Explaining Propagators for String Edit Distance Constraints

The computation of string similarity measures has been thoroughly studied in the scientific literature and has applications in a wide variety of different areas. One of the most widely used measures is the so called string edit distance which captures the number of required edit operations to transform a string into another given string. Although polynomial time algorithms are known for calculating the edit distance between two strings, there also exist NP-hard problems from practical applications like scheduling or computational biology that constrain the minimum edit distance between arrays of decision variables. In this work, we propose a novel global constraint to formulate restrictions on the minimum edit distance for such problems. Furthermore, we describe a propagation algorithm and investigate an explanation strategy for an edit distance constraint propagator that can be incorporated into state of the art lazy clause generation solvers. Experimental results show that the proposed propagator is able to significantly improve the performance of existing exact methods regarding solution quality and computation speed for benchmark problems from the literature.

Cross-Platform User Profile Matching in Online Social Networks

Nowadays, it is common that people have several identities in different online social networks where their identities information is stored as user profiles. Matching cross-platform user profiles becomes a spotlight in the future research. In the paper, we propose a profile matching framework. Depending on the format of each field, different string similarity measures are adopted. Meanwhile, each fields importance is considered. At last, we evaluate the effectiveness of our proposed methods by experiments.

Matching XML Documents at Structural and Conceptual Level using Subtree Patterns

Knowledge matching is an important problem for many emerging applications in many areas including scientific knowledge management, ontology matching, e-commerce, and enterprise application integration. Matching the concepts of heterogeneous knowledge representations is very challenging due to the difficulty of taking contextual information into account and detecting complex matches. In this chapter, we describe a knowledge matching approach that uses subtree patterns to utilize structural information for matching at the conceptual and structural level. Initially, the algorithm does not take any syntactic information into account, but rather forms candidate mappings according to their structural/contextual relationships in the knowledge structures, which are then validated using online dictionaries and string similarity measures. The approach will then automatically extract the knowledge structure that is shared among all the matched knowledge representations. Experimental evaluation is performed on a number of real world XML schemas, which demonstrates the effectiveness of the proposed approach.