Collaborative tagging services provided by various social web sites become popular means to mark web resources for different purposes such as categorization, expression of a preference and so on. However, the tags are of syntactic nature, in a free style and do not reflect semantics, resulting in the problems of redundancy, ambiguity and less semantics. Current tag-based recommender systems mainly take the explicit structural information among users, resources and tags into consideration, while neglecting the important implicit semantic relationships hidden in tagging data. In this study, we propose a Semantic Enhancement Recommendation strategy (SemRec), based on both structural information and semantic information through a unified fusion model. Extensive experiments conducted on two real datasets demonstarte the effectiveness of our approaches.

The phenomenal growth of social media, both in scale and importance, has created a unique opportunity to track information diffusion and the spread of influence, but can also make efficient tracking difficult. Given data streams representing blog posts on multiple blog channels and a focal query post on some topic of interest, our objective is to predict which of those channels are most likely to contain a future post that is relevant, or similar, to the focal query post. We denote this task as the future author prediction problem (FAPP). This problem has applications in information diffusion for brand monitoring and blog channel personalization and recommendation. We develop prediction methods inspired by (naive) information retrieval approaches that use historical posts in the blog channel for prediction. We also train a ranking support vector machine (SVM) to solve the problem. We evaluate our methods on an extensive social media dataset; despite the difficulty of the task, all methods perform reasonably well. Results show that ranking SVM prediction can exploit blog channel and diffusion characteristics to improve prediction accuracy. Moreover, it is surprisingly good for prediction in emerging topics and identifying inconsistent authors.

Question and answer pairs in Community Question Answering (CQA) services are organized into hierarchical structures or taxonomies to facilitate users to find the answers for their questions conveniently. We observe that different CQA services have their own knowledge focus and used different taxonomies to organize their question and answer pairs in their archives. As there are no simple semantic mappings between the taxonomies of the CQA services, the integration of CQA services is a challenging task. The existing approaches on integrating taxonomies ignore the hierarchical structures of the source taxonomy. In this paper, we propose a novel approach that is capable of incorporating the parent-child and sibling information in the hierarchical structures of the source taxonomy for accurate taxonomy integration. Our experimental results with real world CQA data demonstrate that the proposed method significantly outperforms state-of-the-art methods.

We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed subproblems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms show that CCRank gains in both accuracy and efficiency.

Introspection is a question-led process in which one builds on what one already knows to explore what is possible and plausible. In creative introspection, whether in art or in science, framing the right question is as important as finding the right answer. Presupposition-laden questions are themselves a source of knowledge, and in this paper we show how widely-held beliefs about the world can be dynamically acquired by harvesting such questions from the Web. We show how metaphorical reasoning can be modeled as an introspective process, one that builds on questions harvested from the Web to pose further speculative questions and queries. Metaphor is much more than a knowledge-hungry rhetorical device: it is a conceptual lever that allows a system to extend its model of the world.

Latent relational search (LRS) is a novel approach for mapping knowledge across two domains. Given a source domain knowledge concerning the Moon, "The Moon is a satellite of the Earth," one can form a question {(Moon, Earth), (Ganymede, ?)} to query an LRS engine for new knowledge in the target domain concerning the Ganymede. An LRS engine relies on some supporting sentences such as ``Ganymede is a natural satellite of Jupiter.'' to retrieve and rank "Jupiter" as the first answer. This paper proposes cross-language latent relational search (CLRS) to extend the knowledge mapping capability of LRS from cross-domain knowledge mapping to cross-domain and cross-language knowledge mapping. In CLRS, the supporting sentences for the source pair might be in a different language with that of the target pair. We represent the relation between two entities in an entity pair by lexical patterns of the context surrounding the two entities. We then propose a novel hybrid lexical pattern clustering algorithm to capture the semantic similarity between paraphrased lexical patterns across languages. Experiments on Japanese-English datasets show that the proposed method achieves an MRR of 0.579 for CLRS task, which is comparable to the MRR of an existing monolingual LRS engine.

In current search engines, ranking functions are learned from a large number of labeled <query, URL> pairs in which the labels are assigned by human judges, describing how well the URLs match the different queries. However in commercial search engines, collecting high quality labels is time-consuming and labor-intensive. To tackle this issue, this paper studies how to produce the true relevance labels for  <query, URL> pairs using clickthrough data. By analyzing the correlations between query frequency, true relevance labels and users’ behaviors, we demonstrate that the users who search the queries with similar frequency have similar search intents and behavioral characteristics. Based on such properties, we propose an efficient discriminative parameter estimation in a multiple instance learning algorithm (MIL) to automatically produce true relevance labels for  <query, URL> pairs. Furthermore, we test our approach using a set of real world data extracted from a Chinese commercial search engine. Experimental results not only validate the effectiveness of the proposed approach, but also indicate that our approach is more likely to agree with the aggregation of the multiple judgments when strong disagreements exist in the panel of judges. In the event that the panel of judges is consensus, our approach provides more accurate automatic label results. In contrast with other models, our approach effectively improves the correlation between automatic labels and manual labels.

Nowadays web users have clearly expressed their wishes to receive and interact with personalized services directly. However, existing approaches, largely syntactic content-based, fail to provide robust, accurate and useful personalized services to its users. Towards such an issue, the semantic web provides technologies to annotate and match services’ descriptions with users’ features, interests and preferences, thus allowing for more efficient access to services and more generally information. The aim of our work, part of service personalization, is on automated instantiation of services which is crucial for advanced usability i.e., how to prepare and present services ready to be executed while limiting useless interactions with users? We introduce the constructive Description Logics reasoning join and couple it with concept abduction to i) identify useful parts of users profiles that satisfy services requirements and ii) compute the description required by a service to be executed but not provided by users profiles.

Web Service (WS) domains constitute an application field where automated planning can significantly contribute towards achieving customisable and adaptable compositions. Following the vision of using domain-independent planning and declarative complex goals to generate compositions based on atomic service descriptions, we apply a planning framework based on Constraint Satisfaction techniques to a domain consisting of WSs with diverse functionalities. One of the key requirements of such domains is the ability to address the incomplete knowledge problem, as well as recovering from failures that may occur during execution. We propose an algorithm for interleaving planning, monitoring and execution, where continual planning via altering the CSP is performed, under the light of the feedback acquired at runtime. The system is evaluated against a number of scenarios including real WSs, demonstrating the leverage of situations that can be effectively tackled with respect to previous approaches.

Query recommendation can not only effectively facilitate users to obtain their desired information but alsoincrease ads’ click-through rates. This paper presentsa general and highly efficient method for query recommendation. Given query sessions, we automatically generate many similar and dissimilar query-pairs as the prior knowledge. Then we learn a transformation from the prior knowledge to move similar queries closer such that similar queries tend to have similar hash values.This is formulated as minimizing the empirical error on the prior knowledge while maximizing the gap between the data and some partition hyperplanes randomly generated in advance. In the recommendation stage, we search queries that have similar hash values to the given query, rank the found queries and return the top K queries as the recommendation result. All the experimental results demonstrate that our method achieves encouraging results in terms of efficiency and recommendation performance.