SPORTAL

2016 ◽  
Vol 12 (3) ◽  
pp. 134-163 ◽  
Author(s):  
Ali Hasnain ◽  
Qaiser Mehmood ◽  
Syeda Sana e Zainab ◽  
Aidan Hogan
Keyword(s):  
Knowledge Bases ◽  
Best Effort ◽  
Sparql Endpoint ◽  
Large Subset ◽  
Aggregate Queries ◽  
Online Catalogue ◽  
The Web

Access to hundreds of knowledge bases has been made available on the Web through public SPARQL endpoints. Unfortunately, few endpoints publish descriptions of their content (e.g., using VoID). It is thus unclear how agents can learn about the content of a given SPARQL endpoint or, relatedly, find SPARQL endpoints with content relevant to their needs. In this paper, the authors investigate the feasibility of a system that gathers information about public SPARQL endpoints by querying them directly about their own content. With the advent of SPARQL 1.1 and features such as aggregates, it is now possible to specify queries whose results would form a detailed profile of the content of the endpoint, comparable with a large subset of VoID. In theory it would thus be feasible to build a rich centralised catalogue describing the content indexed by individual endpoints by issuing them SPARQL (1.1) queries; this catalogue could then be searched and queried by agents looking for endpoints with content they are interested in. In practice, however, the coverage of the catalogue is bounded by the limitations of public endpoints themselves: some may not support SPARQL 1.1, some may return partial responses, some may throw exceptions for expensive aggregate queries, etc. The authors' goal in this paper is thus twofold: (i) using VoID as a bar, to empirically investigate the extent to which public endpoints can describe their own content, and (ii) to build and analyse the capabilities of a best-effort online catalogue of current endpoints based on the (partial) results collected.

SPORTAL

2018 ◽  
pp. 368-401
Author(s):  
Ali Hasnain ◽  
Qaiser Mehmood ◽  
Syeda Sana e Zainab ◽  
Aidan Hogan
Keyword(s):  
Knowledge Bases ◽  
Best Effort ◽  
Sparql Endpoint ◽  
Large Subset ◽  
Aggregate Queries ◽  
Online Catalogue ◽  
The Web

Access to hundreds of knowledge bases has been made available on the Web through public SPARQL endpoints. Unfortunately, few endpoints publish descriptions of their content (e.g., using VoID). It is thus unclear how agents can learn about the content of a given SPARQL endpoint or, relatedly, find SPARQL endpoints with content relevant to their needs. In this paper, the authors investigate the feasibility of a system that gathers information about public SPARQL endpoints by querying them directly about their own content. With the advent of SPARQL 1.1 and features such as aggregates, it is now possible to specify queries whose results would form a detailed profile of the content of the endpoint, comparable with a large subset of VoID. In theory it would thus be feasible to build a rich centralised catalogue describing the content indexed by individual endpoints by issuing them SPARQL (1.1) queries; this catalogue could then be searched and queried by agents looking for endpoints with content they are interested in. In practice, however, the coverage of the catalogue is bounded by the limitations of public endpoints themselves: some may not support SPARQL 1.1, some may return partial responses, some may throw exceptions for expensive aggregate queries, etc. The authors' goal in this paper is thus twofold: (i) using VoID as a bar, to empirically investigate the extent to which public endpoints can describe their own content, and (ii) to build and analyse the capabilities of a best-effort online catalogue of current endpoints based on the (partial) results collected.

Cataloguing the Context of Public SPARQL Endpoints

2018 ◽  
pp. 295-328
Author(s):  
Ali Hasnain ◽  
Qaiser Mehmood ◽  
Syeda Sana e Zainab ◽  
Aidan Hogan
Keyword(s):  
Knowledge Bases ◽  
Aggregate Queries ◽  
Online Catalogue ◽  
The Web

Access to hundreds of knowledge bases has been made available on the Web through SPARQL endpoints. Unfortunately, few endpoints publish descriptions of their content. It is thus unclear how agents can learn about the content of a given endpoint. This research investigates the feasibility of a system that gathers information about public endpoints by querying directly about their own content. It would thus be feasible to build a centralised catalogue describing the content indexed by individual endpoints by issuing them SPARQL 1.1 queries; this catalogue could be searched and queried by agents looking for endpoints with content they are interested in. However, the coverage of the catalogue is bounded by the limitations of public endpoints themselves: some may not support SPARQL 1.1, some may return partial responses, some may throw exceptions for expensive aggregate queries, etc. The goal is twofold: 1) using VoID as a bar, to empirically investigate the extent to which endpoints can describe their own content, and 2) to build and analyse the capabilities of an online catalogue.

scholarly journals Improving the Quality of Linked Data Using Statistical Distributions

2018 ◽  
pp. 1638-1664 ◽  
Author(s):  
Heiko Paulheim ◽  
Christian Bizer
Keyword(s):  
Knowledge Base ◽  
Linked Data ◽  
Relational Databases ◽  
Knowledge Bases ◽  
Structured Data ◽  
Data Sources ◽  
Data Sets ◽  
Statistical Distributions ◽  
The Web

Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.

The Evolution of the (Hidden) Web and its Hidden Data

2015 ◽  
pp. 1-30 ◽  
Author(s):  
Manuel Álvarez Díaz ◽  
Víctor Manuel Prieto Álvarez ◽  
Fidel Cacheda Seijo
Keyword(s):  
Large Subset ◽  
Hidden Web ◽  
Hidden Data ◽  
The Web

This paper presents an analysis of the most important features of the Web and its evolution and implications on the tools that traverse it to index its content to be searched later. It is important to remark that some of these features of the Web make a quite large subset to remain “hidden”. The analysis of the Web focuses on a snapshot of the Global Web for six different years: 2009 to 2014. The results for each year are analyzed independently and together to facilitate the analysis of both the features at any given time and the changes between the different analyzed years. The objective of the analysis are twofold: to characterize the Web and more importantly, its evolution along the time.

Web knowledge

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Web Applications ◽  
Semantic Network ◽  
Knowledge Bases ◽  
Web Standards ◽  
Description Framework ◽  
The Web

In the introductory chapter of this book, we discussed the means by which knowledge can be made available on the Web. That is, the representation of the knowledge in a form by which it can be automatically processed by a computer. To recap, we identified two essential steps that were deemed necessary to achieve this task: 1. We discussed the need to agree on a suitable structure for the knowledge that we wish to represent. This is achieved through the construction of a semantic network, which defines the main concepts of the knowledge, and the relationships between these concepts. We presented an example network that contained the main concepts to differentiate between kinds of cameras. Our network is a conceptualization, or an abstract view of a small part of the world. A conceptualization is defined formally in an ontology, which is in essence a vocabulary for knowledge representation. 2. We discussed the construction of a knowledge base, which is a store of knowledge about a domain in machine-processable form; essentially a database of knowledge. A knowledge base is constructed through the classification of a body of information according to an ontology. The result will be a store of facts and rules that describe the domain. Our example described the classification of different camera features to form a knowledge base. The knowledge base is expressed formally in the language of the ontology over which it is defined. In this chapter we elaborate on these two steps to show how we can define ontologies and knowledge bases specifically for the Web. This will enable us to construct Semantic Web applications that make use of this knowledge. The chapter is devoted to a detailed explanation of the syntax and pragmatics of the RDF, RDFS, and OWL Semantic Web standards. The resource description framework (RDF) is an established standard for knowledge representation on the Web. Taken together with the associated RDF Schema (RDFS) standard, we have a language for representing simple ontologies and knowledge bases on the Web.

A Cognitive-Based Approach to Identify Topics in Text Using the Web as a Knowledge Source

2011 ◽  
pp. 61-78 ◽  
Author(s):  
Louis Massey ◽  
Wilson Wong
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Knowledge Bases ◽  
Human Cognition ◽  
Web Pages ◽  
Topic Identification ◽  
Unstructured Text ◽  
Text Information ◽  
Processing Techniques ◽  
The Web

This chapter explores the problem of topic identification from text. It is first argued that the conventional representation of text as bag-of-words vectors will always have limited success in arriving at the underlying meaning of text until the more fundamental issues of feature independence in vector-space and ambiguity of natural language are addressed. Next, a groundbreaking approach to text representation and topic identification that deviates radically from current techniques used for document classification, text clustering, and concept discovery is proposed. This approach is inspired by human cognition, which allows ‘meaning’ to emerge naturally from the activation and decay of unstructured text information retrieved from the Web. This paradigm shift allows for the exploitation rather than avoidance of dependence between terms to derive meaning without the complexity introduced by conventional natural language processing techniques. Using the unstructured texts in Web pages as a source of knowledge alleviates the laborious handcrafting of formal knowledge bases and ontologies that are required by many existing techniques. Some initial experiments have been conducted, and the results are presented in this chapter to illustrate the power of this new approach.

SPedia

2018 ◽  
pp. 1576-1596
Author(s):  
Muhammad Ahtisham Aslam ◽  
Naif Radi Aljohani
Keyword(s):  
Knowledge Base ◽  
Open Data ◽  
Data Publishing ◽  
High Quality ◽  
Scientific Publications ◽  
Sparql Endpoint ◽  
Web Of Data ◽  
Intelligent Searching ◽  
The Web

Producing the Linked Open Data (LOD) is getting potential to publish high-quality interlinked data. Publishing such data facilitates intelligent searching from the Web of data. In the context of scientific publications, data about millions of scientific documents published by hundreds and thousands of publishers is in silence as it is not published as open data and ultimately is not linked to other datasets. In this paper the authors present SPedia: a semantically enriched knowledge base of data about scientific documents. SPedia knowledge base provides information on more than nine million scientific documents, consisting of more than three hundred million RDF triples. These extracted datasets, allow users to put sophisticated queries by employing semantic Web techniques instead of relying on keyword-based searches. This paper also shows the quality of extracted data by performing sample queries through SPedia SPARQL Endpoint and analyzing results. Finally, the authors describe that how SPedia can serve as central hub for the cloud of LOD of scientific publications.

Knowing What We Know : Supporting Knowledge Creation and Sharing in Social Networks

2003 ◽  
Author(s):  
Rob Cross ◽  
Andrew Parker
Keyword(s):  
Virtual Work ◽  
Knowledge Bases ◽  
Massachusetts Institute Of Technology ◽  
Codified Knowledge ◽  
Significant Performance ◽  
Informational Environment ◽  
Institute Of Technology ◽  
Knowledge Intensive ◽  
Intensive Work ◽  
The Web

The way in which this manager relied on his network to obtain information and knowledge critical to the success of an important project is common and likely resonates with your own experience. Usually when we think of where people turn for information or knowledge we think of databases, the Web, intranets and portals or other, more traditional, repositories such as file cabinets or policy and procedure manuals. However, a significant component of a person’s information environment consists of the relationships he or she can tap for various informational needs. For example, in summarizing a decade worth of studies, Tom Allen of Massachusetts Institute of Technology (MIT) found that engineers and scientists were roughly five times more likely to turn to a person for information than to an impersonal source such as a database or a file cabinet. In other settings, research has consistently shown that who you know has a significant impact on what you come to know, as relationships are critical for obtaining information, solving problems, and learning how to do your work. Particularly in knowledge-intensive work, creating an informational environment that helps employees solve increasingly complex and often ambiguous problems holds significant performance implications. Frequently such efforts entail knowledge-management initiatives focusing on the capture and sharing of codified knowledge and reusable work products. To be sure, these so-called knowledge bases hold pragmatic benefits. They bridge boundaries of time and space, allow for potential reuse of tools or work products employed successfully in other areas of an organization, and provide a means of reducing organizational “forgetting” as a function of employee turnover. However, such initiatives often undervalue crucial knowledge held by employees and the web of relationships that help dynamically solve problems and create new knowledge. As we move further into an economy where collaboration and innovation are increasingly central to organizational effectiveness, we must pay more attention to the sets of relationships that people rely on to accomplish their work. Certainly we can expect emerging collaborative technologies to facilitate virtual work and skill-profiling systems to help with the location of relevant expertise.

scholarly journals A Survey on Aspect-Based Sentiment Classification

2021 ◽  
Author(s):  
Gianni Brauwers ◽  
Flavius Frasincar
Keyword(s):  
State Of The Art ◽  
Knowledge Bases ◽  
Sentiment Classification ◽  
Automatic Extraction ◽  
Learning Models ◽  
Text Documents ◽  
Fine Grained ◽  
Knowledge Based ◽  
Transformer Model ◽  
The Web

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

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