Tags Are Related: Measurement of Semantic Relatedness Based on Folksonomy Network

Folksonomy and tagging systems, which allow users to interactively annotate a pool of shared resources using descriptive tags, have enjoyed phenomenal success in recent years. The concepts are organized as a map in human mind, however, the tags in folksonomy, which reflect users' collaborative cognition on information, are isolated with current approach. What we do in this paper is to estimate the semantic relatedness among tags in folksonomy: whether tags are related from semantic view, rather than isolated? We introduce different algorithms to form networks of folksonomy, connecting tags by users collaborative tagging, or by resource context. Then we perform multiple measures of semantic relatedness on folksonomy networks to investigate semantic information within them. The result shows that the connections between tags have relatively strong semantic relatedness, and the relatedness decreases dramatically as the distance between tags increases. What we find in this paper could provide useful visions in designing future folksonomy-based systems, constructing semantic web in current state of the Internet, and developing natural language processing applications

Review of Semantic Importance and Role of using Ontologies in Web Information Retrieval Techniques

The Web contains an enormous amount of information, which is managed to accumulate, researched, and regularly used by many users. The nature of the Web is multilingual and growing very fast with its diverse nature of data including unstructured or semi-structured data such as Websites, texts, journals, and files. Obtaining critical relevant data from such vast data with its diverse nature has been a monotonous and challenging task. Simple key phrase data gathering systems rely heavily on statistics, resulting in a word incompatibility problem related to a specific word's inescapable semantic and situation variants. As a result, there is an urgent need to arrange such colossal data systematically to find out the relevant information that can be quickly analyzed and fulfill the users' needs in the relevant context. Over the years ontologies are widely used in the semantic Web to contain unorganized information systematic and structured manner. Still, they have also significantly enhanced the efficiency of various information recovery approaches. Ontological information gathering systems recover files focused on the semantic relation of the search request and the searchable information. This paper examines contemporary ontology-based information extraction techniques for texts, interactive media, and multilingual data types. Moreover, the study tried to compare and classify the most significant developments utilized in the search and retrieval techniques and their major disadvantages and benefits

Configuring the Networked Citizen

Among legal scholars of technology, it has become commonplace to acknowledge that the design of networked information technologies has regulatory effects. For the most part, that discussion has been structured by the taxonomy developed by Lawrence Lessig, which classifies code as one of four principal regulatory modalities, alongside law, markets, and norms. As a result of that framing, questions about the applicability of constitutional protections to technical decisions have taken center stage in legal and policy debates. Some scholars have pondered whether digital architectures unacceptably constrain fundamental liberties, and what public design obligations might follow from such a conclusion. Others have argued that code belongs firmly on the private side of the public/private divide because it originates in the innovative activity of private actors. In a forthcoming book, the author argues that the project of situating code within one or another part of the familiar constitutional landscape too often distracts legal scholars from more important questions about the quality of the regulation that networked digital architectures produce. The gradual, inexorable embedding of networked information technologies has the potential to alter, in largely invisible ways, the interrelated processes of subject formation and culture formation. Within legal scholarship, the prevailing conceptions of subjectivity tend to be highly individualistic, oriented around the activities of speech and voluntary affiliation. Subjectivity also tends to be understood as definitionally independent of culture. Yet subjectivity is importantly collective, formed by the substrate within which individuality emerges. People form their conceptions of the good in part by reading, listening, and watching—by engaging with the products of a common culture—and by interacting with one another. Those activities are socially and culturally mediated, shaped by the preexisting communities into which individuals are born and within which they develop. They are also technically mediated, shaped by the artifacts that individuals encounter in common use. The social and cultural patterns that mediate the activities of self-constitution are being reconfigured by the pervasive adoption of technical protocols and services that manage the activities of content delivery, search, and social interaction. In developed countries, a broad cross-section of the population routinely uses networked information technologies and communications devices in hundreds of mundane, unremarkable ways. We search for information, communicate with each other, and gain access to networked resources and services. For the most part, as long as our devices and technologies work as expected, we give little thought to how they work; those questions are understood to be technical questions. Such questions are better characterized as sociotechnical. As networked digital architectures increasingly mediate the ordinary processes of everyday life, they catalyze gradual yet fundamental social and cultural change. This chapter—originally published in Imagining New Legalities: Privacy and Its Possibilities in the 21st Century, edited by Austin Sarat, Lawrence Douglas, and Martha Merrill Umphrey (2012)—considers two interrelated questions that flow from understanding sociotechnical change as (re)configuring networked subjects. First, it revisits the way that legal and policy debates locate networked information technologies with respect to the public/private divide. The design of networked information technologies and communications devices is conventionally treated as a private matter; indeed, that designation has been the principal stumbling block encountered by constitutional theorists of technology. The classification of code as presumptively private has effects that reach beyond debates about the scope of constitutional guarantees, shaping views about the extent to which regulation of technical design decisions is normatively desirable. This chapter reexamines that discursive process, using lenses supplied by literatures on third-party liability and governance. Second, this chapter considers the relationship between sociotechnical change and understandings of citizenship. The ways that people think, form beliefs, and interact with one another are centrally relevant to the sorts of citizens that they become. The gradual embedding of networked information technologies into the practice of everyday life therefore has important implications for both the meaning and the practice of citizenship in the emerging networked information society. If design decisions are neither merely technical nor presumptively private, then they should be subject to more careful scrutiny with regard to the kind of citizen they produce. In particular, policy-makers cannot avoid engaging with the particular values that are encoded

Commonsense Knowledge in Sentiment Analysis of Ordinance Reactions for Smart Governance

Smart Governance is an emerging research area which has attracted scientific as well as policy interests, and aims to improve collaboration between government and citizens, as well as other stakeholders. Our project aims to enable lawmakers to incorporate data driven decision making in enacting ordinances. Our first objective is to create a mechanism for mapping ordinances (local laws) and tweets to Smart City Characteristics (SCC). The use of SCC has allowed us to create a mapping between a huge number of ordinances and tweets, and the use of Commonsense Knowledge (CSK) has allowed us to utilize human judgment in mapping. We have then enhanced the mapping technique to link multiple tweets to SCC. In order to promote transparency in government through increased public participation, we have conducted sentiment analysis of tweets in order to evaluate the opinion of the public with respect to ordinances passed in a particular region. Our final objective is to develop a mapping algorithm in order to directly relate ordinances to tweets. In order to fulfill this objective, we have developed a mapping technique known as TOLCS (Tweets Ordinance Linkage by Commonsense and Semantics). This technique uses pragmatic aspects in Commonsense Knowledge as well as semantic aspects by domain knowledge. By reducing the sample space of big data to be processed, this method represents an efficient way to accomplish this task. The ultimate goal of the project is to see how closely a given region is heading towards the concept of Smart City

Nerf This! Navigating the Accessibility and Inclusivity of Video Games Through Expressive Arts Therapies: A Literature Review

This thesis explores the accessibility of video games to populations with disabilities, as well as the inclusivity of video game design, and how the application of expressive arts therapy (ExAT) can benefit in processing and navigating difficult feelings that may arise for a gamer in the video game community. Video games have evolved immensely since first being introduced in the 1970-s and come a long way to accommodate a diverse set of people, yet video games are still marketed toward and for ableist populations. This paper reviews literature on the limitations of video games for the player as well as the toxic nature of the gaming community and the benefits of video games on mental wellbeing. It also examines shared personal experiences with colleagues and my own lived experience of physical and mental limitations with video games. Findings suggest that video games can offer positive benefits on social skills and mental health and if they were more accessible to a wider audience, more individuals would be able to experience these benefits. Video games can be used as a therapeutic tool to explore worlds and landscapes that may otherwise be impossible, offer the player to use their senses to explore, play, and find meaning through use of the ETC, and help connect and reconnect relationships

Context-sensitive interpretation of natural language location descriptions : a thesis submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy in Information Technology at Massey University, Auckland, New Zealand

People frequently describe the locations of objects using natural language. Location descriptions may be either structured, such as 26 Victoria Street, Auckland, or unstructured. Relative location descriptions (e.g., building near Sky Tower) are a common form of unstructured location description, and use qualitative terms to describe the location of one object relative to another (e.g., near, close to, in, next to). Understanding the meaning of these terms is easy for humans, but much more difficult for machines since the terms are inherently vague and context sensitive. In this thesis, we study the semantics (or meaning) of qualitative, geospatial relation terms, specifically geospatial prepositions. Prepositions are one of the most common forms of geospatial relation term, and they are commonly used to describe the location of objects in the geographic (geospatial) environment, such as rivers, mountains, buildings, and towns. A thorough understanding of the semantics of geospatial relation terms is important because it enables more accurate automated georeferencing of text location descriptions than use of place names only. Location descriptions that use geospatial prepositions are found in social media, web sites, blogs, and academic reports, and georeferencing can allow mapping of health, disaster and biological data that is currently inaccessible to the public. Such descriptions have unstructured format, so, their analysis is not straightforward. The specific research questions that we address are: RQ1. Which geospatial prepositions (or groups of prepositions) and senses are semantically similar? RQ2. Is the role of context important in the interpretation of location descriptions? RQ3. Is the object distance associated with geospatial prepositions across a range of geospatial scenes and scales accurately predictable using machine learning methods? RQ4. Is human annotation a reliable form of annotation for the analysis of location descriptions? To address RQ1, we determine the nature and degree of similarity among geospatial prepositions by analysing data collected with a human subjects experiment, using clustering, extensional mapping and t-stochastic neighbour embedding (t-SNE) plots to form a semantic similarity matrix. In addition to calculating similarity scores among prepositions, we identify the senses of three groups of geospatial prepositions using Venn diagrams, t-sne plots and density-based clustering, and define the relationships between the senses. Furthermore, we use two text mining approaches to identify the degree of similarity among geospatial prepositions: bag of words and GloVe embeddings. By using these methods and further analysis, we identify semantically similar groups of geospatial prepositions including: 1- beside, close to, near, next to, outside and adjacent to; 2- across, over and through and 3- beyond, past, by and off. The prepositions within these groups also share senses. Through is recognised as a specialisation of both across and over. Proximity and adjacency prepositions also have similar senses that express orientation and overlapping relations. Past, off and by share a proximal sense but beyond has a different sense from these, representing on the other side. Another finding is the more frequent use of the preposition close to for pairs of linear objects than near, which is used more frequently for non-linear ones. Also, next to is used to describe proximity more than touching (in contrast to other prepositions like adjacent to). Our application of text mining to identify semantically similar prepositions confirms that a geospatial corpus (NCGL) provides a better representation of the semantics of geospatial prepositions than a general corpus. Also, we found that GloVe embeddings provide adequate semantic similarity measures for more specialised geospatial prepositions, but less so for those that have more generalised applications and multiple senses. We explore the role of context (RQ2) by studying three sites that vary in size, nature, and context in London: Trafalgar Square, Buckingham Palace, and Hyde Park. We use the Google search engine to extract location descriptions that contain these three sites with 9 different geospatial prepositions (in, on, at, next to, close to, adjacent to, near, beside, outside) and calculate their acceptance profiles (the profile of the use of a preposition at different distances from the reference object) and acceptance thresholds (maximum distance from a reference object at which a preposition can acceptably be used). We use these to compare prepositions, and to explore the influence of different contexts. Our results show that near, in and outside are used for larger distances, while beside, adjacent to and at are used for smaller distances. Also, the acceptance threshold for close to is higher than for other proximity/adjacency prepositions such as next to, adjacent to and beside. The acceptance threshold of next to is larger than adjacent to, which confirms the findings in ‎Chapter 2 which identifies next to describing a proximity rather than touching spatial relation. We also found that relatum characteristics such as image schema affect the use of prepositions such as in, on and at. We address RQ3 by developing a machine learning regression model (using the SMOReg algorithm) to predict the distance associated with use of geospatial prepositions in specific expressions. We incorporate a wide range of input variables including the similarity matrix of geospatial prepositions (RQ1); preposition senses; semantic information in the form of embeddings; characteristics of the located and reference objects in the expression including their liquidity/solidity, scale and geometry type and contextual factors such as the density of features of different types in the surrounding area. We evaluate the model on two different datasets with 25% improvement against the best baseline respectively. Finally, we consider the importance of annotation of geospatial location descriptions (RQ4). As annotated data is essential for the successful study of automated interpretation of natural language descriptions, we study the impact and accuracy of human annotation on different geospatial elements. Agreement scores show that human annotators can annotate geospatial relation terms (e.g., geospatial prepositions) with higher agreement than other geospatial elements. This thesis advances understanding of the semantics of geospatial prepositions, particularly considering their semantic similarity and the impact of context on their interpretation. We quantify the semantic similarity of a set of 24 geospatial prepositions; identify senses and the relationships among them for 13 geospatial prepositions; compare the acceptance thresholds of 9 geospatial prepositions and describe the influence of context on them; and demonstrate that richer semantic and contextual information can be incorporated in predictive models to interpret relative geospatial location descriptions more accurately