This paper presents the Open Source tool myCBR which has been re-implemented as standalone application with a designated application programming interface that can be used as plug-in for various applications. We will introduce how knowledge according to Richter’s knowledge containers can be modeled and how myCBR has been successfully applied within various applications. Especially we introduce novel features of myCBR that support knowledge engineers developing more comprehensive applications making use of existing knowledge such as Linked Data or User Generated Content. The applications presented in this paper present the high variety how CBR can be applied for web-based and mobile technologies as well as configuration, diagnostic or decision support tasks.

Developing Case-Based Reasoning Applications Using myCBR 3

Due to the huge amount of text data in the WWW, annotating unstructured text with semantic markup is a crucial topic in Semantic Web research. This work formally analyzes the incorporation of domain ontologies into information extraction tasks in iDocument. Ontologybased information extraction exploits domain ontologies with formalized and structured domain knowledge for extracting domain-relevant information from un-annotated and unstructured text. iDocument provides a pipeline architecture, an extraction template interface and the ability of exchanging domain ontologies for performing information extraction tasks. This work outlines iDocument's ontology-based architecture, the use of SPARQL queries as extraction templates and an evaluation of iDocument in an automatic document annotation scenario.

iDocument: using ontologies for extracting and annotating information from unstructured text

In this paper, we address the problem of extracting relational information from the Web at a large scale. In particular we present a bootstrapping approach to relation extraction which starts with a few seed tuples of the target relation and induces patterns which can be used to extract further tuples. Our contribution in this paper lies in the formulation of the pattern induction task as a well-known machine learning problem, i.e. the one of determining frequent itemsets on the basis of a set of transactions representing patterns. The formulation of the extraction problem as the task of mining frequent itemsets is not only elegant, but also speeds up the pattern induction step considerably with respect to previous implementations of the bootstrapping procedure. We evaluate our approach in terms of standard measures with respect to seven datasets of varying size and complexity. In particular, by analyzing the extraction rate (extracted tuples per time) we show that our approach reduces the pattern induction complexity from quadratic to linear (in the size of the occurrences to be generalized), while mantaining extraction quality at similar (or even marginally better) levels.

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Scaling up Pattern Induction for Web Relation Extraction through Frequent Itemset Mining

This article reports on ontology use for an automatic summarization that "goes the human way". The idea behind it is that human summary users can comprehend and integrate automatic summaries more easily if they and the automatic summarizer share summarization principles and practices. Our currentfirst real-world application is in bone marrow transplantation (BMT). In the core of the SummIt-BMT system, a domain ontology in a MySQL database provides knowledge for human users and system components. SummIt-BMT supports query formulation through an empirically founded scenario interface. Incoming retrieval results are pre-selected by a text retrieval component and submitted to agents reflecting summarization strategies of competent humans. The agents choose from the text passage retrieval result the sentences that best fit the user question as evidenced by ontology propositions occurring in them. The relevant text clips are entered into the answer version of the question scenario and presented with links to their home positions in the source documents. Summarization and information extraction is ontology-based. It uses the relatively well-defined concepts for objects and properties and finds evidence for relations between them with the help of paraphrases. Discussion concentrates on the ontology and its use for information extraction and question answering / summarization. The system agents are heavy users of the ontology. They typically fetch and combine different types of knowledge from the ontology database: concepts, propositions and their semanto-syntactic schemes, unifiers, paraphrases and query scenario forms. The main achievement of the agents is to keep only text retrieval results that meet user question propositions not only by individual concepts, but also by related units corresponding to phrases or sentences. Our first results are presented in the final section of the paper. They are not yet excellent, but quite good for a start-up team of agents and an ontology that is open for improvement.

Ulrike Ulbricht

Papers

Wissen gewinnen durch Wissen : Ontologiebasierte Informationsextraktion