477,559 research outputs found
Discovering Utility-driven Interval Rules
For artificial intelligence, high-utility sequential rule mining (HUSRM) is a
knowledge discovery method that can reveal the associations between events in
the sequences. Recently, abundant methods have been proposed to discover
high-utility sequence rules. However, the existing methods are all related to
point-based sequences. Interval events that persist for some time are common.
Traditional interval-event sequence knowledge discovery tasks mainly focus on
pattern discovery, but patterns cannot reveal the correlation between interval
events well. Moreover, the existing HUSRM algorithms cannot be directly applied
to interval-event sequences since the relation in interval-event sequences is
much more intricate than those in point-based sequences. In this work, we
propose a utility-driven interval rule mining (UIRMiner) algorithm that can
extract all utility-driven interval rules (UIRs) from the interval-event
sequence database to solve the problem. In UIRMiner, we first introduce a
numeric encoding relation representation, which can save much time on relation
computation and storage on relation representation. Furthermore, to shrink the
search space, we also propose a complement pruning strategy, which incorporates
the utility upper bound with the relation. Finally, plentiful experiments
implemented on both real-world and synthetic datasets verify that UIRMiner is
an effective and efficient algorithm.Comment: Preprint. 11 figures, 5 table
Symbolic methodology for numeric data mining
Currently statistical and artificial neural network methods dominate in data mining applications. Alternative relational (symbolic) data mining methods have shown their effectiveness in robotics, drug design, and other areas. Neural networks and decision tree methods have serious limitations in capturing relations that may have a variety of forms. Learning systems based on symbolic first-order logic (FOL) representations capture relations naturally. The learned regularities are understandable directly in domain terms that help to build a domain theory. This paper describes relational data mining methodology and develops it further for numeric data such as financial and spatial data. This includes (1) comparing the attribute-value representation with the relational representation, (2) defining a new concept of joint relational representations, (3) a process of their use, and the Discovery algorithm. This methodology handles uniformly the numerical and interval forecasting tasks as well as classification tasks. It is shown that Relational Data Mining (RDM) can handle multiple constrains, initial rules and background knowledge very naturally to reduce the search space in contrast with attribute-based data mining. Theoretical concepts are illustrated with examples from financial and image processing domains
A Process Modelling Framework Based on Point Interval Temporal Logic with an Application to Modelling Patient Flows
This thesis considers an application of a temporal theory to describe and model the patient journey in the hospital accident and emergency (A&E) department. The aim is to introduce a generic but dynamic method applied to any setting, including healthcare. Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modelling techniques used in healthcare such as flowcharts, unified modelling language activity diagram (UML AD), and business process modelling notation (BPMN) are intuitive and imprecise. They cannot fully capture the complexities of the types of activities and the full extent of temporal constraints to an extent where one could reason about the flows. Formal approaches such as Petri have also been reviewed to investigate their applicability to the healthcare domain to model processes.
Additionally, to schedule patient flows, current modelling standards do not offer any formal mechanism, so healthcare relies on critical path method (CPM) and program evaluation review technique (PERT), that also have limitations, i.e. finish-start barrier. It is imperative to specify the temporal constraints between the start and/or end of a process, e.g., the beginning of a process A precedes the start (or end) of a process B. However, these approaches failed to provide us with a mechanism for handling these temporal situations. If provided, a formal representation can assist in effective knowledge representation and quality enhancement concerning a process. Also, it would help in uncovering complexities of a system and assist in modelling it in a consistent way which is not possible with the existing modelling techniques.
The above issues are addressed in this thesis by proposing a framework that would provide a knowledge base to model patient flows for accurate representation based on point interval temporal logic (PITL) that treats point and interval as primitives. These objects would constitute the knowledge base for the formal description of a system. With the aid of the inference mechanism of the temporal theory presented here, exhaustive temporal constraints derived from the proposed axiomatic system’ components serves as a knowledge base.
The proposed methodological framework would adopt a model-theoretic approach in which a theory is developed and considered as a model while the corresponding instance is considered as its application. Using this approach would assist in identifying core components of the system and their precise operation representing a real-life domain deemed suitable to the process modelling issues specified in this thesis. Thus, I have evaluated the modelling standards for their most-used terminologies and constructs to identify their key components. It will also assist in the generalisation of the critical terms (of process modelling standards) based on their ontology. A set of generalised terms proposed would serve as an enumeration of the theory and subsume the core modelling elements of the process modelling standards. The catalogue presents a knowledge base for the business and healthcare domains, and its components are formally defined (semantics). Furthermore, a resolution theorem-proof is used to show the structural features of the theory (model) to establish it is sound and complete.
After establishing that the theory is sound and complete, the next step is to provide the instantiation of the theory. This is achieved by mapping the core components of the theory to their corresponding instances. Additionally, a formal graphical tool termed as point graph (PG) is used to visualise the cases of the proposed axiomatic system. PG facilitates in modelling, and scheduling patient flows and enables analysing existing models for possible inaccuracies and inconsistencies supported by a reasoning mechanism based on PITL. Following that, a transformation is developed to map the core modelling components of the standards into the extended PG (PG*) based on the semantics presented by the axiomatic system.
A real-life case (from the King’s College hospital accident and emergency (A&E) department’s trauma patient pathway) is considered to validate the framework. It is divided into three patient flows to depict the journey of a patient with significant trauma, arriving at A&E, undergoing a procedure and subsequently discharged. Their staff relied upon the UML-AD and BPMN to model the patient flows. An evaluation of their representation is presented to show the shortfalls of the modelling standards to model patient flows. The last step is to model these patient flows using the developed approach, which is supported by enhanced reasoning and scheduling
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs
Most knowledge graph completion (KGC) methods learn latent representations of
entities and relations of a given graph by mapping them into a vector space.
Although the majority of these methods focus on static knowledge graphs, a
large number of publicly available KGs contain temporal information stating the
time instant/period over which a certain fact has been true. Such graphs are
often known as temporal knowledge graphs. Furthermore, knowledge graphs may
also contain textual descriptions of entities and relations. Both temporal
information and textual descriptions are not taken into account during
representation learning by static KGC methods, and only structural information
of the graph is leveraged. Recently, some studies have used temporal
information to improve link prediction, yet they do not exploit textual
descriptions and do not support inductive inference (prediction on entities
that have not been seen in training).
We propose a novel framework called TEMT that exploits the power of
pre-trained language models (PLMs) for text-enhanced temporal knowledge graph
completion. The knowledge stored in the parameters of a PLM allows TEMT to
produce rich semantic representations of facts and to generalize on previously
unseen entities. TEMT leverages textual and temporal information available in a
KG, treats them separately, and fuses them to get plausibility scores of facts.
Unlike previous approaches, TEMT effectively captures dependencies across
different time points and enables predictions on unseen entities. To assess the
performance of TEMT, we carried out several experiments including time interval
prediction, both in transductive and inductive settings, and triple
classification. The experimental results show that TEMT is competitive with the
state-of-the-art.Comment: 10 pages, 3 figure
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