Enriching videos with light semantics

This paper describes an ongoing prototypical framework to annotate and retrieve web videos with light semantics. The proposed framework reuses many existing vocabularies along with a video model. The knowledge is captured from three different information spaces (media content, context, document). We also describe ways to extract the semantic content descriptions from the existing usergenerated content using multiple approaches of linguistic processing and Named Entity Recognition, which are later identified with DBpedia resources to establish meanings for the tags. Finally, the implemented prototype is described with multiple search interfaces and retrieval processes. Evaluation on semantic enrichment shows a considerable (50% of videos) improvement in content description

Investigating web APIs on the World Wide Web

Abstract—The world of services on the Web, thus far limited to “classical ” Web services based on WSDL and SOAP, has been increasingly marked by the domination of Web APIs, characterised by their relative simplicity and their natural suitability for the Web. Currently, the development of Web APIs is rather autonomous, guided by no established standards or rules, and Web API documentation is commonly not based on an interface description language such as WSDL, but is rather given directly in HTML as part of a webpage. As a result, the use of Web APIs requires extensive manual effort and the wealth of existing work on supporting common service tasks, including discovery, composition and invocation, can hardly be reused or adapted to APIs. Before we can achieve a higher level of automation and can make any significant improvement to current practices and technologies, we need to reach a deeper understanding of these. Therefore, in this paper we present a thorough analysis of the current landscape of Web API forms and descriptions, which has up-to-date remained unexplored. We base our findings on manually examining a body of publicly available APIs and, as a result, provide conclusions about common description forms, output types, usage of API parameters, invocation support, level of reusability, API granularity and authentication details. The collected data provides a solid basis for identifying deficiencies and realising how we can overcome existing limitations. More importantly, our analysis can be used as a basis for devising common standards and guidelines for Web API development. Keywords-Web APIs, RESTful services, Web services I

SMS: A Framework for Service Discovery by Incorporating Social Media Information

© 2008-2012 IEEE. With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches

Ranking web services using centralities and social indicators

Nowadays, developers of web application mashups face a sheer overwhelming variety and pluralism of web services. Therefore, choosing appropriate web services to achieve specific goals requires a certain amount of knowledge as well as expertise. In order to support users in choosing appropriate web services it is not only important to match their search criteria to a dataset of possible choices but also to rank the results according to their relevance, thus minimizing the time it takes for taking such a choice. Therefore, we investigated six ranking approaches in an empirical manner and compared them to each other. Moreover, we have had a look on how one can combine those ranking algorithms linearly in order to maximize the quality of their outputs

Feature LDA: a supervised topic model for automatic detection of Web API documentations from the Web

Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labelled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models

Experiencing OptiqueVQS: A Multi-paradigm and Ontology-based Visual Query System for End Users

This is author's post-print version, published version available on http://link.springer.com/article/10.1007%2Fs10209-015-0404-5Data access in an enterprise setting is a determining factor for value creation processes, such as sense-making, decision-making, and intelligence analysis. Particularly, in an enterprise setting, intuitive data access tools that directly engage domain experts with data could substantially increase competitiveness and profitability. In this respect, the use of ontologies as a natural communication medium between end users and computers has emerged as a prominent approach. To this end, this article introduces a novel ontology-based visual query system, named OptiqueVQS, for end users. OptiqueVQS is built on a powerful and scalable data access platform and has a user-centric design supported by a widget-based flexible and extensible architecture allowing multiple coordinated representation and interaction paradigms to be employed. The results of a usability experiment performed with non-expert users suggest that OptiqueVQS provides a decent level of expressivity and high usability and hence is quite promising

A New Web Search Engine with Learning Hierarchy

Most of the existing web search engines (such as Google and Bing) are in the form of keyword-based search. Typically, after the user issues a query with the keywords, the search engine will return a flat list of results. When the query issued by the user is related to a topic, only the keyword matching may not accurately retrieve the whole set of webpages in that topic. On the other hand, there exists another type of search system, particularly in e-Commerce web- sites, where the user can search in the categories of different faceted hierarchies (e.g., product types and price ranges). Is it possible to integrate the two types of search systems and build a web search engine with a topic hierarchy? The main diffculty is how to classify the vast number of webpages on the Internet into the topic hierarchy. In this thesis, we will leverage machine learning techniques to automatically classify webpages into the categories in our hierarchy, and then utilize the classification results to build the new search engine SEE. The experimental results demonstrate that SEE can achieve better search results than the traditional keyword-based search engine in most of the queries, particularly when the query is related to a topic. We also conduct a small-scale usability study which further verifies that SEE is a promising search engine. To further improve SEE, we also propose a new active learning framework with several novel strategies for hierarchical classification

hRESTS: An HTML microformat for describing RESTful web services

The Web 2.0 wave brings, among other aspects, the programmable Web: increasing numbers of Web sites provide machine-oriented APIs and Web services. However, most APIs are only described with text in HTML documents. The lack of machine-readable API descriptions affects the feasibility of tool support for developers who use these services. We propose a microformat called hRESTS (HTML for RESTful Services) for machine-readable descriptions of Web APIs, backed by a simple service model. The hRESTS microformat describes main aspects of services, such as operations, inputs and outputs. We also present two extensions of hRESTS: SA-REST, which captures the facets of public APIs important for mashup developers, and MicroWSMO, which provides support for semantic automation