Visual exploration and retrieval of XML document collections with the generic system X2

This article reports on the XML retrieval system X2 which has been developed at the University of Munich over the last five years. In a typical session with X2, the user first browses a structural summary of the XML database in order to select interesting elements and keywords occurring in documents. Using this intermediate result, queries combining structure and textual references are composed semiautomatically. After query evaluation, the full set of answers is presented in a visual and structured way. X2 largely exploits the structure found in documents, queries and answers to enable new interactive visualization and exploration techniques that support mixed IR and database-oriented querying, thus bridging the gap between these three views on the data to be retrieved. Another salient characteristic of X2 which distinguishes it from other visual query systems for XML is that it supports various degrees of detailedness in the presentation of answers, as well as techniques for dynamically reordering and grouping retrieved elements once the complete answer set has been computed

A Survey on Retrieval of Mathematical Knowledge

We present a short survey of the literature on indexing and retrieval of mathematical knowledge, with pointers to 72 papers and tentative taxonomies of both retrieval problems and recurring techniques.Comment: CICM 2015, 20 page

Modeling image databases using Xml schema

This thesis presents a model for still images in order to support content-based querying and browsing by hierarchical tree structures and object relational graphs. We use the extensible markup language (XML) schema to illustrate and exemplify the proposed model because of its interoperability and flexibility advantages. Of primary interest is the notion of complex types and referential integrity to fully describe the physical and semantic properties of images. XQuery is used to support query processing. We further show how these complex types of XML schema can be used to overcome the shortcomings of reported image database descriptions in the literature

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Automatic message annotation and semantic interface for context aware mobile computing

This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.The Ministry of Higher Education and Scientific Research (IRAQ

Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases

A critical challenge in constructing a natural language interface to database (NLIDB) is bridging the semantic gap between a natural language query (NLQ) and the underlying data. Two specific ways this challenge exhibits itself is through keyword mapping and join path inference. Keyword mapping is the task of mapping individual keywords in the original NLQ to database elements (such as relations, attributes or values). It is challenging due to the ambiguity in mapping the user's mental model and diction to the schema definition and contents of the underlying database. Join path inference is the process of selecting the relations and join conditions in the FROM clause of the final SQL query, and is difficult because NLIDB users lack the knowledge of the database schema or SQL and therefore cannot explicitly specify the intermediate tables and joins needed to construct a final SQL query. In this paper, we propose leveraging information from the SQL query log of a database to enhance the performance of existing NLIDBs with respect to these challenges. We present a system Templar that can be used to augment existing NLIDBs. Our extensive experimental evaluation demonstrates the effectiveness of our approach, leading up to 138% improvement in top-1 accuracy in existing NLIDBs by leveraging SQL query log information.Comment: Accepted to IEEE International Conference on Data Engineering (ICDE) 201

The Topology ToolKit

This system paper presents the Topology ToolKit (TTK), a software platform designed for topological data analysis in scientific visualization. TTK provides a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependence-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code, online documentation and video tutorials are available on TTK's website

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation