Web Information Systems and Technologies

In this paper we propose a query suggestion method for price comparison search engines. Query suggestion techniques are used for generating alternative queries to facilitate web users in information seeking; in this specific domain, suggestions provided to web users need to be properly generated taking into account that the suggested products must be still available for sale. We propose a novel approach based on a slightly variant of classical query-URL graphs: the query-product click-through bipartite graph. Information extracted both from search engine logs and specific domain features are exploited to build the graph, and one of the advantages of this model is that such a graph can be used to suggest not only related queries but also related products. Concepts used in the proposed method are not restricted to our context but are used in many other major e-commerce and search engine websites, we tested the model on several challenging datasets, and also compared with a recent query suggestion approach specifically designed for price comparison engines. Our solution outperforms the competing approach, achieving higher results in terms of relevance of the provided suggestions and coverage rates on top-8 suggestions.

The matchmaking is a crucial operation in Web service discovery and selection. The objective of the matchmaking is to discover and select the most appropriate Web service among the different available candidates. Different matchmaking frameworks are now available in the literature but most of them present at least one of the following shortcomings: (i) use of strict syntactic matching; (ii) use of capability-based matching; (iii) lack of customization support; and (iv) lack of accurate ranking of matching Web services. The objective of this paper is thus to present the design, implementation and evaluation of the Parameterized Matching-Ranking Framework (PMRF). The PMRF uses semantic matchmaking, accepts capability and property attributes, supports different levels of customization and generates a ranked list of Web services. Accordingly, it fully overcomes the first, third and fourth shortcomings enumerated earlier and partially addresses the second one. The PMRF is composed of two layers. The role of the first layer is to parse the input data and parameters and then transfer it to the second layer, which represents the matching and ranking engine. The comparison of PMRF to iSeM-logic-based and SPARQLent, using the OWLS-TC4 datasets, shows that the algorithms supported by PMRF outperform those proposed in iSeM-logic-based and SPARQLent.

Based on an increasing number of web resources and services, the mashup paradigm enables end users to create custom web applications consisting of several components in order to fulfill specific needs. End user development of such composite web applications poses tough challenges to composition platforms, especially with non-programmers as end users. For instance, communicating on a non-technical level is crucial. Furthermore, assistance is essential throughout the entire process, ranging from composition to usage of mashups. Amongst others, users should be supported by explaining inter-widget communication, by helping to understand a mashup’s functionality and by identifying mashups providing desired functionality. However, prevalent mashup solutions provide no or limited concepts regarding these aspects. In this paper, we introduce our proposal for formalizing and calculating the functionality of mashup compositions based on capabilities and communication relations of mashup components as well as semantic domain knowledge. It serves as a foundation for our assisted, capability-centered end user development approach within the CRUISE platform. The latter features several assistance mechanisms, like presenting the functionality of mashups and recommending composition steps. We describe a prototypical implementation of the proposed algorithm and discuss its usage in our platform. Additionally, we evaluate our modeling and algorithmic concepts by means of example applications and an expert evaluation.

We propose methods for ranking subtopics of a keyword query. Subtopics are also keyword queries which specialize and/or disambiguate search intent behind their original query. Information on subtopics are useful for search systems to generate diversified search results. Search result diversification is important when there are multiple ways to interpret the submitted query. In search result diversification, it is important to rank subtopics by their intent probabilities that users need information on the subtopics. Our subtopic ranking methods use hierarchical structure in documents in the corpus. Hierarchical structure in documents consists of nested logical blocks with headings. A heading describes the topic of a part of a document, and a block is such a part of a document. All our methods are based on two assumptions related to the structure. First, hierarchical headings in a document represent hierarchical topics discussed in the document. Second, authors write more contents about subtopics with higher intent probabilities. Based on these assumptions, our methods score each subtopic based on the total size of the blocks whose hierarchical headings represent the subtopic. We develop our methods in the following way. We first propose four methods to score a subtopic on a document, four methods to integrate subtopic scores on multiple documents, and two methods to sort subtopics based on their scores. We then combined these methods, which results in 32 subtopic ranking methods in total. We evaluated these methods on the data set for the subtopic mining subtask of the NTCIR-10 INTENT-2 task. The results indicated that our methods generated rankings statistically significantly better than the query completion snapshots by major commercial search engines.

The paper illustrates a system for the automatic classification of the sentiment orientation expressed into reviews written in Italian language. A proper stratification of linguistic resources is adopted in order to solve the lacking of an opinion lexicon specifically suited for the Italian language. Experiments show that the proposed system can be applied to a wide range of domains.

Personalized event recommendations are a challenging task. Unlike other items such as movies or restaurants, events often come with an expiration date. User ratings are usually not available before the event date and become dispensable after the event has taken place. In this work, we present the benefits and challenges of mobile and context-aware event recommender systems (RSs). We summarize basics and related work covering the most important requirements for developing event RSs. We developed a hybrid algorithm for context-aware event recommendations and integrated it into an Android prototype. Results of a two-week user study show that our RS provides useful recommendations. Based on our findings, we outline future challenges in the field of event recommendations: Improving the context-awareness, recommendations for different user and event types and an integration of event recommendations into city trip planners.

Support for collaborative work by software or web applications is well studied for years, but yet no approach exists which allow end-users with no or limited programming skills to build custom groupware applications for individual collaboration needs. Due to an increasing number of resources, APIs, and services within the web, creating new web applications nowadays can be simplified by just combining these atomic building blocks. Meanwhile, an increasing number of mashup platforms enable non-programmers to build situational web applications by their own by facilitating recommendation techniques and visual abstraction layers. But, none of these approaches cover sufficient support for multi-user scenarios. As one major foundation for collaboratively building and using composite web application (CWAs), we propose a triple-based permission management concept in line with a target group specific UI support. Thereby, users are empowered to share either applications, components or parts of them in the form of single application features or data. Additionally, previously selected private data can be excluded from being shared. We implemented the approach within our distributed runtime environment for CWAs and proved by two user studies that the basic concepts as well as the UI guidance work as expected.