction behavior. The paper shows how to reconstruct a ranking function from its iterated contraction behavior uniquely up to multiplicative constant and thus how to measure ranks on a ratio scale. Thereby, it also shows how to completely axiomatize that behavior. The complete set o...

In Bayesian statistics probability distributions express beliefs. However, for many problems the beliefs cannot be computed analytically and approximations of beliefs are needed. We seek a ranking function that quantifies how “embarrassing” it is to communicate a given approximation. We show that there is only one ranking under the requirements that (1) the best ranked approximation is the non-...

In this paper we propose a way to find weights of a ranking function semi-automatically. From the manual choices made by (experienced) human test architects, our idea is to propose an optimization model that tries to find the necessary weights automatically. We present some experiments by encoding our optimization model in the Z3 SMT solver and using real Motorola Mobility1 data.

When two terms occur together in a document, the probability of a close relationship between them and the document itself is greater if they are in nearby positions. However, ranking functions including term proximity (TP) require larger indexes than traditional document-level indexing, which slows down query processing. Previous studies also show that this technique is not effective for all ty...

Rank breaking is a methodology introduced by Azari Soufiani et al. (2013a) for applying a Generalized Method of Moments (GMM) algorithm to the estimation of parametric ranking models. Breaking takes full rankings and breaks, or splits them up, into counts for pairs of alternatives that occur in particular positions (e.g., first place and second place, second place and third place). GMMs are of ...

In pseudo relevance feedback (PRF), the document weight which indicates how important a document is for the PRF model, plays a key role. In this paper, we investigate the smoothness issue of the document weights in PRF. The term smoothness means that the document weights decrease smoothly (i.e. gradually) along the document ranking list, and the weights are smooth (i.e. similar) within topicall...

We present a new method for the constraint-based synthesis of termination arguments for linear loop programs based on linear ranking templates. Linear ranking templates are parametrized, well-founded relations such that an assignment to the parameters gives rise to a ranking function. This approach generalizes existing methods and enables us to use templates for many different ranking functions...

In this paper, we present a framework for improving the ranking function training and the re-ranking process for web search personalization. Our method is based on utilizing clickthrough data from several users in order to create multiple ranking functions that correspond to different topic areas. Those ranking functions are combined each time a user poses a new query in order to produce a new ...