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This study aims to present a new framework for ethical creation and evaluation of multi-perspective knowledge organization systems.

Applying Held's understanding of the ethics of care, this paper proposes five operative criteria for ethical building and evaluation of multi-perspective knowledge representation and organization systems.

This paper argues that a carefully designed multipoint view of representation and organization conforms to the proposed ethical criteria and shifts concerns associated with the expectation of neutrality of library information professionals to the necessity to humanize and diversify the representation and organization of knowledge to build inclusive and equitable systems.

This paper presents multi-perspectiveness as key to ethical knowledge organization. The paper proposes a generic taxonomy of the main stages in the creation of multi-perspective knowledge representation and organization systems and demonstrates how to apply the proposed framework in each stage to ensure ethical outcomes.

Social network sites have been widely adopted by politicians in the last election campaigns. To increase the effectiveness of these campaigns the potential electorate is to be identified, as targeted ads are much more effective than non-targeted ads. Therefore, the purpose of this paper is to propose and implement a new methodology for automatic prediction of political orientation of users on social network sites by comparison to texts from the overtly political parties’ pages.

To this end, textual information on personal users’ pages is used as a source of statistical features. The authors apply automatic text categorization algorithms to distinguish between texts of users from different political wings. However, these algorithms require a set of manually labeled texts for training, which is typically unavailable in real life situations. To overcome this limitation the authors propose to use texts available on various political parties’ pages on a social network site to train the classifier. The political leaning of these texts is determined by the political affiliation of the corresponding parties. The classifier learned on such overtly political texts is then applied on the personal user pages to predict their political orientation. To assess the validity and effectiveness of the proposed methodology two corpora were constructed: personal Facebook pages of 450 Israeli citizens, and political parties Facebook pages of the nine prominent Israeli parties.

The authors found that when a political tendency classifier is trained and tested on data in the same corpus, accuracy is very high. More significantly, training on manifestly political texts (political party Facebook pages) yields classifiers which can be used to classify non-political personal Facebook pages with fair accuracy.

Previous studies have shown that targeted ads are more effective than non-targeted ads leading to substantial saving in the advertising budget. Therefore, the approach for automatic determining the political orientation of users on social network sites might be adopted for targeting political messages, especially during election campaigns.

This paper proposes and implements a new approach for automatic cross-corpora identification of political bias of user profiles on social network. This suggests that individuals’ political tendencies can be identified without recourse to any tagged personal data. In addition, the authors use learned classifiers to determine which self-identified centrists lean left or right and which voters are likely to switch allegiance in subsequent elections.

The need for inclusive and logically consistent representation of diverse and even confronting viewpoints on the domain knowledge has been widely discussed in the literature in the past decade. The purpose of this paper is to propose a generic model for building an open coherent diversified knowledge organization system (KOS).

The proposed model incorporates a generic epistemological component, the validity scope type, assigned to each statement in the constructed KOS. Statements are clustered by their association with various validity scope types into internally coherent subsystems. These subsystems form a knowledge organization network connected through the universal (consensual) subsystems with more than one validity scope type. The model extends the Galili’s Cultural Content Representation paradigm, which divides the knowledge content of a scientific theory into two confronting parts: body and periphery.

The knowledge organization network model makes it possible to comparatively examine similarities and differences among various viewpoints and theories on the domain knowledge. The presented approach conforms with the principle of Open Knowledge Network initiative for creation of open accessible knowledge.

The proposed model can be used for ontological reasoning by a variety of information services, such as ontology-based decision-support and learning systems, diversified search and customer management applications.

The model enables explicit representation of social and cultural minority voices and historical knowledge in the KOS.

The main contribution of the proposed model is that it generalizes and enhances various previously proposed representations of epistemological aspects of KOS and allows for multiple inter-linked subsystems to coherently co-exist as part of the extensible network.

Large cultural heritage datasets from museum collections tend to be biased and demonstrate omissions that result from a series of decisions at various stages of the collection construction. The purpose of this study is to apply a set of ethical criteria to compare the level of bias of six online databases produced by two major art museums, identifying the most biased and the least biased databases.

At the first stage, the relevant data have been automatically extracted from all six databases and mapped to a unified ontological scheme based on Wikidata. Then, the authors applied ethical criteria to the results of the geographical distribution of records provided by two major art museums as online databases accessed via museums' websites, API datasets and datasets submitted to Wikidata.

The authors show that the museums use different artworks in each of its online databases and each data-base has different types of bias reflected by the study variables, such as artworks' country of origin or the creator's nationality. For most variables, the database behind the online search system on the museum's website is more balanced and ethical than the API dataset and Wikidata databases of the two museums.

By applying ethical criteria to the analysis of cultural bias in various museum databases aimed at different audiences including end users, researchers and commercial institutions, this paper shows the importance of explicating bias and maintaining integrity in cultural heritage representation through different channels that potentially have high impact on how culture is perceived, disseminated, contextualized and transformed.

The purpose of this paper is to investigate the predictive factors of information seeking behavior of smartphone users from the cross-generational perspective. Based on existing literature, the two most popular types of information seeking behavior of smartphone users were determined: social information seeking behavior; and functional/cognitive information seeking behavior.

A questionnaire comprising 66 questions was administered online to 216 smartphone users of three age groups according to three generations: generation X, Y (millennials) and Z. Several predictive factors were examined for each of these information seeking behavior types: generation, gender, personality traits (the Big Five), daily usage time, period of ownership, various application utilization and the level of emotional gain from smartphones.

There is a trade-off between the two types of information seeking behavior. Also, men exhibited significantly more functional/cognitive information seeking behavior than women, and younger generations reported significantly higher emotional gain and social information seeking behavior than older generations. Interestingly, significant differences in smartphone apps’ utilization, information seeking behavior types and their predictive factors were found among users from different generations. Extraversion was positively related to social information seeking behavior only for generations X and Y, while WhatsApp usage was one of the strongest predictive factors only for generation Z.

This research has practical implications for information system design, education, e-commerce and libraries.

This is a first study that systematically examines predictive factors of the two prominent types of information seeking behavior on smartphones from the cross-generational perspective.

The purpose of this paper is to investigate the attitudes and influential factors of users’ knowledge and use of the tools designated for controlling and enhancing online privacy, which are referred to as online privacy literacy (OPL). Particularly, inspired by the protection motivation theory, a motivational factor is defined as comprising several variables which reflect users’ motivation to protect their online privacy.

To this end, a user study was conducted based on the quantitative method with the participation of 169 students from the Israeli academia who were administered closed-ended questionnaires.

Generally low to moderate levels of OPL were obtained. Interestingly, the multivariate linear regression analysis showed that motivational factors, such as users’ concern for personal information protection on the internet and users’ privacy self-efficacy and sense of anonymity when visiting a website, were among the strongest predictive factors of users’ OPL level.

This research has social implications that might contribute to an increase in the OPL among internet users.

The direct influence of the examined factors on users’ OPL was not previously discussed in the literature. As a result of the study, a comprehensive model of user online privacy behavior was constructed.

Digitization of historical documents is a challenging task in many digital humanities projects. A popular approach for digitization is to scan the documents into images, and then convert images into text using optical character recognition (OCR) algorithms. However, the outcome of OCR processing of historical documents is usually inaccurate and requires post-processing error correction. The purpose of this paper is to investigate how crowdsourcing can be utilized to correct OCR errors in historical text collections, and which crowdsourcing methodology is the most effective in different scenarios and for various research objectives.

A series of experiments with different micro-task’s structures and text lengths were conducted with 753 workers on the Amazon’s Mechanical Turk platform. The workers had to fix OCR errors in a selected historical text. To analyze the results, new accuracy and efficiency measures were devised.

The analysis suggests that in terms of accuracy, the optimal text length is medium (paragraph-size) and the optimal structure of the experiment is two phase with a scanned image. In terms of efficiency, the best results were obtained when using longer text in the single-stage structure with no image.

The study provides practical recommendations to researchers on how to build the optimal crowdsourcing task for OCR post-correction. The developed methodology can also be utilized to create golden standard historical texts for automatic OCR post-correction.

This is the first attempt to systematically investigate the influence of various factors on crowdsourcing-based OCR post-correction and propose an optimal strategy for this process.

One of the under-explored aspects in the process of user information seeking behaviour is influence of time on relevance evaluation. It has been shown in previous studies that individual users might change their assessment of search results over time. It is also known that aggregated judgements of multiple individual users can lead to correct and reliable decisions; this phenomenon is known as the “wisdom of crowds”. The purpose of this paper is to examine whether aggregated judgements will be more stable and thus more reliable over time than individual user judgements.

In this study two simple measures are proposed to calculate the aggregated judgements of search results and compare their reliability and stability to individual user judgements. In addition, the aggregated “wisdom of crowds” judgements were used as a means to compare the differences between human assessments of search results and search engine’s rankings. A large-scale user study was conducted with 87 participants who evaluated two different queries and four diverse result sets twice, with an interval of two months. Two types of judgements were considered in this study: relevance on a four-point scale, and ranking on a ten-point scale without ties.

It was found that aggregated judgements are much more stable than individual user judgements, yet they are quite different from search engine rankings.

The proposed “wisdom of crowds”-based approach provides a reliable reference point for the evaluation of search engines. This is also important for exploring the need of personalisation and adapting search engine’s ranking over time to changes in users preferences.

This is a first study that applies the notion of “wisdom of crowds” to examine an under-explored in the literature phenomenon of “change in time” in user evaluation of relevance.

Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations.

Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies’ semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies.

To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies.

This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research.

This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results.

To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.

The purpose of this paper is to determine whether the quality and reliability of websites’ content can be assessed through the lens of “wisdom of the crowds”. In particular as a case study the authors examine the information supplied over time on several prominent Israeli real estate websites.

The Israeli real estate market was selected for the study, since there are many large, popular and dynamic real estate websites that feature hundreds of thousands of ads, representing most of the supply of real estate properties in the country. The authors built an automatic, ontology-based system that downloaded advertisements from three selected websites every two weeks for a number of months and checked for changes in these advertisements over time. The authors conjecture that wisdom of the crowds is mostly reflected by the information changes on the websites, since they indicate the anticipated market trends. Hence the authors developed a number of statistical measures to comparatively analyse trends of information changes on these websites, and assess their reliability compared to the actual market data and tendencies.

The primary results suggest similar information change trends amongst all the websites. Surprisingly, although some properties did not sell over time, sellers generally did not lower their asking price and were willing to wait. Sellers even raised their asking price, apparently in anticipation of future price increases. Comparison of recurring trends among the websites with the trends of the real market during the same time period and a few months after reveals that wisdom of the crowds is only partially effective as an indicator and predictor of website content quality: it correctly reflects the fluctuation in demand, but not in the prices.

This study was conducted over a limited time period of five months, and only in several cities in Israel. Additionally, since buyers are not explicitly represented in these sites, their information behaviour was not analysed, although it undoubtedly influences information changes performed by the sellers.

The practical contribution of this study is the ontology of the real estate world. Its assimilation by real estate websites would promote the development of their sites and user services. It would also enable ad sharing amongst the various websites and enable efficient searches by search engines. In addition the tools and measures that the authors developed will allow continued monitoring and analysis of user information change patterns.

To the best of the knowledge this is the first study to examine and compare real estate websites’ quality and evaluate their information reliability as wisdom of the crowds.