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This paper presents a framework which combines data and text retrieval techniques to exercise and evaluate ontology based query expansions. We prepare by using linguistic techniques to identify query and document concepts, locating them in a ontologically defined semantic space. Expansions originate from the identified query concepts, with success determined by matching in the relevant document set. We identify three orthogonal dimensions that can affect query expansion success; relationship source, success measure technique, and query expansion technique. Expansion technique is further divided into six different categories: simple pruning, complex probability, voting, directional, semantic propagation, and multiple source concept. We describe each technique and show examples where they would be useful. The system architecture used facilitates plugging in of various expansion and evaluation routines, and flowing results from one method to the next. The system is useful for microanalysis of query expansion, discovering which components of ontological derived knowledge most influence query expansion success. In this work, we apply our framework to the medical domain.

This paper aims to focus on the design of algorithms and techniques for an effective set expansion. A tool that finds and extracts candidate sets of tuples from the World Wide Web was designed and implemented. For instance, when a given user provides <Indonesia, Jakarta, Indonesian Rupiah>, <China, Beijing, Yuan Renminbi>, <Canada, Ottawa, Canadian Dollar> as seeds, our system returns tuples composed of countries with their corresponding capital cities and currency names constructed from content extracted from Web pages retrieved.

The seeds are used to query a search engine and to retrieve relevant Web pages. The seeds are also used to infer wrappers from the retrieved pages. The wrappers, in turn, are used to extract candidates. The Web pages, wrappers, seeds and candidates, as well as their relationships, are vertices and edges of a heterogeneous graph. Several options for ranking candidates from PageRank to truth finding algorithms were evaluated and compared. Remarkably, all vertices are ranked, thus providing an integrated approach to not only answer direct set expansion questions but also find the most relevant pages to expand a given set of seeds.

The experimental results show that leveraging the truth finding algorithm can indeed improve the level of confidence in the extracted candidates and the sources.

Current approaches on set expansion mostly support sets of atomic data expansion. This idea can be extended to the sets of tuples and extract relation instances from the Web given a handful set of tuple seeds. A truth finding algorithm is also incorporated into the approach and it is shown that it can improve the confidence level in the ranking of both candidates and sources in set of tuples expansion.

The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR).

The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate).

The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|R_D) is able to erase the negative influences of error images in R_D.

Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach.

Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.

With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG.

The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method.

The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches.

Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.

The purpose of this paper is to explore the use of inverse local context analysis (ILCA) to obtain data from limited accessible data sources.

The experimental results show that the method the authors proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

The experimental results show that the method we proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

To the best of the authors’ knowledge, this paper provides the first attempt to gather all the retrieved documents from limited accessible data source, and the efficiency and ease of implementation of the proposed solution make it feasible for practical applications. The method the authors proposed can also benefit the construction of web corpus.

The purpose of this paper is to propose a framework and system to address the inability to discover new and authentic learning material and the lack of a single access point for search and browsing of remote learning object repositories (LORs).

The authors develop a framework for keyword-based query expansion using SKOS domain terminologies and implement a federated search mechanism integrating various disparate LORs within a learning management system (LMS).

The authors show that the expanded query achieves improved information gain and it is applied for federated information access, by simultaneously searching within a number of repositories. Results can be seamlessly aggregated back within the LMS and the course context.

It is possible to retrieve additional learning objects (LOs) and achieve a corresponding increase in recall, while maintaining precision. SKOS expansion behaves well in a scholarly setting, which, combined with federated search, can contribute toward LOs’ discovery at a balanced cost. The system can be easily integrated with other platforms as well, building on open standards and RESTful communication.

To the authors’ knowledge, this is the first time SKOS-based query expansion is applied in a federated setting, and for the discovery and alignment of learning objects residing within LORs. The results show that this approach can achieve considerable information gain and that it is possible to strike a balance between search effectiveness, query drift and performance.

Timely detection of emergency events and effective tracking of corresponding public opinions are critical in emergency management. As media are immediate sources of information on emergencies, the purpose of this paper is to propose cross-media analytics to detect and track emergency events and provide decision support for government and emergency management departments.

In this paper, a novel emergency event detection and opinion mining method is proposed for emergency management using cross-media analytics. In the proposed approach, an event detection module is constructed to discover emergency events based on cross-media analytics, and after the detected event is confirmed as an emergency event, an opinion mining module is used to analyze public sentiments and then generate public sentiment time series for early warning via a semantic expansion technique.

Empirical results indicate that a specific emergency can be detected and that public opinion can be tracked effectively and efficiently using cross-media analytics. In addition, the proposed system can be used for decision support and real-time response for government and emergency management departments.

This paper takes full advantage of cross-media information and proposes novel emergency event detection and opinion mining methods for emergency management using cross-media analytics. The empirical analysis results illustrate the efficiency of the proposed method.

The proposed method can be applied for detection of emergency events and tracking of public opinions for emergency decision support and governmental real-time response.

This research work contributes to the design of a decision support system for emergency event detection and opinion mining. In the proposed approaches, emergency events are detected by leveraging cross-media analytics, and public sentiments are measured using an auto-expansion of the domain dictionary in the field of emergency management to eliminate the misclassification of the general dictionary and to make the quantization more accurate.

This paper fills a gap in the research undertaken into the influence of organisational structure on entrepreneurial orientation and expansion performance. The expansion decision‐making process of an international hotel group was investigated. In‐depth interviews, observations and document analysis were used as the data collection techniques. Findings suggest that the concern about protecting and developing internationally recognised brands profitably caused the organisation to be remarkably centralised. A centralised decision‐making structure, however, not only slowed down the decision‐making process and limited international expansion, but also resulted in frustration and de‐motivation among the market‐based organisational members who were responsible for international expansion. The paper therefore concludes that organisations whose growth performance is highly dependent on having market‐based organisational members in diverse host country environments need to structure differently and bring about a new way of thinking to the management.

The purpose of this study is to analyse the magnetic field effect on Fe₃O₄/H₂O Ferrofluid flowing in a sudden expansion tube, which has specific behaviour in terms of rheology, with convex dimple fins. Because the investigation of flow separation is a prominent application in performance, the effect of magnetic field and convex dimple on the thermo-hydraulic performance of sudden expansion tube are examined, in detail.

During the solution of the boundary conditions of the sudden expansion tube, finite volume method was used. Analyses have been conducted considering the single-phase solution, steady-state, incompressible fluid and no-slip condition of the wall under forced convection conditions. In the analyses, it has been assumed that the flow was developing thermally and has been fully developed hydrodynamically.

The present study focuses on exploring the influence of the magnetic field, nanofluid concentration and convex dimple fins on the thermo-hydraulic performance of sudden expansion tube. The results indicate that the strength of the magnetic field, nanofluid concentration and convex dimple fins have a positive effect on the convective heat transfer in the system.

The authors conducted numerical studies, determining through a literature search that no one had yet investigated enhancing heat transfer on a sudden expansion tube using combinations of magnetic fields, nanofluids and convex dimple fins. The results of the numerical analyses provide valuable information about the improvement of heat transfer and system performance in electronic device cooling and heat exchangers.

To discuss the entrepreneurial role and importance of organisational members in implementing franchising decisions internationally.

A single embedded case study of an international hotel firm was the focus of the enquiry. Interviews, observations and document analysis were used as the data collection techniques.

Findings suggest that Development Directors, as the organisational members in host country markets, display many characteristics of entrepreneurial salespeople in the process of selling the franchise system. However, they face organisational obstacles which mean that they cannot fully exploit their entrepreneurial effort. The paper concludes by suggesting that it is Development Directors, as opposed to senior decision makers, who possess an international orientation and are therefore crucial to the internationalisation process of the firm. In addition, there is an indication that there is a relationship between entrepreneurial efforts within sales and the performance of a firm.

The hotel industry has been chosen as the focus of the study, and the comparability with other industries is therefore questionable. Although the research findings have not indicated any substantial differences in terms of the international management of the hotel firms and other types of organisations, the findings of this research cannot be generalised.

This paper both illuminates the challenges franchise organisations face internationally and highlights the options that companies may consider as part of their growth strategies.

This paper illustrates the role and importance of organisational entrepreneurs in the international expansion process and sheds new light on the implementation of franchising decisions internationally. It identifies an important factor in the operationalisation of international franchising – the need for entrepreneurial sales people in the franchise‐selling procedure.