The next decade will see an abundance of new intelligent systems, many of which will be market-based. Soon, users will interact with many new markets, perhaps without even knowing it: when driving their car, when listening to a song, when backing up their files, or when surfing the web. We argue that these new systems can only be successful if a new approach is chosen towards designing them. In this paper we introduce the general problem of "Hidden Market Design." The design of a "weakly hidden" market involves reducing some of the market complexities and providing a user interface (UI) that makes the interaction seamless for the user. A "strongly hidden market" is one where some semantic aspect of a market is hidden altogether (e.g., budgets, prices, combinatorial constraints). We show that the intersection of UI design and market design is of particular importance for this research agenda. To illustrate hidden market design, we give a series of potential applications. We hope that the problem of hidden market design will inspire other researchers and lead to new research in this direction, paving the way for more successful market-based systems in the future.

Constraint programming can be divided very crudely into modeling and solving. Modeling defines the problem, in terms of variables that can take on different values, subject to restrictions (constraints) on which combinations of variables are allowed. Solving finds values for all the variables that simultaneously satisfy all the constraints. However, the impact of constraint programming has been constrained by a lack of "user-friendliness''. Constraint programming has a major "declarative" aspect, in that a problem model can be handed off for solution to a variety of standard solving methods. These methods are embedded in algorithms, libraries, or specialized constraint programming languages. To fully exploit this declarative opportunity however, we must provide more assistance and automation in the modeling process, as well as in the design of application-specific problem solvers. Automated modelling and solving in constraint programming presents a major challenge for the artificial intelligence community. Artificial intelligence, and in particular machine learning, is a natural field in which to explore opportunities for moving more of the burden of constraint programming from the user to the machine. This paper presents technical challenges in the areas of constraint model acquisition, formulation and reformulation, synthesis of filtering algorithms for global constraints, and automated solving. We also present the metrics by which success and progress can be measured.

Good and generous knowledge sources, reliable and efficient induction patterns, and automatic and controllable quality assertion approaches are three critical issues to commonsense knowledge (CSK) acquisition. This paper employs Open Mind Common Sense (OMCS), a volunteers-contributed CSK database, to study the first and the third issues. For those stylized CSK, our result shows that over 40% of CSK for four predicate types in OMCS can be found in the web, which contradicts to the assumption that CSK is not communicated in texts. Moreover, we propose a commonsense knowledge classifier trained from OMCS, and achieve high precision in some predicate types, e.g., 82.6% in HasProperty. The promising results suggest new ways of analyzing and utilizing volunteer-contributed knowledge to design systems automatically mining commonsense knowledge from the web.

Burst detection is an important topic in temporal stream analysis. Usually, only the textual features are used in burst detection. In the theme extraction from current prevailing social media content, it is necessary to consider not only textual features but also the pervasive collaborative context, e.g., resource lifetime and user activity. This paper explores novel approaches to combine multiple sources of such indication for better burst extraction. We systematically investigate the characters of collaborative context, i.e., metadata frequency, topic coverage and user attractiveness. First, a robust state based model is utilized to detect bursts from individual streams. We then propose a learning method to combine these burst pulses. Experiments on a large real dataset demonstrate the remarkable improvements over the traditional methods.

Top- k approximate querying on string collections is an important data analysis tool for many applications, and it has been exhaustively studied. However, the scale of the problem has increased dramatically because of the prevalence of the Web. In this paper, we aim to explore the efficient top- k similar string matching problem. Several efficient strategies are introduced, such as length aware and adaptive q -gram selection. We present a general q -gram based framework and propose two efficient algorithms based on the strategies introduced. Our techniques are experimentally evaluated on three real data sets and show a superior performance.

In the form of topic discussions, users interact with each other to share knowledge and exchange information in online forums. Modeling the evolution of topic discussion reveals how information propagates on Internet and can thus help understand sociological phenomena and improve the performance of applications such as recommendation systems. In this paper, we argue that a user’s participation in topic discussions is motivated by either her friends or her own preferences. Inspired by the theory of information flow, we propose dynamic topic discussion models by mining influential relationships between users and individual preferences. Reply relations of users are exploited to construct the fundamental influential social network. The property of discussed topics and time lapse factor are also considered in our modeling. Furthermore, we propose a novel measure called ParticipationRank to rank users according to how important they are in the social network and to what extent they prefer to participate in the discussion of a certain topic. The experiments show our model can simulate the evolution of topic discussions well and predict the tendency of user’s participation accurately.

Self-publication of news on Web sites is becoming a common application platform to enable more engaging interaction among users. Discussion in the form of comments following news postings can be effectively facilitated if the service provider can recommend articles based on not only the original news itself but also the thread of changing comments. This turns the traditional news recommendation to a "discussion moderator" that can intelligently assist online forums. In this work, we present a framework to implement such adaptive news recommendation. In addition, to alleviate the problem of recommending essentially identical articles, the relationship (duplication, generalization or specialization) between suggested news articles and the original posting is investigated. Experiments indicate that our proposed solutions provide an enhanced news recommendation service in forum-based social media.

In many data-centric semantic web applications, it is desirable to use OWL to encode the Integrity Constraints (IC) that must be satisfied by instance data. However, challenges arise due to the Open World Assumption (OWA) and the lack of a Unique Name Assumption (UNA) in OWL’s standard semantics. In particular, conditions that trigger constraint violations in systems using the ClosedWorld Assumption (CWA), will generate new inferences in standard OWL-based reasoning applications. In this paper, we present an alternative IC semantics for OWL that allows applications to work with the CWA and the weak UNA. Ontology modelers can choose which OWL axioms to be interpreted with our IC semantics. Thus application developers are able to combine open world reasoning with closed world constraint validation in a flexible way. We also show that IC validation can be reduced to query answering under certain conditions. Finally, we describe our prototype implementation based on the OWL reasoner Pellet.

We describe a generic framework for representing and reasoning with annotated Semantic Web data, formalise the annotated language, the corresponding deductive system, and address the query answering problem. We extend previous contributions on RDF annotations by providing a unified reasoning formalism and allowing the seamless combination of different annotation domains. We demonstrate the feasibility of our method by instantiating it on (i) temporal RDF; (ii) fuzzy RDF; (iii) and their combination. A prototype shows that implementing and combining new domains is easy and that RDF stores can easily be extended to our framework.

Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and provide a formalization of the ontology matching problem within the framework. The approach has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of a-priori confidence values. We show empirically that the approach is efficient and more accurate than existing matchers on an established ontology alignment benchmark dataset.