One of the main challenges that the Semantic Web faces is the integration of a growing number of independently designed ontologies. In this work, we present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with some of the world's largest ontologies.

PARIS: Probabilistic Alignment of Relations, Instances, and Schema

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Computers And Intractability A Guide To The Theory Of Np Completeness

Recently, with the development of mobile Internet and smartphones, the online minimum bipartite matching in real time spatial data (OMBM) problem becomes popular. Specifically, given a set of service providers with specific locations and a set of users who dynamically appear one by one, the OMBM problem is to find a maximum-cardinality matching with minimum total distance following that once a user appears, s/he must be immediately matched to an unmatched service provider, which cannot be revoked, before subsequent users arrive. To address this problem, existing studies mainly focus on analyzing the worst-case competitive ratios of the proposed online algorithms, but study on the performance of the algorithms in practice is absent. In this paper, we present a comprehensive experimental comparison of the representative algorithms of the OMBM problem. Particularly, we observe a surprising result that the simple and efficient greedy algorithm, which has been considered as the worst due to its exponential worst-case competitive ratio, is significantly more effective than other algorithms. We investigate the results and further show that the competitive ratio of the worst case of the greedy algorithm is actually just a constant, 3.195, in the average-case analysis. We try to clarify a 25-year misunderstanding towards the greedy algorithm and justify that the greedy algorithm is not bad at all. Finally, we provide a uniform implementation for all the algorithms of the OMBM problem and clarify their strengths and weaknesses, which can guide practitioners to select appropriate algorithms for various scenarios.

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Online minimum matching in real-time spatial data: experiments and analysis

One of the most critical tasks for improving data quality and increasing the reliability of data analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to the same real-world entity. Despite several decades of research, ER remains a challenging problem. In this survey, we highlight the novel aspects of resolving Big Data entities when we should satisfy more than one of the Big Data characteristics simultaneously (i.e., Volume and Velocity with Variety). We present the basic concepts, processing steps, and execution strategies that have been proposed by database, semantic Web, and machine learning communities in order to cope with the loose structuredness, extreme diversity, high speed, and large scale of entity descriptions used by real-world applications. We provide an end-to-end view of ER workflows for Big Data, critically review the pros and cons of existing methods, and conclude with the main open research directions.

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An Overview of End-to-End Entity Resolution for Big Data

Online event-based social network (EBSN) platforms are becoming popular these days. An important task of managing EBSNs is to arrange proper social events to interested users. Existing approaches usually assume that each user only attends one event or ignore location information. The overall utility of such strategy is limited in real world: 1) each user may attend multiple events; 2) attending multiple events will incur spatio-temporal conflicts and travel expenses. Thus, a more intelligent EBSN platform that provides personalized event planning for each participant is desired. In this paper, we first formally define the problem of Utility-aware Social Event-participant Planning (USEP), which is proven to be NP-hard. To solve the USEP problem, we first devise a greedy-based heuristic algorithm, which performs fast under certain circumstances but has no approximation guarantee. We then present a two-step approximation framework, which not only guarantees a 1/2-approximation ratio but also includes a series of optimization techniques to improve its space/time efficiency. Finally, we verify the efficiency and effectiveness of the proposed methods through extensive experiments on real and synthetic datasets.

Utility-Aware Social Event-Participant Planning

With the popularity of mobile computing and social media, various kinds of online event-based social network (EBSN) platforms, such as Meetup, Plancast and Whova, are gaining in prominence. A fundamental task of managing EBSN platforms is to recommend suitable social events to potential users according to the following three factors: spatial locations of events and users, attribute similarities between events and users, and friend relationships among users. However, none of the existing approaches considers all the aforementioned influential factors when they recommend users to proper events. Furthermore, the existing recommendation strategies neglect the bottleneck cases of the global recommendation. Thus, it is impossible for the existing recommendation solutions to be fair in real-world scenarios. In this paper, we first formally define the problem of bottleneck-aware social event arrangement (BSEA), which is proven to be NP-hard. To solve the BSEA problem approximately, we devise two greedy heuristic algorithms, Greedy and Random+Greedy, and a local-search-based optimization technique. In particular, the Greedy algorithm is more effective but less efficient than the Random+Greedy algorithm in most cases. Moreover, a variant of the BSEA problem, called the Extended BSEA problem, is studied, and the above solutions can be extended to address this variant easily. Finally, we conduct extensive experiments on real and synthetic datasets which verify the efficiency and effectiveness of our proposed algorithms.

Bottleneck-aware arrangement over event-based social networks: the max-min approach

The semantic web has enabled the creation of a growing number of knowledge bases (KBs), which are designed independently using different techniques. Integration of KBs has attracted much attention as different KBs usually contain overlapping and complementary information. Automatic techniques for KB integration have been improved but far from perfect. Therefore, in this paper, we study the problem of knowledge base semantic integration using crowd intelligence. There are both classes and instances in a KB, in our work, we propose a novel hybrid framework for KB semantic integration considering the semantic heterogeneity of KB class structures. We first perform semantic integration of the class structures via crowdsourcing, then apply the blocking-based instance matching approach according to the integrated class structure. For class structure (taxonomy) semantic integration, the crowd is leveraged to help identifying the semantic relationships between classes to handle the semantic heterogeneity problem. Under the conditions of both large scale KBs and limited monetary budget for crowdsourcing, we formalize the class structure (taxonomy) semantic integration problem as a Local Tree Based Query Selection (LTQS) problem. We show that the LTQS problem is NP-hard and propose two greedy-based algorithms, i.e., static query selection and adaptive query selection. Furthermore, the KBs are usually of large scales and have millions of instances, direct pairwise-based instance matching is inefficient. Therefore, we adopt the blocking-based strategy for instance matching, taking advantage of the class structure (taxonomy) integration result. The experiments on real large scale KBs verify the effectiveness and efficiency of the proposed approaches.

Knowledge Base Semantic Integration Using Crowdsourcing

Recently, taxonomy has attracted much attention. Both automatic construction solutions and human-based computation approaches have been proposed. The automatic methods suffer from the problem of either low precision or low recall and human computation, on the other hand, is not suitable for large scale tasks. Motivated by the shortcomings of both approaches, we present a hybrid framework, which combines the power of machine-based approaches and human computation (the crowd) to construct a more complete and accurate taxonomy. Specifically, our framework consists of two steps: we first construct a complete but noisy taxonomy automatically, then crowd is introducedto adjust the entity positions in the constructed taxonomy. However, the adjustment is challenging as the budget (money) for asking the crowd is often limited. In our work, we formulatethe problem of finding the optimal adjustment as an entityselection optimization (ESO) problem, which is proved to beNP-hard. We then propose an exact algorithm and a moreefficient approximation algorithm with an approximation ratioof 1/2(1-1/e). We conduct extensive experiments on real datasets, the results show that our hybrid approach largely improves the recall of the taxonomy with little impairment for precision.

CrowdTC: Crowdsourced Taxonomy Construction

With the popularity of mobile computing and social media, various kinds of online event-based social network (EBSN) platforms, such as Meetup, Plancast and Whova, is gaining in prominence. A fundamental task of managing EBSN platforms is to recommend suitable social events to potential users according to the following three factors: distances between events and users, attribute similarities between events and users and friend relationships among users. However, none of existing approaches consider all aforementioned influential factors when they recommend users to proper events. Furthermore, existing recommendation strategies neglect the bottleneck cases on the global recommendation. Thus, it is impossible for the existing recommendation solutions to achieve the optimal utility in real-world scenarios. In this paper, we first formally define the problem of bottleneck-aware social event arrangement (BSEA), which is proven to be NP-hard. To solve the BSEA problem approximately, we devise two greedy-based heuristic algorithms, Greedy and Random+Greedy. In particular, the Random+Greedy algorithm is faster and more effective than the Greedy algorithm in most cases. Finally, we conduct extensive experiments on real and synthetic datasets which verify the efficiency and accuracy of our proposed algorithms.

On bottleneck-aware arrangement for event-based social networks

Knowledge base construction (KBC) has become a hot and in-time topic recently with the increasing application need of large-scale knowledge bases (KBs), such as semantic search, QA systems, the Google Knowledge Graph and IBM Watson QA System. Existing KBs mainly focus on encoding the factual facts of the world, e.g., city area and company product, which are regarded as the objective knowledge, whereas the subjective knowledge, which is frequently mentioned in Web queries, has been neglected. The subjective knowledge has no documented ground truth, instead, the truth relies on people's dominant opinion, which can be solicited from online crowd workers. In our work, we propose a KBC framework for subjective knowledge base construction taking advantage of the knowledge from the crowd and existing KBs. We develop a two-staged framework for subjective KB construction which consists of core subjective KB construction and subjective KB enrichment. Firstly, we try to build a core subjective KB mined from existing KBs, where every instance has rich objective properties. Then, we populate the core subjective KB with instances extracted from existing KBs, in which the crowd is leverage to annotate the subjective property of the instances. In order to optimize the crowd annotation process, we formulate the problem of subjective KB enrichment procedure as a cost-aware instance annotation problem and propose two instance annotation algorithms, i.e., adaptive instance annotation and batch-mode instance annotation algorithms. We develop a two-stage system for subjective KB construction which consists of core subjective KB construction and subjective knowledge enrichment. We evaluate our framework on real knowledge bases and a real crowdsourcing platform, the experimental results show that we can derive high quality subjective knowledge facts from existing KBs and crowdsourcing techniques through our proposed framework.

Rui Meng

Papers

Bottleneck-aware arrangement over event-based social networks: the max-min approach

Knowledge Base Semantic Integration Using Crowdsourcing

CrowdTC: Crowdsourced Taxonomy Construction

On bottleneck-aware arrangement for event-based social networks

Subjective Knowledge Base Construction Powered By Crowdsourcing and Knowledge Base