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The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.

The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.

The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.

In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.

The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

This study aims to examine nature-based tourists’ experiences in tree houses using user-generated content in Web reviews. The research objectives were to identify the main dimensions of tree house experiences and test whether these dimensions vary according to different traveller-type market segments.

A sample of 722 Web reviews was analysed using mixed content analysis methods. Leximancer software provided computer-assisted qualitative data analysis that identified the main themes, after which further qualitative analysis identified the key narratives associated with experiences.

The results reveal that tourists are extremely satisfied with their stays in tree houses. The main themes that encompass the dominant narratives are tree house, Costa Rica, staff, morning, walk, wildlife, rainforest, opportunity to learn, trip, experience and recommendation. The narratives vary according to type of traveller.

The valuable insights gained emphasise the advantages of using user-generated content in tourism studies. The results also offer a better understanding of the key dimensions of this type of nature-based tourism, including a graphic representation of the main themes and concepts in guests’ narratives. In addition, the findings emphasise that nature-based tourists are not a homogeneous group and that they can be segmented according to type of traveller.

The purpose of this paper is to investigate the linking of a folksonomy (user vocabulary) and LCSH (controlled vocabulary) on the basis of word matching, for the potential use of LCSH in bringing order to folksonomies.

A selected sample of a folksonomy from a popular collaborative tagging system, Delicious, was word‐matched with LCSH. LCSH was transformed into a tree structure called an LCSH tree for the matching. A close examination was conducted on the characteristics of folksonomies, the overlap of folksonomies with LCSH, and the distribution of folksonomies over the LCSH tree.

The experimental results showed that the total proportion of tags being matched with LC subject headings constituted approximately two‐thirds of all tags involved, with an additional 10 percent of the remaining tags having potential matches. A number of barriers for the linking as well as two areas in need of improving the matching are identified and described. Three important tag distribution patterns over the LCSH tree were identified and supported: skewedness, multifacet, and Zipfian‐pattern.

The results of the study can be adopted for the development of innovative methods of mapping between folksonomy and LCSH, which directly contributes to effective access and retrieval of tagged web resources and to the integration of multiple information repositories based on the two vocabularies.

The linking of controlled vocabularies can be applicable to enhance information retrieval capability within collaborative tagging systems as well as across various tagging system information depositories and bibliographic databases.

This is among frontier works that examines the potential of linking a folksonomy, extracted from a collaborative tagging system, to an authority‐maintained subject heading system. It provides exploratory data to support further advanced mapping methods for linking the two vocabularies.

With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it does not offer any semantic support for the stored data which can facilitate data access for the users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDBs is not widespread due to their deficiencies in understanding natural language (NL) queries. In this sense, the purpose of this paper is to propose a novel method for an intelligent understanding of NL queries using semantically enriched database sources.

First a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing NL queries uses the stored semantics to generate a semantic tree.

The evaluation part of the work shows the advantages of using a semantically enriched database source to understand NL queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a NL query.

Existing NLIDBs are not yet a standard option for interfacing a relational database due to their lack for understanding NL queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying the NL elements by their semantic nature in order to generate a semantic tree. This last is a key solution towards an intelligent understanding of NL queries to relational databases.

In response to the scarcity of research on Islamic corporate social responsibility (CSR), the purpose of this paper is to explore how young Saudi consumers perceive CSR from an Islamic perspective. The study is focused on Saudi Arabia, a Muslim country, and the largest exporter of petroleum in the world.

The present study uses a qualitative methodology with 34 in-depth interviews undertaken in two major Saudi cities (Tabouk and Riyadh).

Utilising social contract theory, this study contributes to the literature by developing an Islamic “CSR Tree” model (which consists of three parts: “roots”, “trunk”, and “crown”) to increase the understanding of Islamic CSR (i-CSR) and consumer behaviour. The roots are hidden, while the trunk and crown are visible. In particular, private social responsibility (the roots of the CSR Tree) incorporating Sadaqa, or values and intention, is the fundamental component on which organisations should base their CSR strategy from an Islamic perspective. The study also reveals that internal, external, and private social responsibilities are connected, and all are dependent on each other. The higher the level of private social responsibility exhibited, the higher the level of external social responsibility.

According to the CSR Tree model presented in this study, an organisation should avoid Riya (showing off) as this would represent shirk or idolatry, which is the opposite of Tawhid. The findings are particularly relevant for advancing the concept of i-CSR and for considering complex perspectives less travelled in the CSR literature. The study suggests that the best strategy for an organisation wishing to pursue an i-CSR agenda would be to balance internal and external responsibilities, and to bear in mind that private responsibility should be the motivation for action, and that CSR should be applied for the benefit of society.

This paper aims to focus on automatic selection of two important structural concepts required in an XML query, namely, target and constraint concepts, when given a keywords query. Due to the diversities of concepts used in XML resources, it is not easy to select a correct concept when constructing an XML query.

In this paper, a Context-based Term Weighting model that performs term weighting based on part of documents. Each part represents a specific context, thus offering better capturing of concept and term relationship. For query time analysis, a Query Context Graph and two algorithms, namely, Select Target and Constraint (QC) and Select Target and Constraint (QCAS) are proposed to find the concepts for constructing XML query.

Evaluations were performed using structured document for conference domain. For constraint concept selection, the approach CTX+TW achieved better result than its baseline, NCTX, when search term has ambiguous meanings by using context-based scoring for the concepts. CTX+TW also shows its stability on various scoring models like BM25, TFIEF and LM. For target concept selection, CTX+TW outperforms the standard baseline, SLCA, whereas it also records higher coverage than FCA, when structural keywords are used in query.

The idea behind this approach is to capture the concepts required for term interpretation based on parts of the collections rather than the entire collection. This allows better selection of concepts, especially when a structured XML document consists many different types of information.

The purpose of this paper is to provide instructional guidance on how to increase validity and reduce subjectivity in qualitative studies, such as grounded theory. The paper also demonstrates how different techniques can help management research by including informants/managers in a time efficient way.

This paper describes how three complementary triangulation methods can be used for validation and exploration of concepts and themes in qualitative studies. Tree graphs, concept mapping, and member checking are applied in a managerial case study, complementing a conventional grounded theory approach.

The paper suggests that naturalistic inquiries, such as grounded theory and thematic analysis, can use mixed methods and multiple sources and coders in order to offset biases and to validate and sort findings. The case study presents three different perspectives on how an organization comprehends diversity as a strategic issue.

The paper suggests a mixed methods design that addresses some of the potential shortcomings often found in grounded theory and other qualitative studies, their theory development and their documentation of processes. It positions the approach over the range of the triangulation literature and it argues that it is important to be aware of different triangulation mindsets, and these they are not necessarily contradictory.

A grid information retrieval model has benefits for sharing resources and processing mass information, but cannot handle conceptual heterogeneity without integration of semantic information. The purpose of this research is to propose a concept‐based retrieval mechanism to catch the user's query intentions in a grid environment. This research re‐ranks documents over distributed data sources and evaluates performance based on the user judgment and processing time.

This research uses the ontology lookup service to build the concept set in the ontology and captures the user's query intentions as a means of query expansion for searching. The Globus toolkit is used to implement the grid service. The modification of the collection retrieval inference (CORI) algorithm is used for re‐ranking documents over distributed data sources.

The experiments demonstrate that this proposed approach successfully describes the user's query intentions evaluated by user judgment. For processing time, building a grid information retrieval model is a suitable strategy for the ontology‐based retrieval model.

Most current semantic grid models focus on construction of the semantic grid, and do not consider re‐ranking search results from distributed data sources. The significance of evaluation from the user's viewpoint is also ignored. This research proposes a method that captures the user's query intentions and re‐ranks documents in a grid based on the CORI algorithm. This proposed ontology‐based retrieval mechanism calculates the global relevance score of all documents in a grid and displays those documents with higher relevance to users.

Presents the scientific methodology from the enlarged cybernetical perspective that recognizes the anisotropy of time, the probabilistic character of natural laws, and the entry that the incomplete determinism in Nature opens to the occurrence of innovation, growth, organization, teleology communication, control, contest and freedom. The new tier to the methodological edifice that cybernetics provides stands on the earlier tiers, which go back to the Ionians (c. 500 BC). However, the new insights reveal flaws in the earlier tiers, and their removal strengthens the entire edifice. The new concepts of teleological activity and contest allow the clear demarcation of the military sciences as those whose subject matter is teleological activity involving contest. The paramount question “what ought to be done”, outside the empirical realm, is embraced by the scientific methodology. It also embraces the cognitive sciences that ask how the human mind is able to discover, and how the sequence of discoveries might converge to a true description of reality.

This study aims to demonstrate the application of computer-aided text analysis (CATA) software in identifying primary associations and impressions of a specified tourist destination.

The Leximancer software is applied on primary information to analyze the concepts evoked by a destination. Because no specific planning has been done for destination image marketing strategies for rural tourism in Andalusia, this study visualizes and determines clusters of the main attributes associated with this destination.

The analysis identifies the main clusters among associations and impressions of the destination that can be useful in developing strategies.

Only a target segment is studied, with a relatively small sample size.

Leximancer can not only be applied to online user-generated content, but primary information can also be mapped to generate a holistic destination image. Furthermore, identification of the relevant attributes and impressions can serve to identify unique assets to help tourism organizations develop a destination.

Several implications concerning destination marketing are outlined.

Although previous studies have applied Leximancer and other CATA software, the present research uses a new approach. Deriving the primary information on destination image using an unstructured methodology, the concepts evoked by a destination are mapped. Because there is a lack of research on rural tourism in Andalusia and its destination image, its associated attributes are studied.

本论文展示如何使用CATA分析软件来确定一个具体旅游目的地的主要关系和印象。

Leximancer软件主要用于分析一个目的地引发的相关概念。因为安达卢西亚至今未有确定的乡村旅游目的地形象营销策略, 本论文视觉化和决定与此目的地相关的主要因素群。

研究结果指出了此安达卢西亚目的地的主要相关群和印象群, 这些对于指定战略计划有很大帮助。

本论文只研究了单一市场群, 分析样本量较小。

Leximancer不仅可以用来分析用户生成内容, 还可以分析主要信息, 以展示整体旅游目的地形象。此外, 分析指出的相关因素和印象群可以用来确立独特的资源组合, 帮助旅游机构开发旅游目的地。

本论文结构指出了多个旅游目的地营销的相关启示

尽管文献中有关Leximancer和其他CATA软件的使用文章, 本论文创立了新的使用方法。基于对旅游目的地形象的主要信息进行非结构性研究, 本论文对于旅游目的地的概念进行展开论述。由于至今未有针对安达卢西亚的乡村旅游研究以及旅游目的地形象研究, 本研究论述了其相关因素。

关键词 安达卢西亚、旅游目的地形象、旅游目的地因素、CATA软件、Leximancer