158 research outputs found
Truth maintenance in knowledge-based systems
Truth Maintenance Systems (TMS) have been applied in a wide range of domains, from diagnosing electric circuits to belief revision in agent systems. There also has been work on using the TMS in modern Knowledge-Based Systems such as intelligent agents and ontologies. This thesis investigates the applications of TMSs in such systems.
For intelligent agents, we use a “light-weight” TMS to support query caching in agent programs. The TMS keeps track of the dependencies between a query and the facts used to derive it so that when the agent updates its database, only affected queries are invalidated and removed from the cache. The TMS employed here is “light-weight” as it does not maintain all intermediate reasoning results. Therefore, it is able to reduce memory consumption and to improve performance in a dynamic setting such as in multi-agent systems.
For ontologies, this work extends the Assumption-based Truth Maintenance System (ATMS) to tackle the problem of axiom pinpointing and debugging in ontology-based systems with different levels of expressivity. Starting with finding all errors in auto-generated ontology mappings using a “classic” ATMS [23], we extend the ATMS to solve the axiom pinpointing problem in Description Logics-based Ontologies. We also attempt this approach to solve the axiom pinpointing problem in a more expressive upper ontology, SUMO, whose underlying logic is undecidable
Constructive Reasoning for Semantic Wikis
One of the main design goals of social software, such as wikis, is to
support and facilitate interaction and collaboration. This dissertation
explores challenges that arise from extending social software with
advanced facilities such as reasoning and semantic annotations and
presents tools in form of a conceptual model, structured tags, a rule
language, and a set of novel forward chaining and reason maintenance
methods for processing such rules that help to overcome the
challenges.
Wikis and semantic wikis were usually developed in an ad-hoc
manner, without much thought about the underlying concepts. A conceptual
model suitable for a semantic wiki that takes advanced features
such as annotations and reasoning into account is proposed. Moreover,
so called structured tags are proposed as a semi-formal knowledge
representation step between informal and formal annotations.
The focus of rule languages for the Semantic Web has been predominantly
on expert users and on the interplay of rule languages
and ontologies. KWRL, the KiWi Rule Language, is proposed as a
rule language for a semantic wiki that is easily understandable for
users as it is aware of the conceptual model of a wiki and as it
is inconsistency-tolerant, and that can be efficiently evaluated as it
builds upon Datalog concepts.
The requirement for fast response times of interactive software
translates in our work to bottom-up evaluation (materialization) of
rules (views) ahead of time – that is when rules or data change, not
when they are queried. Materialized views have to be updated when
data or rules change. While incremental view maintenance was intensively
studied in the past and literature on the subject is abundant,
the existing methods have surprisingly many disadvantages – they
do not provide all information desirable for explanation of derived
information, they require evaluation of possibly substantially larger
Datalog programs with negation, they recompute the whole extension
of a predicate even if only a small part of it is affected by a
change, they require adaptation for handling general rule changes.
A particular contribution of this dissertation consists in a set of
forward chaining and reason maintenance methods with a simple declarative
description that are efficient and derive and maintain information
necessary for reason maintenance and explanation. The reasoning
methods and most of the reason maintenance methods are described
in terms of a set of extended immediate consequence operators the
properties of which are proven in the classical logical programming
framework. In contrast to existing methods, the reason maintenance methods in this dissertation work by evaluating the original Datalog
program – they do not introduce negation if it is not present in the input
program – and only the affected part of a predicate’s extension is
recomputed. Moreover, our methods directly handle changes in both
data and rules; a rule change does not need to be handled as a special
case.
A framework of support graphs, a data structure inspired by justification
graphs of classical reason maintenance, is proposed. Support
graphs enable a unified description and a formal comparison of the
various reasoning and reason maintenance methods and define a notion
of a derivation such that the number of derivations of an atom is
always finite even in the recursive Datalog case.
A practical approach to implementing reasoning, reason maintenance,
and explanation in the KiWi semantic platform is also investigated. It
is shown how an implementation may benefit from using a graph
database instead of or along with a relational database
Truth maintenance in knowledge-based systems
Truth Maintenance Systems (TMS) have been applied in a wide range of domains, from diagnosing electric circuits to belief revision in agent systems. There also has been work on using the TMS in modern Knowledge-Based Systems such as intelligent agents and ontologies. This thesis investigates the applications of TMSs in such systems.
For intelligent agents, we use a “light-weight” TMS to support query caching in agent programs. The TMS keeps track of the dependencies between a query and the facts used to derive it so that when the agent updates its database, only affected queries are invalidated and removed from the cache. The TMS employed here is “light-weight” as it does not maintain all intermediate reasoning results. Therefore, it is able to reduce memory consumption and to improve performance in a dynamic setting such as in multi-agent systems.
For ontologies, this work extends the Assumption-based Truth Maintenance System (ATMS) to tackle the problem of axiom pinpointing and debugging in ontology-based systems with different levels of expressivity. Starting with finding all errors in auto-generated ontology mappings using a “classic” ATMS [23], we extend the ATMS to solve the axiom pinpointing problem in Description Logics-based Ontologies. We also attempt this approach to solve the axiom pinpointing problem in a more expressive upper ontology, SUMO, whose underlying logic is undecidable
Explanation and diagnosis services for unsatisfiability and inconsistency in description logics
Description Logics (DLs) are a family of knowledge representation formalisms with formal semantics and well understood computational complexities. In recent years, they have found applications in many domains, including domain modeling, software engineering, configuration, and the Semantic Web. DLs have deeply influenced the design and standardization of the Web Ontology Language OWL. The acceptance of OWL as a web standard has reciprocally resulted in the widespread use of DL ontologies on the web. As more applications emerge with increasing complexity, non-standard reasoning services, such as explanation and diagnosis, have become important capabilities that a DL reasoner should provide. For example, unsatisfiability and inconsistency may arise in an ontology due to unintentional design defects or changes in the ontology evolution process. Without explanations, searching for the cause is like looking for a needle in a haystack. It is, therefore, surprising that most of the existing DL reasoners do not provide explanation services; they provide "Yes/No" answers to satisfiability or consistency queries without giving any reasons. This thesis presents our solution for providing explanation and diagnosis services for DL reasoners. We firstly propose a framework based on resolution to explain inconsistency and unsatisfiability in Description Logic. A sound and complete algorithm is developed to generate explanations for the DL language [Special characters omitted.] ALCHI based on the unsatisfiability and inconsistency patterns in [Special characters omitted.] ALCHI . We also develop a technique based on Shapley values to measure inconsistencies in ontologies for diagnosis purposes. This measure is used to identify which axioms in an input ontology or which parts of these axioms need to be repaired in order to make the input consistent. We also investigate optimization techniques to compute the inconsistency measures based on particular properties of DLs. Based on the above theoretical foundations, a running prototype system is implemented to evaluate the practicability of the proposed services. Our preliminary empirical results show that the resolution based explanation framework and the diagnosis procedure based on inconsistency measures can be applied in the real world application
Scaling Up Description Logic Reasoning by Distributed Resolution
Benefits from structured knowledge representation have motivated the creation of large description logic ontologies. For accessing implicit information and avoiding errors in ontologies, reasoning services are necessary. However, the available reasoning methods suffer from scalability problems as the size of ontologies keeps growing.
This thesis investigates a distributed reasoning method that improves scalability by splitting a reasoning process into a set of largely independent subprocesses. In contrast to most description logic reasoners, the proposed approach is based on resolution calculi. We prove that the method is sound and complete for first order logic and different description logic subsets. Evaluation of the implementation shows a heavy decrease of runtime compared to reasoning on a single machine. Hence, the increased computation power pays off the overhead caused by distribution. Dependencies between subprocesses can be kept low enough to allow efficient distribution.
Furthermore, we investigate and compare different algorithms for computing the distribution of axioms and provide an optimization of the distributed reasoning method that improves workload balance in a dynamic setting
The Conflict Notion and its Static Detection: a Formal Survey
The notion of policy is widely used to enable a flexible control of many systems: access control, privacy, accountability, data base, service, contract , network configuration, and so on. One important feature is to be able to check these policies against contradictions before the enforcement step. This is the problem of the conflict detection which can be done at different steps and with different approaches. This paper presents a review of the principles for conflict detection in related security policy languages. The policy languages, the notions of conflict and the means to detect conflicts are various, hence it is difficult to compare the different principles. We propose an analysis and a comparison of the five static detection principles we found in reviewing more than forty papers of the literature. To make the comparison easier we develop a logical model with four syntactic types of systems covering most of the literature examples. We provide a semantic classification of the conflict notions and thus, we are able to relate the detection principles, the syntactic types and the semantic classification. Our comparison shows the exact link between logical consistency and the conflict notions, and that some detection principles are subject to weaknesses if not used with the right conditions
Incremental query answering over semantic contextual information
Master'sMASTER OF SCIENC
Reasoning-Supported Quality Assurance for Knowledge Bases
The increasing application of ontology reuse and automated knowledge acquisition tools in ontology engineering brings about a shift of development efforts from knowledge modeling towards quality assurance. Despite the high practical importance, there has been a substantial lack of support for ensuring semantic accuracy and conciseness. In this thesis, we make a significant step forward in ontology engineering by developing a support for two such essential quality assurance activities
Knowledge-based Biomedical Data Science 2019
Knowledge-based biomedical data science (KBDS) involves the design and
implementation of computer systems that act as if they knew about biomedicine.
Such systems depend on formally represented knowledge in computer systems,
often in the form of knowledge graphs. Here we survey the progress in the last
year in systems that use formally represented knowledge to address data science
problems in both clinical and biological domains, as well as on approaches for
creating knowledge graphs. Major themes include the relationships between
knowledge graphs and machine learning, the use of natural language processing,
and the expansion of knowledge-based approaches to novel domains, such as
Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages
with 3 table
- …