A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

SEMANTIC NETWORK PRESENTATION OF MECHANICAL MOTION SCHEME AND ITS MECHANISM TYPES SELECTION METHOD

The presentation method of the mechanical motion scheme must support thewhole process of conceptual design. To meet the requirement, a semantic network method is selectedto represent process level, action level, mechanism level and relationships among them. Computeraided motion cycle chart exploration can be realized by the representation and revision of timecoordination of mechanism actions and their effect on the design scheme. The uncertain reasoningtechnology based on semantic network is applied in the mechanism types selection of the needledriving mechanism of industrial sewing mechanism, and the application indicated it is correct,useful and advance.

Semantic Link Network Based Knowledge Graph Representation and Construction

A knowledge graph consists of a set of interconnected typed entities and their attributes,which shows a better performance to organize,manage and understand knowledge.However,because knowledge graphs contain a lot of knowledge triples,it is difficult to directly display to researchers.Semantic Link Network is an attempt,and it can deal with the construction,representation and reasoning of semantics naturally.Based on the Semantic Link Network,this paper explores the representation and construction of knowledge graph,and develops an academic knowledge graph prototype system to realize the representation,construction and visualization of knowledge graph.

Environmental complaint insights through text mining based on the driver,pressure,state,impact,and response(DPSIR)framework:Evidence from an Italian environmental agency

Individuals,local communities,environmental associations,private organizations,and public representatives and bodies may all be aggrieved by environmental problems concerning poor air quality,illegal waste disposal,water contamination,and general pollution.Environmental complaints represent the expressions of dissatisfaction with these issues.As the timeconsuming of managing a large number of complaints,text mining may be useful for automatically extracting information on stakeholder priorities and concerns.The paper used text mining and semantic network analysis to crawl relevant keywords about environmental complaints from two online complaint submission systems:online claim submission system of Regional Agency for Prevention,Environment and Energy(Arpae)(“Contact Arpae”);and Arpae's internal platform for environmental pollution(“Environmental incident reporting portal”)in the Emilia-Romagna Region,Italy.We evaluated the total of 2477 records and classified this information based on the claim topic(air pollution,water pollution,noise pollution,waste,odor,soil,weather-climate,sea-coast,and electromagnetic radiation)and geographical distribution.Then,this paper used natural language processing to extract keywords from the dataset,and classified keywords ranking higher in Term Frequency-Inverse Document Frequency(TF-IDF)based on the driver,pressure,state,impact,and response(DPSIR)framework.This study provided a systemic approach to understanding the interaction between people and environment in different geographical contexts and builds sustainable and healthy communities.The results showed that most complaints are from the public and associated with air pollution and odor.Factories(particularly foundries and ceramic industries)and farms are identified as the drivers of environmental issues.Citizen believed that environmental issues mainly affect human well-being.Moreover,the keywords of“odor”,“report”,“request”,“presence”,“municipality”,and“hours”were the most influential and meaningful concepts,as demonstrated by their high degree and betweenness centrality values.Keywords connecting odor(classified as impacts)and air pollution(classified as state)were the most important(such as“odor-burnt plastic”and“odor-acrid”).Complainants perceived odor annoyance as a primary environmental concern,possibly related to two main drivers:“odor-factory”and“odorsfarms”.The proposed approach has several theoretical and practical implications:text mining may quickly and efficiently address citizen needs,providing the basis toward automating(even partially)the complaint process;and the DPSIR framework might support the planning and organization of information and the identification of stakeholder concerns and priorities,as well as metrics and indicators for their assessment.Therefore,integration of the DPSIR framework with the text mining of environmental complaints might generate a comprehensive environmental knowledge base as a prerequisite for a wider exploitation of analysis to support decision-making processes and environmental management activities.

Research on Networked Rapid Product Development Process

Today the cycle time of the product develop is requ ir ed to be shortened. At the same time the requirement of the customers becomes mo re and more diverse and complex. The capability of the develop unit is limited b ecause of the existence of heterogeneous systems and distributed environments. I n this paper, we bring forward a new approach to solve the problem in product de velopment process. We also settle part key technologies in it. A great deal of information from all kinds of sources in the distributed develop ment process is interweaved. The solution to organize the workflow and manage th e information in the process is called for anxiously. We use a new approach that is asynchronous and synchronous coupling product development approach based on the network. The approach extends the develop process from the time axis. Then t he activities in the process are organized from the asynchronous and synchronous aspects. The state of every activity projects at the ASN (active semantic netwo rk). The ASN includes decision system, intelligent agent, user interface and net work. The ASN decides the types and states of the activities and deals with the couple relationship among them. The knowledge stored in ASN is open to all users through the relative interfaces. Every specialist keeps contact with their user s relying on collaborative platform implements CSCW (computer support collaborat ive work) that integrated product/process design and development. The lack of gl obal communication in product development process can be prevented in the most d egree. The key technologies that exist in the asynchronous and synchronous coupling pro duct develop approach include: integrated development structure, orderly organiz ation of information, transparent management of process, agile transfer of infor mation and rapid prototype. The development process can be completed quickly by these technologies. The technologies involve wide content. In this paper, we dis cuss some key technologies. We validate the approach by the projectrapid response manufacturing a pplication in the distributed environment. The expensive device, high technology and low using lead to RE (Rapid engineering) and RP (Rapid prototype) service a pplication by the network. RE and RP develop rapidly due to the accelerated prod uct development process. RE and RP application service platform is built in the project.

A New Synthetical Knowledge Representation Model and Its Application in Data Flow Diagram

A new synthetical knowledge representation model that integrates the attribute grammar model with the semantic network model was presented. The model mainly uses symbols of attribute grammar to establish a set of syntax and semantic rules suitable for a semantic network. Based on the model,the paper introduces a formal method defining data flow diagrams (DFD) and also simply explains how to use the method.

TSPN: Term-Based Semantic Peer-to-Peer Networks

In this paper, we propose Term-based Semantic Peerto-Peer Networks （TSPN） to achieve semantic search. For each peer, TSPN builds a full text index of its documents. Through the analysis of resources, TSPN obtains series of terms, and distributes these terms into the network. Thus, TSPN can use query terms to locate appropriate peers to perform semantic search. Moreover, unlike the traditional structured P2P networks, TSPN uses the terms, not the peers, as the logical nodes of DHT. This can withstand the impact of network chum. The experimental results show that TSPN has better performance compared with the existing P2P semantic searching algorithms.

Deep Neural Semantic Network for Keywords Extraction on Short Text

Keyword extraction is a branch of natural language processing,which plays an important role in many tasks,such as long text classification,automatic summary,machine translation,dialogue system,etc.All of them need to use high-quality keywords as a starting point.In this paper,we propose a deep learning network called deep neural semantic network(DNSN)to solve the problem of short text keyword extraction.It can map short text and words to the same semantic space,get the semantic vector of them at the same time,and then compute the similarity between short text and words to extract top-ranked words as keywords.The Bidirectional Encoder Representations from Transformers was first used to obtain the initial semantic feature vectors of short text and words,and then feed the initial semantic feature vectors to the residual network so as to obtain the final semantic vectors of short text and words at the same vector space.Finally,the keywords were extracted by calculating the similarity between short text and words.Compared with existed baseline models including Frequency,Term Frequency Inverse Document Frequency(TF-IDF)and Text-Rank,the model proposed is superior to the baseline models in Precision,Recall,and F-score on the same batch of test dataset.In addition,the precision,recall,and F-score are 6.79%,5.67%,and 11.08%higher than the baseline model in the best case,respectively.

Ontology-Driven Mashup Auto-Completion on a Data API Network

The building of data mashups is complicated and error-prone, because this process requires not only finding suitable APIs but also combining them in an appropriate way to get the desired result. This paper describes an ontology-driven mashup auto-completion approach for a data API network to facilitate this task. First, a microformats-based ontology was defined to describe the attributes and activities of the data APIs. A semantic Bayesian network （sBN） and a semantic graph template were used for the link prediction on the Semantic Web and to construct a data API network denoted as Np. The performance is improved by a semi-supervised learning method which uses both labeled and unlabeled data. Then, this network is used to build an ontology-driven mashup auto-completion system to help users build mashups by providing three kinds of recommendations. Tests demonstrate that the approach has a precisionp of about 80%, recallp of about 60%, and F0.5 of about 70% for predicting links between APIs. Compared with the API network Ne com-posed of existing links on the current Web, Np contains more links including those that should but do not exist. The ontology-driven mashup auto-completion system gives a much better recallr and discounted cumula-tive gain （DCG） on Np than on Ne. The tests suggest that this approach gives users more creativity by constructing the API network through predicting mashup APIs rather than using only existing links on the Web.

Interoperability for Global Observation Data by Ontological Information

The Ontology registry system is developed to collect, manage, and compare ontological information for integrating global observation data. Data sharing and data service such as support of metadata deign, structuring of data contents, support of text mining are applied for better use of data as data interoperability. Semantic network dictionary and gazetteers are constructed as a trans-disciplinary dictionary. Ontological information is added to the system by digitalizing text based dictionaries, developing 'knowledge writing tool' for experts, and extracting semantic relations from authoritative documents with natural language processing technique. The system is developed to collect lexicographic ontology and geographic ontology.

An Approach of Knowledge-Based Programming in Postal Service

The postal service includes many items,such as EMS,Parcel,express letter, registered letter andmoney orders,each of which needs complex rules to calculate postage and to deal the backpound process.The present situation is that items are increasing and rules are changing,and vary hem one post office toanother. How to design a computer system to deal with the services and to suit most post offices at the sametime is the key problem.In this paper, knowledge-based programming method is adopted,and forther a newintentted model,'product-rule database-semantic network'is given to design the system.