57,026 research outputs found
Information Extraction Techniques for the Purposes of Semantic Indexing of Archaeological Resources
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 Such unpublished online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.
The study proposes the use of knowledge resources and
conceptual models to assist an Information Extraction process
able to provide ‘rich’ semantic indexing of archaeological
documents capable of resolving linguistic ambiguities of
indexed terms. CRM CIDOC-EH, a standard core ontology in
cultural heritage, and the English Heritage (EH) Thesauri for
archaeological concepts are employed to drive the Information
Extraction process and to support the aims of a semantic
framework in which indexed terms are capable of supporting
semantic-aware access to on-line resources. The paper
describes the process of semantic indexing of archaeological
concepts (periods and finds) in a corpus of 535 grey literature
documents using a rule based Information Extraction
technique facilitated by the General Architecture of Text
Engineering (GATE) toolkit and expressed by Java Annotation
Pattern Engine (JAPE) rules. Illustrative examples
demonstrate the different stages of the process.
Initial results suggest that the combination of information
extraction with knowledge resources and standard core
conceptual models is capable of supporting semantic aware and
linguistically disambiguate term indexing
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration
Building document-grounded dialogue systems have received growing interest as
documents convey a wealth of human knowledge and commonly exist in enterprises.
Wherein, how to comprehend and retrieve information from documents is a
challenging research problem. Previous work ignores the visual property of
documents and treats them as plain text, resulting in incomplete modality. In
this paper, we propose a Layout-aware document-level Information Extraction
dataset, LIE, to facilitate the study of extracting both structural and
semantic knowledge from visually rich documents (VRDs), so as to generate
accurate responses in dialogue systems. LIE contains 62k annotations of three
extraction tasks from 4,061 pages in product and official documents, becoming
the largest VRD-based information extraction dataset to the best of our
knowledge. We also develop benchmark methods that extend the token-based
language model to consider layout features like humans. Empirical results show
that layout is critical for VRD-based extraction, and system demonstration also
verifies that the extracted knowledge can help locate the answers that users
care about.Comment: Accepted to ACM Multimedia (MM) Industry Track 202
Structuring and extracting knowledge for the support of hypothesis generation in molecular biology
Background: Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results: We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion: We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation
Literature-driven Curation for Taxonomic Name Databases
Digitized biodiversity literature provides a wealth of content for using biodiversity knowledge by machines. However, identifying taxonomic names and the associated semantic metadata is a difficult and labour intensive process. We present a system to support human assisted creation of semantic metadata. Information extraction techniques auto-matically identify taxonomic names from scanned documents. They are then presented to users for manual correction or verification. The tools that support the curation process include taxonomic name identification and mapping, and community-driven taxonomic name verification. Our research shows the potential for these information extrac-tion techniques to support research and curation in disciplines dependent upon scanned document
Content-based video indexing for the support of digital library search
Presents a digital library search engine that combines efforts of the AMIS and DMW research projects, each covering significant parts of the problem of finding the required information in an enormous mass of data. The most important contributions of our work are the following: (1) We demonstrate a flexible solution for the extraction and querying of meta-data from multimedia documents in general. (2) Scalability and efficiency support are illustrated for full-text indexing and retrieval. (3) We show how, for a more limited domain, like an intranet, conceptual modelling can offer additional and more powerful query facilities. (4) In the limited domain case, we demonstrate how domain knowledge can be used to interpret low-level features into semantic content. In this short description, we focus on the first and fourth item
Chatbots4Mobile: Feature-oriented knowledge base generation using natural language
Chatbots4Mobile is a research project from the GESSI research group (UPC-BarcelonaTech) which aims at designing and developing a task oriented, knowledge based conversational agent to support mobile users in the process of managing and integrating the functionalities exposed by their own application portfolio. To support the design of the required knowledge base, the project focuses on the application of Natural Language Processing (NLP) techniques to infer extended knowledge about the features exposed by a subset of mobile applications, including feature extraction from app-related documents, syntactic and semantic similarity analysis between features, and intent/entity classification focused on functionality identification from user requests. As next steps, we are focusing on the evaluation of embedded linguistic knowledge in large language models, as well as the application of granular sentiment analysis techniques to discern biased documents and process sentiment-based user feedback.With the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (published version
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A hybrid NLP & semantic knowledgebase approach for the intelligent exploration of Arabic documents
In the contemporary era, a colossal amount of information is published daily on the Web in the form of articles, documents, reviews, blogs and social media posts. As most of this data is available in the form of unstructured documents, it makes it challenging and timeconsuming to extract non-trivial, previously unknown, and potentially useful knowledge from the published documents. Hence, extracting useful knowledge from unstructured text, i.e., Information Extraction, is becoming an increasingly significant aspect of knowledge discovery.
This work focuses on Information Extraction form Arabic unstructured text, which is an especially challenging task as Arabic is a highly inflectional and derivational language. The problem is compounded by the lack of mature tools and advanced research in Arabic Natural Language Processing (NLP) in comparison to European languages for instance.
The principal objective of this research work is presenting a comprehensive methodology for integrating domain knowledge with Natural Language Processing techniques that were proven effective in solving most classification problems in order to improve the Information extraction process form online unstructured data. The importance of NLP tools lies in that they play a key role in allowing semantic concept tagging of unstructured text, and so realize the Semantic Web. This work presents a novel rule-based approach that uses linguistic grammar-based techniques to extract Arabic composite names from Arabic text. Our approach uniquely exploits the genitive Arabic grammar rules; in particular, the rules regarding the identification of definite nouns (معرفة) and indefinite nouns (نكرة) to support the process of extracting composite names. Furthermore, this approach does not place any constraints on the length of the Arabic composite name. The results of our experiments show that there are improvement in recognizing Arabic composite names entity in the Arabic language text.
Our research also contributes a novel, knowledge-based approach to relation extraction from unstructured Arabic text, which is based on the principles of Functional Discourse Grammar (FDG). We further improve the approach by integrating it with Machine Learning relation classification, resulting in a hybrid relation extraction algorithm that can handle especially complex Arabic sentence structures. The accuracy of our relation classification efforts was extensively evaluated by means of experimental evaluation that evidenced the accuracy of the FDG relation extraction approach and the improvement gained by the Machine Learning integration.
The essential NLP algorithms of entity recognition and relation extraction were deployed in a Semantic Knowledge-base that was built from the outset to model the knowledge of the problem domain. The semantic modelling of the knowledgebase aided improving the accuracy of the NLP algorithms by leveraging relevant domain knowledge published in Open Linked Datasets. Moreover, the extracted information was semantically tagged and inserted into the Semantic Knowledge-base, which facilitated building advanced rules to infer new interesting information from the extracted knowledge as well as utilising advanced query mechanisms for intelligently exploring the mined problem domain knowledge
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