Toward the automation of business process ontology generation

Semantic Business Process Management (SBPM) utilises semantic technologies (e.g., ontology) to model and query process representations. There are times in which such models must be reconstructed from existing textual documentation. In this scenario the automated generation of ontological models would be preferable, however current methods and technology are still not capable of automatically generating accurate semantic process models from textual descriptions. This research attempts to automate the process as much as possible by proposing a method that drives the transformation through the joint use of a foundational ontology and lexico-semantic analysis. The method is presented, demonstrated and evaluated. The original dataset represents 150 business activities related to the procurement processes of a case study company. As the evaluation shows, the proposed method can accurately map the linguistic patterns of the process descriptions to semantic patterns of the foundational ontology to a high level of accuracy, however further research is required in order to reduce the level of human intervention, expand the method so as to recognise further patterns of the foundational ontology and develop a tool to assist the business process modeller in the semi-automated generation of process models

Advanced Knowledge Technologies at the Midterm: Tools and Methods for the Semantic Web

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962:Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)Over forty years later, the seamless interplay that McLuhan demanded between our technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet the integration of electronic systems as open systems remains in its infancy.Advanced Knowledge Technologies (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the services and technologies that such unification will require. Half way through its sixyear span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.The WWW provided the original context that made the AKT approach to knowledge management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle. The combination of this expertise, and the time and space afforded the consortium by the IRC structure, suggested the opportunity for a concerted effort to develop an approach to advanced knowledge technologies, based on the WWW as a basic infrastructure.The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.The SW, as an extension of the WWW, provides an interesting set of constraints to the knowledge management services AKT tries to provide. As a medium for the semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing ontologies to create a third). Ontology mapping, and the elimination of conflicts of reference, will be important tasks. All of these issues are discussed along with our proposed technologies.Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering metaservices that are envisaged will have to deal with this heterogeneity.The emerging picture of the SW is one of great opportunity but it will not be a wellordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts. But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which semantic hygiene prevails interesting enough to reason in? These and many other questions need to be addressed if we are to provide effective knowledge technologies for our content on the web

Enriching videos with light semantics

This paper describes an ongoing prototypical framework to annotate and retrieve web videos with light semantics. The proposed framework reuses many existing vocabularies along with a video model. The knowledge is captured from three different information spaces (media content, context, document). We also describe ways to extract the semantic content descriptions from the existing usergenerated content using multiple approaches of linguistic processing and Named Entity Recognition, which are later identified with DBpedia resources to establish meanings for the tags. Finally, the implemented prototype is described with multiple search interfaces and retrieval processes. Evaluation on semantic enrichment shows a considerable (50% of videos) improvement in content description

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

Opening up Magpie via semantic services

Magpie is a suite of tools supporting a ‘zero-cost’ approach to semantic web browsing: it avoids the need for manual annotation by automatically associating an ontology-based semantic layer to web resources. An important aspect of Magpie, which differentiates it from superficially similar hypermedia systems, is that the association between items on a web page and semantic concepts is not merely a mechanism for dynamic linking, but it is the enabling condition for locating services and making them available to a user. These services can be manually activated by a user (pull services), or opportunistically triggered when the appropriate web entities are encountered during a browsing session (push services). In this paper we analyze Magpie from the perspective of building semantic web applications and we note that earlier implementations did not fulfill the criterion of “open as to services”, which is a key aspect of the emerging semantic web. For this reason, in the past twelve months we have carried out a radical redesign of Magpie, resulting in a novel architecture, which is open both with respect to ontologies and semantic web services. This new architecture goes beyond the idea of merely providing support for semantic web browsing and can be seen as a software framework for designing and implementing semantic web applications

The quest for information retrieval on the semantic web

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal has been tested on corpora of significant size, showing promising results with respect to keyword-based search, and providing ground for further analysis and research

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Automatically Detecting the Resonance of Terrorist Movement Frames on the Web

The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups

HOLMES: A Hybrid Ontology-Learning Materials Engineering System

Designing and discovering novel materials is challenging problem in many domains such as fuel additives, composites, pharmaceuticals, and so on. At the core of all this are models that capture how the different domain-specific data, information, and knowledge regarding the structures and properties of the materials are related to one another. This dissertation explores the difficult task of developing an artificial intelligence-based knowledge modeling environment, called Hybrid Ontology-Learning Materials Engineering System (HOLMES) that can assist humans in populating a materials science and engineering ontology through automatic information extraction from journal article abstracts. While what we propose may be adapted for a generic materials engineering application, our focus in this thesis is on the needs of the pharmaceutical industry. We develop the Columbia Ontology for Pharmaceutical Engineering (COPE), which is a modification of the Purdue Ontology for Pharmaceutical Engineering. COPE serves as the basis for HOLMES. The HOLMES framework starts with journal articles that are in the Portable Document Format (PDF) and ends with the assignment of the entries in the journal articles into ontologies. While this might seem to be a simple task of information extraction, to fully extract the information such that the ontology is filled as completely and correctly as possible is not easy when considering a fully developed ontology. In the development of the information extraction tasks, we note that there are new problems that have not arisen in previous information extraction work in the literature. The first is the necessity to extract auxiliary information in the form of concepts such as actions, ideas, problem specifications, properties, etc. The second problem is in the existence of multiple labels for a single token due to the existence of the aforementioned concepts. These two problems are the focus of this dissertation. In this work, the HOLMES framework is presented as a whole, describing our successful progress as well as unsolved problems, which might help future research on this topic. The ontology is then presented to help in the identification of the relevant information that needs to be retrieved. The annotations are next developed to create the data sets necessary for the machine learning algorithms to perform. Then, the current level of information extraction for these concepts is explored and expanded. This is done through the introduction of entity feature sets that are based on previously extracted entities from the entity recognition task. And finally, the new task of handling multiple labels for tagging a single entity is also explored by the use of multiple-label algorithms used primarily in image processing

SCRE:special cargo relation extraction using representation learning

The airfreight industry of shipping goods with special handling needs, also known as special cargo, often deals with non-transparent data and outdated technology, resulting in significant inefficiency. A special cargo ontology is a means of extracting, structuring, and storing domain knowledge and representing the concepts and relationships that can be processed by computers. This ontology can be used as the base of semantic data retrieval in many artificial intelligence applications, such as planning for special cargo shipments. Domain information extraction is an essential task in implementing and maintaining special cargo ontology. However, the absence of domain information makes instantiating the cargo ontology challenging. We propose a relation representation learning approach based on a hierarchical attention-based multi-task model and leverage it in the special cargo domain. The proposed relation representation learning architecture is applied for identifying and categorizing samples of various relation types in the special cargo ontology. The model is trained with domain-specific documents on a number of semantic tasks that vary from lightweight tasks in the bottom layers to the heavyweight tasks in the top layers of the model in a hierarchical setting. Therefore, it conveys complementary input features and learns a rich representation. We also train a domain-specific relation representation model that relies only on an entity-linked corpus of cargo shipment domain. These two relation representation models are then employed in a supervised multi-class classifier called Special Cargo Relation Extractor (SCRE). The results of the experiments show that the proposed relation representation models can represent the complex semantic information of the special cargo domain efficiently.</p