Logical structure based semantic relationship extraction from semi-structured documents

This paper proposes a semantic information extraction technology model system based on BERT, aiming to solve the problem of semantic understanding and information extraction in online public opinion analysis. The system design includes data collection, preprocessing, semantic information extraction and result display modules, using the BERT model to capture the deep semantic relationship of the text and enhance the ability to understand complex semantics and contextual information. The data collection module covers multiple sources such as social media, news comments and forum posts, and ensures the high quality and diversity of data through real-time crawling and cleaning. The semantic information extraction module is based on the multi-head attention mechanism of BERT, which can effectively capture the emotional tendency, topic information and potential intention in the public opinion text. The experiment verifies the system performance based on 1 million online public opinion data. The results show that in the semantic information extraction task, the accuracy of the BERT-based model is improved by 22.4%, the recall rate is improved by 19.8%, and the F1 value reaches 91.7<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>, which is significantly better than traditional methods such as TF-IDF and Word2Vec. The sentiment classification module has an accuracy of 93.1%, achieving accurate recognition of positive, negative and neutral emotions. The processing time of a single piece of public opinion text is reduced by an average of 25.6%, indicating the high efficiency of the system in large-scale data processing. The simulation test verifies the system's stable operation capability in the scenario of 100,000 new public opinion data per day, with a delay of less than 1 second, which can meet the needs of real-time public opinion analysis.

Ontologies play an important role of the applications in recent years, especially on the Semantic Web, Information Retrieval, Information Extraction, and Question Answering. Ontologies are used in order to specify the knowledge that is exchanged and shared between the different systems. Ontologies define the formal semantics of the terms used for describing data, and the relations between these terms. Hence, one of the important steps for building the domain specific ontology is to construct the semantic relations among the terms of the ontology. This paper represents our proposed method for automatic semantic relation extraction from text documents of the ACM Digital Library. A random sample among 170 categories of ACM categories is used to evaluate our proposed method. Results generated show that our proposed method achieves high precision.

This paper presents an approach to construct rules automatically to identify semantic relations, such as Hypernym, Part_Of and Material from Modern Chinese Standard Dictionary (MCSD). This approach combines all the useful information such as part of speeches, syntactic information, and extracted semantic relations together in order to identify as many semantic relations as possible. However, not all the information provided, like syntactic information, is reliable. A method is required to ensure that the right information up to a point can be picked. And then all possible semantic relations are constructed as a net after being identified. Finally, we evaluate the net by determining word similarity.

In this paper, we proposed a novel approach based on topic ontology for tag recommendation. The proposed approach intelligently generates tag suggestions to blogs. In this approach, we construct topic ontology through enriching the set of categories in existing small ontology called as Open Directory Project. To construct topic ontology, a set of topics and their associated semantic relationships is identified automatically from the corpus-based external knowledge resources such as Wikipedia and WordNet. The construction relies on two folds such as concept acquisition and semantic relation extraction. In the first fold, a topic-mapping algorithm is developed to acquire the concepts from the semantic of Wikipedia. A semantic similarity-clustering algorithm is used to compute the semantic similarity measure to group the set of similar concepts. The second is the semantic relation extraction algorithm, which derives associated semantic relations between the set of extracted topics from the lexical patterns between synsets in WordNet. A suitable software prototype is created to implement the topic ontology construction process. A Jena API framework is used to organize the set of extracted semantic concepts and their corresponding relationship in the form of knowledgeable representation of Web ontology language. Thus, Protege tool provides the platform to visualize the automatically constructed topic ontology successfully. Using the constructed topic ontology, we can generate and suggest the most suitable tags for the new resource to users. The applicability of topic ontology with a spreading activation algorithm supports efficient recommendation in practice that can recommend the most popular tags for a specific resource. The spreading activation algorithm can assign the interest scores to the existing extracted blog content and tags. The weight of the tags is computed based on the activation score determined from the similarity between the topics in constructed topic ontology and content of the existing blogs. High-quality tags that has the highest activation score is recommended to the users. Finally, we conducted experimental evaluation of our tag recommendation approach using a large set of real-world data sets. Our experimental results explore and compare the capabilities of our proposed topic ontology with the spreading activation tag recommendation approach with respect to the existing AutoTag mechanism. And also discuss about the improvement in precision and recall of recommended tags on the data sets of Delicious and BibSonomy. The experiment shows that tag recommendation using topic ontology results in the folksonomy enrichment. Thus, we report the results of an experiment mean to improve the performance of the tag recommendation approach and its quality. © 2012 Wiley Periodicals, Inc.

Geographic information integration and fusion from multi-sources geospatial data is still a challenge because of semantic heterogeneity of geospatial entity data. These will delay geospatial cooperation decision support and integrated assessment application for natural resources and environmental problem. Ontology as a conceptualization and knowledge representation of application domain have provided a potential and aided support for entity matching and information integration of multiple sources and heterogeneous data. However, many data sources may not provided ontology definition and formalization representation. This paper will provide a method of graph model based ontologies representation and ontologies learning from data sources schema. Entity matching and fusion with ontology support is also discussed. This paper has classified unary properties and binary properties in ontology representation. Binary semantic relations in ontologies are analyzed. Based on semantic relations, the Tree model and Network model of ontology representation are defined. Through analyzing entity data schema, semantic relations extraction and learning are also discussed in constitution of entity ontologies. The framework of entity fusion with ontology support has been designed. Finally entity matching and fusion methods are discussed.

A large volume of unstructured data, especially text data, is generated and exchanged daily. Consequently, the importance of extracting patterns and discovering knowledge from textual data is significantly increasing. As the task of automatically recognizing the relations between two or more entities, semantic relation extraction has a prominent role in the exploitation of raw text. This article surveys different approaches and types of relation extraction in English and the most prominent proposed methods in Persian. We also introduce, analyze, and compare the most important datasets available for relation extraction in Persian and English. Furthermore, traditional and emerging evaluation metrics for supervised, semi-supervised, and unsupervised methods are described, along with pointers to commonly used performance evaluation datasets. Finally, we briefly describe challenges in extracting relationships in Persian and English and dataset creation challenges.

Hierarchical learning strategy in semantic relation extraction

Adverse Outcome Pathways (AOPs) provide a structured framework for linking mechanistic events across biological organization levels, supporting chemical hazard identification and regulatory decision-making in Next-Generation Risk Assessment (NGRA). However, AOP development remains labor-intensive, requiring extensive literature searching and expert review to identify relevant events and supporting evidence. Existing text mining tools often operate on limited datasets, lack interactive capabilities, and miss contextually relevant studies. To address these challenges, we present S2CIE (Semantic & Syntactic Context-based Information Extraction), an interactive, real-time platform for extracting mechanistic information from biomedical literature. S2CIE annotates PubMed abstracts (32 million) at the word level with grammatical, entity-type, and dependency annotations, enabling custom extraction rules based on syntactic patterns, and employ semantic retrieval based on user specific query. The system supports near real-time querying and integrates entity recognition and visual exploration tools. We demonstrate S2CIE's utility through four case studies. First, we identified 110 chemicals linked to liver steatosis/cholestasis with 98.79% precision, extracting chemical and PPAR (peroxisome proliferator-activated receptors) interactions as molecular initiating events. Second enrichment analysis of literature derived genes and successful mapping with AOPs. Third, comparative analysis against AOP-HelpFinder for key event relationships in AOP 220 showed 42.2% increased evidence capture (1,614 vs 1,135 abstracts) with 99% reduced retrieval time. Fourth, large-scale extraction of post-translational modification events demonstrated domain-agnostic applicability beyond toxicology. S2CIE accelerates AOP evidence gathering, while maintaining transparency and auditability essential for regulatory application. S2CIE is available as open source through an API and as web application at https://dev.s2cie.insilicohub.org/.

Ever increasing volumes of media content and the desire to extract information from media archives motivate the studies into semantic audio information mining. Much research in this filed concerns development of bespoke systems, in which soundtracks are exclusively classified and segmented, and a specific type of sound is recognized and analyzed. This approach however is detrimental to the complete extraction of all relevant semantic information and audio scene analysis. The current study addresses the issues of soundtracks with overlapped music, speech and ambient sounds, and explores how MARSYAS (Music Analysis, Retrieval and Synthesis for Audio Signals) can be extended to mixed and overlapped soundtrack applications. The MARSYAS has been adapted to this application by means of adopting additional speech cleaning algorithms. The proposed new system can analyze arbitrary soundtracks and timestamp the occurrence of music and speech, allowing overlaps, in the form of a “sound score” for further recognition methods to extract music score and text information. Validation tests have shown that the new system handles overlapping cases and is therefore capable of extracting more information than other existing methods.

SLAM is a critical technology for enabling autonomous navigation and positioning in unmanned vehicles. Traditional visual simultaneous localization and mapping algorithms are built upon the assumption of a static scene, overlooking the impact of dynamic targets within real-world environments. Interference from dynamic targets can significantly degrade the system's localization accuracy or even lead to tracking failure. To address these issues, we propose a dynamic visual SLAM system named BY-SLAM, which is based on BEBLID and semantic information extraction. Initially, the BEBLID descriptor is introduced to describe Oriented FAST feature points, enhancing both feature point matching accuracy and speed. Subsequently, FasterNet replaces the backbone network of YOLOv8s to expedite semantic information extraction. By using the results of DBSCAN clustering object detection, a more refined semantic mask is obtained. Finally, by leveraging the semantic mask and epipolar constraints, dynamic feature points are discerned and eliminated, allowing for the utilization of only static feature points for pose estimation and the construction of a dense 3D map that excludes dynamic targets. Experimental evaluations are conducted on both the TUM RGB-D dataset and real-world scenarios and demonstrate the effectiveness of the proposed algorithm at filtering out dynamic targets within the scenes. On average, the localization accuracy for the TUM RGB-D dataset improves by 95.53% compared to ORB-SLAM3. Comparative analyses against classical dynamic SLAM systems further corroborate the improvement in localization accuracy, map readability, and robustness achieved by BY-SLAM.

Semantic relation extraction has been a known branch in Natural language processing allowing the identification of relations between words. Knowledge-based approaches including wordnet and pattern matching approaches such as Hearst for hypernyms are used among many others. The search for relations is limited to sentences which avert the rest of the text narrowing the semantic relation extraction. In this paper, we introduce the use of context for improving the extraction using global information from the text and domain ontology.

In the modern era, web data retrieval and data analytics play a crucial role for taking intelligent decisions in Internet of Things (IoT) environment. In IoT environment, various objects perform the tasks of sensing and computation for providing uninterrupted services (e.g., e-health, e- transportation, security access, etc.) to the end users. However, accessing the relevant and accurate information with reduced delay is still a challenging task in IoT environment. Although this aspect has been explored in the literature, the existing proposals have high complexity and require long time for accessing the relevant information from different IoT objects located across the globe. The information may be located across different web pages, which are linked together irrespective of their geographical locations. So, this paper addresses the issues such as accuracy, context- aware, reduced delay with low complexity in accessing the information from a remote device by the end users. In the proposed scheme, the strength of a web page which contains the relevant information to be fetched is judged by the quality of content and the inter- connections between different web pages. The proposed scheme simplifies the rank score calculation of these web pages and provides quality web pages at the top of the search result pages by demoting spam web pages. Bias connected web pages are verified by the linkage information of spam web pages. The Quality Assessment for Information Retrieval (QAIR) algorithm is proposed for the classification of web pages. The proposed algorithm computes the QAIR score by evaluating the web page quality. Microsoft Learning to Rank dataset has been used for the experiments, which consists of 239092 query-url pairs. By using this dataset, the computed QAIR score is compared with the PageRank score. This comparison determines the category of web page, i.e., either the page is strong or weak. The proposed scheme has been validated with decision tree followed by ten- fold cross validation, which results in an accuracy of 92.4%.

The proliferation of social media has the potential for changing the structure and organization of the web. In the past, scientists have looked at the web as a large connected component to understand how the topology of hyperlinks correlates with the quality of information contained in the page and they proposed techniques to rank information contained in web pages. We argue that information from web pages and network data on social relationships can be combined to create a personalized and socially connected web. In this paper, we look at the web as a composition of two networks, one consisting of information in web pages and the other of personal data shared on social media web sites. Together, they allow us to analyze how social media tunnels the flow of information from person to person and how to use the structure of the social network to rank, deliver, and organize information specifically for each individual user. We validate our social ranking concepts through a ranking experiment conducted on web pages that users shared on Google Buzz and Twitter.

Extracting semantic relations from the Quranic Arabic based on Arabic conjunctive patterns

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