25,935 research outputs found
Combination of DROOL rules and Protégé knowledge bases in the ONTO-H annotation tool
ONTO-H is a semi-automatic collaborative tool for the semantic annotation of documents, built as a Protégé 3.0 tab plug-in. Among its multiple functionalities aimed at easing the document annotation process, ONTO-H uses a rule-based system to create cascading annotations out from a single drag and drop operation from a part of a document into an already existing concept or instance of the domain ontology being used for annotation. It also gives support to the detection of name conflicts and instance duplications in the creation of the annotations. The rule system runs on top of the open source rule engine DROOLS and is connected to the domain ontology used for annotation by means of an ad-hoc programmed Java proxy
A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation
Automatic extraction and analysis of meaning-related information from natural language data has been an important issue in a number of research areas, such as natural language processing (NLP), text mining, corpus linguistics, and data science. An important aspect of such information extraction and analysis is the semantic annotation of language data using a semantic tagger. In practice, various semantic annotation tools have been designed to carry out different levels of semantic annotation, such as topics of documents, semantic role labeling, named entities or events. Currently, the majority of existing semantic annotation tools identify and tag partial core semantic information in language data, but they tend to be applicable only for modern language corpora. While such semantic analyzers have proven useful for various purposes, a semantic annotation tool that is capable of annotating deep semantic senses of all lexical units, or all-words tagging, is still desirable for a deep, comprehensive semantic analysis of language data. With large-scale digitization efforts underway, delivering historical corpora with texts dating from the last 400 years, a particularly challenging aspect is the need to adapt the annotation in the face of significant word meaning change over time. In this paper, we report on the development of a new semantic tagger (the Historical Thesaurus Semantic Tagger), and discuss challenging issues we faced in this work. This new semantic tagger is built on existing NLP tools and incorporates a large-scale historical English thesaurus linked to the Oxford English Dictionary. Employing contextual disambiguation algorithms, this tool is capable of annotating lexical units with a historically-valid highly fine-grained semantic categorization scheme that contains about 225,000 semantic concepts and 4,033 thematic semantic categories. In terms of novelty, it is adapted for processing historical English data, with rich information about historical usage of words and a spelling variant normalizer for historical forms of English. Furthermore, it is able to make use of knowledge about the publication date of a text to adapt its output. In our evaluation, the system achieved encouraging accuracies ranging from 77.12% to 91.08% on individual test texts. Applying time-sensitive methods improved results by as much as 3.54% and by 1.72% on average
Ontology Learning and Semantic Annotation: a Necessary Symbiosis
Semantic annotation of text requires the dynamic merging of linguistically structured information and a ?world model?, usually represented as a domain-specific ontology. On the other hand, the process of engineering a domain-ontology through semi-automatic ontology learning system requires the availability of a considerable amount of semantically annotated documents. Facing this bootstrapping paradox requires an incremental process of annotation-acquisition-annotation, whereby domain-specific knowledge is acquired from linguistically-annotated texts and then projected back onto texts for extra linguistic information to be annotated and further knowledge layers to be extracted. The presented methodology is a first step in the direction of a full ?virtuous? circle where the semantic annotation platform and the evolving ontology interact in symbiosis. As a case study we have chosen the semantic annotation of product catalogues. We propose a hybrid approach, combining pattern matching techniques to exploit the regular structure of product descriptions in catalogues, and Natural Language Processing techniques which are resorted to analyze natural language descriptions. The semantic annotation involves the access to the ontology, semi-automatically bootstrapped with an ontology learning tool from annotated collections of catalogues
Doc2RDFa: Semantic Annotation for Web Documents
Ever since its conception, the amount of data published on the worldwide
web has been rapidly growing to the point where it has become an important
source of both general and domain specific information. However, the majority
of documents published online are not machine readable by default. Many researchers
believe that the answer to this problem is to semantically annotate these
documents, and thereby contribute to the linked "Web of Data". Yet, the process
of annotating web documents remains an open challenge. While some efforts towards
simplifying this process have been made in the recent years, there is still a
lack of semantic content creation tools that integrate well with information worker
toolsets. Towards this end, we introduce Doc2RDFa, an HTML rich text processor
with the ability to automatically and manually annotate domain-specific Content
Semantic web-based document: editing and browsing in AktiveDoc
This paper presents a tool for supporting sharing and reuse of knowledge in document creation (writing) and use (reading). Semantic Web technologies are used to support the production of ontology based annotations while the document is written. Free text annotations (comments) can be added to integrate the knowledge in the document. In addition the tool uses external services (e.g. a Semantic Web harvester) to propose relevant content to writing
user, enabling easy knowledge reuse. Similar facilities are provided for readers when their task does not coincide with the author’s one. The tool is specifically designed for Knowledge Management in organisations. In this paper we present and discuss how Semantic Web technologies are designed and integrated in the system
Magpie: towards a semantic web browser
Web browsing involves two tasks: finding the right web page and then making sense of its content. So far, research has focused on supporting the task of finding web resources through ‘standard’ information retrieval mechanisms, or semantics-enhanced search. Much less attention has been paid to the second problem. In this paper we describe Magpie, a tool which supports the
interpretation of web pages. Magpie offers complementary knowledge sources, which a reader can call upon to quickly gain access to any background knowledge relevant to a web resource. Magpie automatically associates an ontologybased
semantic layer to web resources, allowing relevant services to be invoked within a standard web browser. Hence, Magpie may be seen as a step towards a semantic web browser. The functionality of Magpie is illustrated using examples of how it has been integrated with our lab’s web resources
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