7 results on '"Lopez-Arevalo, Ivan"'
Search Results
2. Information Extraction meets the Semantic Web: A Survey (Talk)
- Author
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Martinez-Rodriguez, Jose L., Hogan, Aidan, and Lopez-Arevalo, Ivan
- Subjects
knowledge graphs ,semantic web ,information extraction - Abstract
Talk presented at the Knowledge Graphs Conference (KGC 2023) in the Semantic Web Journal - State of the Art track.
- Published
- 2023
- Full Text
- View/download PDF
3. Mining information from sentences through Semantic Web data and Information Extraction tasks.
- Author
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Martinez-Rodriguez, Jose L., Lopez-Arevalo, Ivan, and Rios-Alvarado, Ana B.
- Subjects
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SEMANTIC Web , *DATA mining , *DATA extraction , *MINES & mineral resources , *NATURAL languages - Abstract
The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed by computers. Thus, the challenge is to identify and extract the elements of information that can be represented. Hence, this article presents a strategy to extract information from sentences and its representation with Semantic Web standards. Our strategy involves Information Extraction tasks and a hybrid semantic similarity measure to get entities and relations that are later associated with individuals and properties from a Knowledge Base to create RDF triples (Subject–Predicate–Object structures). The experiments demonstrate the feasibility of our method and that it outperforms the accuracy provided by a pattern-based method from the literature. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Information extraction meets the Semantic Web: A survey.
- Author
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Hotho, Andreas, Martinez-Rodriguez, Jose L., Hogan, Aidan, and Lopez-Arevalo, Ivan
- Subjects
DATA mining ,INTERNET surveys ,SEMANTIC Web ,EXTRACTION techniques ,NATURAL languages - Abstract
We provide a comprehensive survey of the research literature that applies Information Extraction techniques in a Semantic Web setting. Works in the intersection of these two areas can be seen from two overlapping perspectives: using Semantic Web resources (languages/ontologies/knowledge-bases/tools) to improve Information Extraction, and/or using Information Extraction to populate the Semantic Web. In more detail, we focus on the extraction and linking of three elements: entities, concepts and relations. Extraction involves identifying (textual) mentions referring to such elements in a given unstructured or semi-structured input source. Linking involves associating each such mention with an appropriate disambiguated identifier referring to the same element in a Semantic Web knowledge-base (or ontology), in some cases creating a new identifier where necessary. With respect to entities, works involving (Named) Entity Recognition, Entity Disambiguation, Entity Linking, etc. in the context of the Semantic Web are considered. With respect to concepts, works involving Terminology Extraction, Keyword Extraction, Topic Modeling, Topic Labeling, etc., in the context of the Semantic Web are considered. Finally, with respect to relations, works involving Relation Extraction in the context of the Semantic Web are considered. The focus of the majority of the survey is on works applied to unstructured sources (text in natural language); however, we also provide an overview of works that develop custom techniques adapted for semi-structured inputs, namely markup documents and web tables. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
5. OpenIE-based approach for Knowledge Graph construction from text.
- Author
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Martinez-Rodriguez, Jose L., Lopez-Arevalo, Ivan, and Rios-Alvarado, Ana B.
- Subjects
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SEMANTIC Web , *INFORMATION retrieval , *DISCOURSE analysis , *MACHINE learning , *ALGORITHMS - Abstract
Transforming unstructured text into a formal representation is an important goal of the Semantic Web in order to facilitate the integration and retrieval of information. The construction of Knowledge Graphs (KGs) pursues such an idea, where named entities (real world things) and their relations are extracted from text. In recent years, many approaches for the construction of KGs have been proposed by exploiting Discourse Analysis, Semantic Frames, or Machine Learning algorithms with existing Semantic Web data. Although such approaches are useful for processing taxonomies and connecting beliefs, they provide several linguistic descriptions, which lead to semantic data heterogeneity and thus, complicating data consumption. Moreover, Open Information Extraction (OpenIE) approaches have been slightly explored for the construction of KGs, which provide binary relations representing atomic units of information that could simplify the querying and representation of data. In this paper, we propose an approach to generate KGs using binary relations produced by an OpenIE approach. For such purpose, we present strategies for favoring the extraction and linking of named entities with KG individuals, and additionally, their association with grammatical units that lead to producing more coherent facts. We also provide decisions for selecting the extracted information elements for creating potentially useful RDF triples for the KG. Our results demonstrate that the integration of information extraction units with grammatical structures provides a better understanding of proposition-based representations provided by OpenIE for supporting the construction of KGs. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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6. Building topic maps from relational databases.
- Author
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Jose-Garcia, Adan, Lopez-Arevalo, Ivan, and Sosa-Sosa, Victor
- Abstract
Relational Databases (RDB) have been traditionally widely used as the backend database for information systems. Considering that RDBs contain valuable data, the challenge is to find out how to improve accessing and sharing knowledge that resides in databases. The use of topic maps is one solution for representing that knowledge. However, manual development of topic maps is a difficult, time consuming and subjective task if there is not a common guideline. The existing topic maps building approaches convert RDBs without considering the knowledge residing in the database. This paper proposes an automatic approach that considers the background knowledge in the building process of topic maps. The proposed model was implemented and applied on a benchmark of RDBs. The resulted topic maps were validated syntactically using the Ontopia Vizigator tool and validated semantically through the inference of information using the Tolog query language. The results found in our experiments are encouraging. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
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7. An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web.
- Author
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Rubio-Sandoval, Jesus Ivan, Martinez-Rodriguez, Jose L., Lopez-Arevalo, Ivan, Rios-Alvarado, Ana B., Rodriguez-Rodriguez, Adolfo Josue, and Vargas-Requena, David Tomas
- Subjects
SEMANTIC Web ,AUGMENTED reality ,WEB services ,AUTOMOTIVE navigation systems ,SYSTEM integration ,MOBILE apps ,INDOOR positioning systems - Abstract
Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system's feasibility, where the participants show a positive interest in its functionalities. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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