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Since the last two decades, XML has gained momentum as the standard for web information management and complex data representation. Also, collaboratively built semi-structured information resources, such as Wikipedia, have become prevalent on the Web and can be inherently encoded in XML. Yet most methods for processing XML and semi-structured information handle mainly the syntactic properties of the data, while ignoring the semantics involved. To devise more intelligent applications, one needs to augment syntactic features with machine-readable semantic meaning. This can be achieved through the computational identification of the meaning of data in context, also known as (a.k.a.) automated semantic analysis and disambiguation, which is nowadays one of the main challenges at the core of the Semantic Web. This survey paper provides a concise and comprehensive review of the methods related to XML-based semi-structured semantic analysis and disambiguation. It is made of four logical parts. First, we briefly cover traditional word sense disambiguation methods for processing flat textual data. Second, we describe and categorize disambiguation techniques developed and extended to handle semi-structured and XML data. Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, web and mobile services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity. [ABSTRACT FROM PUBLISHER]

Various methods have been put forward to perform automatic social-based event detection and description. Yet, most of them do not capture the semantic meaning embedded in online social media data, which are usually highly heterogeneous and unstructured, and do not identify event relationships (e.g., car accident temporally occurs after storm, and geographically occurs near soccer match). To address this problem, we introduce a generic Social-based Event Detection, Description, and Linkage framework titled SEDDaL, taking as input: a collection of social media objects from heterogeneous sources (e.g., Flickr, YouTube, and Twitter), and producing as output a collection of semantically meaningful events interconnected with spatial, temporal, and semantic relationships. The latter are required as the building blocks for event-based Collective Knowledge (CK) organization, where CK underlines the combination of all known data, information, and metadata concerning a given concept or event. SEDDaL consists of four main modules for: i) describing social media objects in a generic Metadata Representation Space Model (MRSM) consisting of three composite dimensions: temporal, spatial, and semantic, ii) evaluating the similarity between social media objects' descriptions following MRSM, iii) detecting events from similar social media objects using an adapted unsupervised learning algorithm, where events are represented as clusters of objects in MRSM, and iv) identifying directional, metric, and topological relationships between events following MRSM's dimensions. We believe this is the first study to provide a generic model for describing semantic-aware events and their relationships extracted from social metadata on the Web. Experimental results confirm the quality and potential of our approach. • Performs semantic-aware event detection, description, and linkage from social media data. • Represents heterogeneous data in generic model made of temporal, spatial, & semantic dimensions. • Evaluates data similarity using combined temporal, spatial, and semantic similarity measures. • Detects events from similar social media objects using adapted unsupervised learning algorithm. • Describes events in generic model and identifies their directional, metric & topologic relations. [ABSTRACT FROM AUTHOR]

Abstract Lexical sentiment analysis (LSA) is of central importance in extracting and analyzing user moods and views on the Web. Most existing LSA approaches have utilized supervised learning techniques applied on corpus-based statistics, requiring extensive training data, training time, and large statistical corpora which are not always available. Other studies have utilized unsupervised and lexicon-based approaches to match target words in a lexical knowledge base (KB) with seed words in a sentiment lexicon , usually suffering from the limited coverage or inconsistent connectivity of affective concepts. In this paper, we introduce LISA, an unsupervised word-level knowledge graph-based LSA framework. It uses different variants of shortest path graph navigation techniques to compute and propagate affective scores in a lexical-affective graph (LAG), created by connecting a typical lexical KB like WordNet, with a reliable affect KB like WordNet-Affect Hierarchy (where any other lexical or affective KB can be utilized). LISA was designed in two consecutive iterations, producing two main modules: i) LISA 1.0 for affect navigation, and ii) LISA 2.0 for affect propagation and lookup. LISA 1.0 suffered from the semantic connectivity problem shared by some existing lexicon-based methods, and required polynomial execution time. This led to the development of LISA 2.0, which i) processes affective relationships separately from lexical/semantic connections (solving the semantic connectivity problem of LISA 1.0), and ii) produces a sentiment lexicon which can be searched in logarithmic time (handling LISA 1.0's efficiency problem). Experimental results on the ANEW dataset show that our approach, namely LISA 2.0, while completely unsupervised, is on a par with existing (semi)supervised solutions, highlighting its quality and potential. [ABSTRACT FROM AUTHOR]