Semantic annotation, indexing, and retrieval

Interdisciplinary Science Reviews, 2009

In the context of large and ever growing archives, generating annotation suggestions automatically from textual resources related to the documents to be archived is an interesting option in theory. It could save a lot of work in the time-consuming and expensive task of manual annotation and it could help cataloguers attain a higher inter annotator agreement. However, some questions arise in practice: what is the quality of the automatically produced annotations? How do they compare with manual annotations and with the requirements for annotation that were defined in the archive? If different from the manual annotations, are the automatic annotations wrong? In the CHOICE project, partially hosted at the Netherlands Institute for Sound and Vision, the Dutch public archive for audiovisual broadcasts, we automatically generate annotation suggestions for cataloguers. In this paper, we define three types of evaluation of these annotation suggestions: (1) a classic and strict precision/recall measure expressing the overlap between automatically generated keywords and the manual annotations, (2) a loosened precision/recall measure for which semantically very similar annotations are also considered as relevant matches, (3) an in-use evaluation of the usefulness of manual versus automatic annotations in the context of Serendipitous Browsing. During serendipitous browsing the annotations (manual or automatic) are used to retrieve and visualize semantically related documents.

Knowledge and Information Systems, 2013

Research in the Life Sciences depends on the integration of large, distributed and heterogeneous data sources and web services. The discovery of which of these resources are the most appropriate to solve a given task is a complex research question, since there is a large amount of plausible candidates and there is little, mostly unstructured, metadata to be able to decide among them. We contribute a semi-automatic approach, based on semantic techniques, to assist researchers in the discovery of the most appropriate web services to fulfill a set of given requirements.

Aslib Proceedings, 2010

The paper describes the use of Information Extraction (IE), a Natural Language Processing (NLP) technique to assist 'rich' semantic indexing of diverse archaeological text resources. Such unpublished online documents are often referred to as 'Grey Literature'. Established document indexing techniques are not sufficient to satisfy user information needs that expand beyond the limits of a simple term matching search. The focus of the research is to direct a semantic-aware 'rich' indexing of diverse natural language resources with properties capable of satisfying information retrieval from on-line publications and datasets associated with the Semantic Technologies for Archaeological Resources (STAR) project in the UoG Hypermedia Research Unit.

Communications in Computer and Information Science, Volume 83, 2011

A few years ago, the System on Chip idea grew largely and 'flooded' the market of embedded systems. Many System on Chip designers started to write their own HDL components and made them available on the Internet. The idea of searching for a couple of pre-written cores and building your own System on Chip only by connecting them seemed time saving. We‘ve developed a system that enables a semantic description of VHDL IP components, allows search of specific components based on the unambiguous semantic description and works with prebuilt VHDL IP cores. We present an application built around the system and focus on the benefits the application user gains during the process of System on Chip design.

Studies in Computational Intelligence, 2009

Extraction ontologies represent a novel paradigm in web information extraction (as one of 'deductive' species of web mining) allowing to swiftly proceed from initial domain modelling to running a functional prototype, without the necessity of collecting and labelling large amounts of training examples. Bottlenecks in this approach are however the tedium of developing an extraction ontology adequately covering the semantic scope of web data to be processed and the difficulty of combining the ontology-based approach with inductive or wrapper-based approaches. We report on an ongoing project aiming at developing a web information extraction tool based on richly-structured extraction ontologies and with additional possibility of (1) semi-automatically constructing these from third-party domain ontologies, (2) absorbing the results of inductive learning for subtasks where pre-labelled data abound, and (3) actively exploiting formatting regularities in the wrapper style.

Applied Sciences

In modern society, people are heavily reliant on information available online through various channels, such as websites, social media, and web portals. Examples include searching for product prices, news, weather, and jobs. This paper focuses on an area of information extraction in e-recruitment, or job searching, which is increasingly used by a large population of users in across the world. Given the enormous volume of information related to job descriptions and users’ profiles, it is complicated to appropriately match a user’s profile with a job description, and vice versa. Existing information extraction techniques are unable to extract contextual entities. Thus, they fall short of extracting domain-specific information entities and consequently affect the matching of the user profile with the job description. The work presented in this paper aims to extract entities from job descriptions using a domain-specific dictionary. The extracted information entities are enriched with kn...

Advanced Functional Materials

This is the pre-peer reviewed version of the paper, which has been published already (see link below). This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions

Information, 2019

One way to increase the understanding of texts by machines is through adding semantic information to lexical items by including metadata tags, a process also called semantic annotation. There are several semantic aspects that can be added to the words, among them the information about the nature of the concept denoted through the association with a category of an ontology. The application of ontologies in the annotation task can span multiple domains. However, this particular research focused its approach on top-level ontologies due to its generalizing characteristic. Considering that annotation is an arduous task that demands time and specialized personnel to perform it, much is done on ways to implement the semantic annotation automatically. The use of machine learning techniques are the most effective approaches in the annotation process. Another factor of great importance for the success of the training process of the supervised learning algorithms is the use of a sufficiently large corpus and able to condense the linguistic variance of the natural language. In this sense, this article aims to present an automatic approach to enrich documents from the American English corpus through a CRF model for semantic annotation of ontologies from Schema.org top-level. The research uses two approaches of the model obtaining promising results for the development of semantic annotation based on top-level ontologies. Although it is a new line of research, the use of top-level ontologies for automatic semantic enrichment of texts can contribute significantly to the improvement of text interpretation by machines.

2010

Traditional information retrieval systems represent documents and queries by keyword sets. However, the content of a document or a query is mainly defined by both keywords and named entities occurring in it. Named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. Besides, the meaning of a query may imply latent named entities that are related to the apparent ones in the query. We propose an ontology-based generalized vector space model to semantic text search. It exploits ontological features of named entities and their latently related ones to reveal the semantics of documents and queries. We also propose a framework to combine different ontologies to take their complementary advantages for semantic annotation and searching. Experiments on a benchmark dataset show better search quality of our model to other ones.

Proceedings of the 17th International Workshop on Data Warehousing and OLAP - DOLAP '14, 2014

Despite recent progresses in methods for processing data about the movement of objects in the geographic space, some fundamental issues remain unresolved. One of them is how to describe movement segments (e.g., semantic trajectories, episodes like stops and moves) and diverse movement patterns (e.g., moving clusters, hotel-restaurant-shop-hotel), with formal semantic descriptions. Another issue is how to arrange descriptive data and measures in a Movement Data Warehouse (MDW) for powerful information analyses and reasonable performance. This paper introduces general definitions for movement segments, movement patterns, their categories and hierarchies. The proposed constructs are semantically enriched with references to concepts (categories) and/or instances of these concepts (objects) arranged in distinct hierarchies. Based on these constructs, we propose a semantic multidimensional model for MDW. A case study illustrates the expressiveness of the proposal for analyzing movement data collected via social media and semantically enriched with Linked Open Data (LOD).

Current Trends in Web Engineering, 2012

In order to develop web mashups, designers need an in-depth understanding of each Web API they are using. However, Web API documentation is rather heterogeneous, represented by big HTML files or collection of files in which it is difficult to identify elements such as API methods and how they can be invoked. Models have been widely recognized as first-citizen artifacts for documenting software applications, abstracting from implementation details, thus becoming good candidates to raise the level of automation of web mashup development. In this paper we present an approach for extracting models from Web API documentation. Our contributions are (i) a metamodel for standardizing the information extracted from Web APIs documentation; and (ii) a method for the extraction of models by parsing HTML files containing the Web API documentation, discovering useful data, and automatically generating the corresponding models (that conform to the defined metamodel).

Journal of Sensors, 2013

This paper presents an approach for the automatic detection and fast 3D profiling of lateral body panels of vehicles. The work introduces a method to integrate raw streams from depth sensors in the task of 3D profiling and reconstruction and a methodology for the extrinsic calibration of a network of Kinect sensors. This sensing framework is intended for rapidly providing a robot with enough spatial information to interact with automobile panels using various tools. When a vehicle is positioned inside the defined scanning area, a collection of reference parts on the bodywork are automatically recognized from a mosaic of color images collected by a network of Kinect sensors distributed around the vehicle and a global frame of reference is set up. Sections of the depth information on one side of the vehicle are then collected, aligned, and merged into a global RGB-D model. Finally, a 3D triangular mesh modelling the body panels of the vehicle is automatically built. The approach has applications in the intelligent transportation industry, automated vehicle inspection, quality control, automatic car wash systems, automotive production lines, and scan alignment and interpretation.

International Journal on Semantic Web and Information Systems

This article describes how Web services play an important role in several fields such as e-commerce and e-health. As the number of Web services is increasing rapidly, finding the best Web service according to users' requirements becomes more challenging. The traditional method of Web service discovery is based on keyword match. Due to this, many Web services which are most relevant to the user request are left undiscoverable. Some other emergent approaches are based on semantics to improve the quality of the discovered Web services in terms of relevance and satisfaction of user's need. In this paper, the authors present a survey of existing semantic Web services discovery approaches giving priority to relevant ones. Furthermore, this paper provides a critical and comparative analysis of the studied approaches and stands out major challenges to be addressed to substantially enhance the semantic Web service discovery.

Lecture Notes in Computer Science, 2013

Development of conceptual knowledge is an important part of education. In individualized instruction this aim can be facilitated by concept maps interconnected with learning materials. Traditional adaptive web-based elearning systems use a closed and static corpus, which is predefined in the design time. But there is plenty of learning resources publicly available on the Web, which provide a huge potential for learners. Obviously, it would be very useful to have an opportunity to choose for a particular context a suitable concept map that could dynamically access selected learning repositories and enable an intuitive navigation in both the concept and document layers, as well as between them. We have attempted to develop a solution that is based on the principles of open corpus adaptive educational hypermedia and at the same time can be part of a personal learning environment. The outcomes show that this may be a promising approach, but its usefulness is highly dependent on the usability and flexibility of the e-learning application.

Companion Proceedings of the Web Conference 2022

Semantic text annotations have been a key factor for supporting computer applications ranging from knowledge graph construction to biomedical question answering. In this systematic review, we provide an analysis of the data models that have been applied to semantic annotation projects for the scholarly publications available in the CORD-19 dataset, an open database of the full texts of scholarly publications about COVID-19. Based on Google Scholar and the screening of specific research venues, we retrieve seventeen publications on the topic mostly from the United States of America. Subsequently, we outline and explain the inline semantic annotation models currently applied on the full texts of biomedical scholarly publications. Then, we discuss the data models currently used with reference to semantic annotation projects on the CORD-19 dataset to provide interesting directions for the development of semantic annotation models and projects.

Multimedia Tools and Applications, 2018

Synchronizing TV contents with applications is a topic that has gained much interest during the last years. Reaching the viewers through various channels (TV, web, mobile devices, etc.) has shown to be a means for increasing the audience. Related to the above, the hybrid TV standard HbbTV (Hybrid Broadcast Broadband TV) synchronizes the broadcast of video and audio with applications that may be delivered through either the broadcast channel or a broadband network. Thus, HbbTV applications may be developed to provide contextual information for emitted TV shows and advertisements. This paper reports on the integration of the automatic generation of geographic focus of text content with interactive TV. In particular it describes a framework for the incorporation of geographic context to TV shows and its visualization through HbbTV. To achieve this, geographic named entities are first extracted form the available subtitles and next the spatial extension of This work has been partially funded by the Galician Government (Xunta de Galicia) and FEDER funds of the EU under the Consolidation Program of Competitive Research Units (R2014/007, GPC2014/037, R2016/011).