MISNIS: an intelligent platform for Twitter topic mining

Twitter has become a major tool for spreading news, for dissemination of positions and ideas, and for the commenting and analysis of current world events. However, with more than 500 million tweets flowing per day, it is necessary to find efficient ways of collecting, storing, managing, mining and visualizing all this information. This is especially relevant if one considers that Twitter has no ways of indexing tweet contents, and that the only available categorization “mechanism” is the #hashtag, which is totally dependent of a user's will to use it. This paper presents an intelligent platform and framework, named MISNIS - Intelligent Mining of Public Social Networks’ Influence in Society - that facilitates these issues and allows a non-technical user to easily mine a given topic from a very large tweet's corpus and obtain relevant contents and indicators such as user influence or sentiment analysis. When compared to other existent similar platforms, MISNIS is an expert system that includes specifically developed intelligent techniques that: (1) Circumvent the Twitter API restrictions that limit access to 1% of all flowing tweets. The platform has been able to collect more than 80% of all flowing portuguese language tweets in Portugal when online; (2) Intelligently retrieve most tweets related to a given topic even when the tweets do not contain the topic #hashtag or user indicated keywords. A 40% increase in the number of retrieved relevant tweets has been reported in real world case studies. The platform is currently focused on Portuguese language tweets posted in Portugal. However, most developed technologies are language independent (e.g. intelligent retrieval, sentiment analysis, etc.), and technically MISNIS can be easily expanded to cover other languages and locations

Exploring the law of text geographic information

Textual geographic information is indispensable and heavily relied upon in practical applications. The absence of clear distribution poses challenges in effectively harnessing geographic information, thereby driving our quest for exploration. We contend that geographic information is influenced by human behavior, cognition, expression, and thought processes, and given our intuitive understanding of natural systems, we hypothesize its conformity to the Gamma distribution. Through rigorous experiments on a diverse range of 24 datasets encompassing different languages and types, we have substantiated this hypothesis, unearthing the underlying regularities governing the dimensions of quantity, length, and distance in geographic information. Furthermore, theoretical analyses and comparisons with Gaussian distributions and Zipf's law have refuted the contingency of these laws. Significantly, we have estimated the upper bounds of human utilization of geographic information, pointing towards the existence of uncharted territories. Also, we provide guidance in geographic information extraction. Hope we peer its true countenance uncovering the veil of geographic information.Comment: IP

Using semantic technologies to resolve heterogeneity issues in sustainability and disaster management knowledge bases

This thesis examines issues of semantic heterogeneity in the domains of sustainability indicators and disaster management. We propose a model that links two domains with the following logic. While disaster management implies a proper and efficient response to a risk that has materialised as a disaster, sustainability can be defined as the preparedness to unexpected situations by applying measurements such as sustainability indicators. As a step to this direction, we investigate how semantic technologies can tackle the issues of heterogeneity in the aforementioned domains. First, we consider approaches to resolve the heterogeneity issues of representing the key concepts of sustainability indicator sets. To develop a knowledge base, we apply the METHONTOLOGY approach to guide the construction of two ontology design candidates: generic and specic. Of the two, the generic design is more abstract, with fewer classes and properties. Documents describing two indicator systems - the Global Reporting Initiative and the Organisation for Economic Co-operation and Development - are used in the design of both candidate ontologies. We then evaluate both ontology designs using the ROMEO approach, to calculate their level of coverage against the seen indicators, as well as against an unseen third indicator set (the United Nations Statistics Division). We also show that use of existing structured approaches like METHONTOLOGY and ROMEO can reduce ambiguity in ontology design and evaluation for domain-level ontologies. It is concluded that where an ontology needs to be designed for both seen and unseen indicator systems, a generic and reusable design is preferable. Second, having addressed the heterogeneity issues at the data level of sustainability indicators in the first phase of the research, we then develop a software for a sustainability reporting framework - Circles of Sustainability - which provides two mechanisms for browsing heterogeneous sustainability indicator sets: a Tabular view and a Circular view. In particular, the generic design of ontology developed during the first phase of the research is applied to this software. Next, we evaluate the overall usefulness and ease of use for the presented software and the associated user interfaces by conducting a user study. The analysis of quantitative and qualitative results of the user study concludes that the Circular view is the preferred interface by most participants for browsing semantic heterogeneous indicators. Third, in the context of disaster management, we present a geotagger method for the OzCrisisTracker application that automatically detects and disambiguates the heterogeneity of georeferences mentioned in the tweets' content with three possibilities: definite, ambiguous and no-location. Our method semantically annotates the tweet components utilising existing and new ontologies. We also concluded that the accuracy of geographic focus of our geotagger is considerably higher than other systems. From a more general perspective the research contributions can be articulated as follows. The knowledge bases developed in this research have been applied to the two domain applications. The thesis therefore demonstrates how semantic technologies, such as ontology design patterns, browsing tools and geocoding, can untangle data representation and navigation issues of semantic heterogeneity in sustainability and disaster management domains

Geospatial crowdsourced data fitness analysis for spatial data infrastructure based disaster management actions

The reporting of disasters has changed from official media reports to citizen reporters who are at the disaster scene. This kind of crowd based reporting, related to disasters or any other events, is often identified as 'Crowdsourced Data' (CSD). CSD are freely and widely available thanks to the current technological advancements. The quality of CSD is often problematic as it is often created by the citizens of varying skills and backgrounds. CSD is considered unstructured in general, and its quality remains poorly defined. Moreover, the CSD's location availability and the quality of any available locations may be incomplete. The traditional data quality assessment methods and parameters are also often incompatible with the unstructured nature of CSD due to its undocumented nature and missing metadata. Although other research has identified credibility and relevance as possible CSD quality assessment indicators, the available assessment methods for these indicators are still immature. In the 2011 Australian floods, the citizens and disaster management administrators used the Ushahidi Crowd-mapping platform and the Twitter social media platform to extensively communicate flood related information including hazards, evacuations, help services, road closures and property damage. This research designed a CSD quality assessment framework and tested the quality of the 2011 Australian floods' Ushahidi Crowdmap and Twitter data. In particular, it explored a number of aspects namely, location availability and location quality assessment, semantic extraction of hidden location toponyms and the analysis of the credibility and relevance of reports. This research was conducted based on a Design Science (DS) research method which is often utilised in Information Science (IS) based research. Location availability of the Ushahidi Crowdmap and the Twitter data assessed the quality of available locations by comparing three different datasets i.e. Google Maps, OpenStreetMap (OSM) and Queensland Department of Natural Resources and Mines' (QDNRM) road data. Missing locations were semantically extracted using Natural Language Processing (NLP) and gazetteer lookup techniques. The Credibility of Ushahidi Crowdmap dataset was assessed using a naive Bayesian Network (BN) model commonly utilised in spam email detection. CSD relevance was assessed by adapting Geographic Information Retrieval (GIR) relevance assessment techniques which are also utilised in the IT sector. Thematic and geographic relevance were assessed using Term Frequency – Inverse Document Frequency Vector Space Model (TF-IDF VSM) and NLP based on semantic gazetteers. Results of the CSD location comparison showed that the combined use of non-authoritative and authoritative data improved location determination. The semantic location analysis results indicated some improvements of the location availability of the tweets and Crowdmap data; however, the quality of new locations was still uncertain. The results of the credibility analysis revealed that the spam email detection approaches are feasible for CSD credibility detection. However, it was critical to train the model in a controlled environment using structured training including modified training samples. The use of GIR techniques for CSD relevance analysis provided promising results. A separate relevance ranked list of the same CSD data was prepared through manual analysis. The results revealed that the two lists generally agreed which indicated the system's potential to analyse relevance in a similar way to humans. This research showed that the CSD fitness analysis can potentially improve the accuracy, reliability and currency of CSD and may be utilised to fill information gaps available in authoritative sources. The integrated and autonomous CSD qualification framework presented provides a guide for flood disaster first responders and could be adapted to support other forms of emergencies

The Semantic Shadow : Combining User Interaction with Context Information for Semantic Web-Site Annotation

This thesis develops the concept of the Semantic Shadow (SemS), a model for managing contentual and structural annotations on web page elements and their values. The model supports a contextual weighting of the annotated information, allowing to specify the annotation values in relation to the evaluation context. A procedure is presented, which allows to manage and process this context-dependent meta information on web page elements using a dedicated programming interface. Two distinct implementations for the model have been developed: One based on Java objects, the other using the Resource Description Framework (RDF) as modeling backend. This RDF-based storage allows to integrate the annotations of the Semantic Shadow with other information of the Semantic Web. To demonstrate the application of the Semantic Shadow concept, a procedure to optimize web based user interfaces based on the structural semantics has been developed: Assuming a mobile client, a requested web page is dynamically adapted by a proxy prototype, where the context-awareness of the adaptation can be directly modeled alongside with the structural annotations. To overcome the drawback of missing annotations for existing web pages, this thesis introduces a concept to derive context-dependent meta-information on the web pages from their usage: From the observation of the users' interaction with a web page, certain context-dependent structural information about the concerned web page elements can be derived and stored in the annotation model of the Semantic Shadow concept.In dieser Arbeit wird das Konzept des Semantic Shadow (dt. Semantischer Schatten) entwickelt, ein Programmier-Modell um Webseiten-Elemente mit inhaltsbezogenen und strukturellen Anmerkungen zu versehen. Das Modell unterstützt dabei eine kontextabhängige Gewichtung der Anmerkungen, so dass eine Anmerkung in Bezug zum Auswertungs-Kontext gesetzt werden kann. Zur Verwaltung und Verarbeitung dieser kontextbezogenen Meta-Informationen für Webseiten-Elemente wurde im Rahmen der Arbeit eine Programmierschnittstelle definiert. Dazu wurden zwei Implementierungen der Schnittstelle entwickelt: Eine basiert ausschließlich auf Java-Objekten, die andere baut auf einem RDF-Modell auf. Die RDF-basierte Persistierung erlaubt eine Integration der Semantic-Shadow-Anmerkungen mit anderen Anwendungen des Semantic Webs. Um die Anwendungsmöglichkeiten des Semantic-Shadow-Konzepts darzustellen, wurde eine Vorgehensweise zur Optimierung von webbasierten Benutzerschnittstellen auf Grundlage von semantischen Strukturinformationen entwickelt: Wenn ein mobiler Benutzer eine Webseite anfordert, wird diese dynamisch durch einen Proxy angepasst. Die Kontextabhängigkeit dieser Anpassung wird dabei bereits direkt mit den Struktur-Anmerkungen modelliert. Für bestehende Webseiten liegen zumeist keine Annotationen vor. Daher wird in dieser Arbeit ein Konzept vorgestellt, kontextabhängige Meta-Informationen aus der Benutzung der Webseiten zu bestimmen: Durch Beobachtung der Benutzer-Interaktionen mit den Webseiten-Elementen ist es möglich bestimmte kontextabhängige Strukturinformationen abzuleiten und als Anmerkungen im Modell des Semantic-Shadow-Konzepts zu persistieren

The sources and characteristics of electronic evidence and artificial intelligence

In this updated edition of the well-established practitioner text, Stephen Mason and Daniel Seng have brought together a team of experts in the field to provide an exhaustive treatment of electronic evidence and electronic signatures. This fifth edition continues to follow the tradition in English evidence text books by basing the text on the law of England and Wales, with appropriate citations of relevant case law and legislation from other jurisdictions

Context-aware Non-electronic Service Discovery and Composition

In today’s web, many web services are created and updated on the Internet. In many cases, a single service is not sufficient to respond to the user’s request and often services should be combined through service composition to fulfill business goals. Service discovery and service composition can be highly compatible with context, i.e., according to context information, e.g., location, budget and time, services are chosen and composed. Moreover, we include non-electronic services, e.g., restaurants, movie theaters shopping malls and so on, into service composition. Non-electronic services are rarely considered in existing service composition research, however are frequently used in people’s daily life. In this thesis, we provide an approach for using contexts to discover and compose non-electronic services. We present a new context model which is to make it more suitable for service composition. This model is also able to handle both low level sensor data and high level data in predicated logic. Our service composition algorithm uses soft constraints, dissatisfaction of which causes a penalty instead of the fail of planning. With this feature, the service composition algorithm can give the user several “good enough” solutions, instead of null solution. Additionally, a replanning module is developed to refine the solution according to user’s further adjustments of his or her requirements. As a motivating example, a web based Personal Entertainment Planner system is built