9 research outputs found
Improving Term Extraction with Terminological Resources
Studies of different term extractors on a corpus of the biomedical domain
revealed decreasing performances when applied to highly technical texts. The
difficulty or impossibility of customising them to new domains is an additional
limitation. In this paper, we propose to use external terminologies to
influence generic linguistic data in order to augment the quality of the
extraction. The tool we implemented exploits testified terms at different steps
of the process: chunking, parsing and extraction of term candidates.
Experiments reported here show that, using this method, more term candidates
can be acquired with a higher level of reliability. We further describe the
extraction process involving endogenous disambiguation implemented in the term
extractor YaTeA
Algorithm for Adapting Cases Represented in a Tractable Description Logic
Case-based reasoning (CBR) based on description logics (DLs) has gained a lot
of attention lately. Adaptation is a basic task in the CBR inference that can
be modeled as the knowledge base revision problem and solved in propositional
logic. However, in DLs, it is still a challenge problem since existing revision
operators only work well for strictly restricted DLs of the \emph{DL-Lite}
family, and it is difficult to design a revision algorithm which is
syntax-independent and fine-grained. In this paper, we present a new method for
adaptation based on the DL . Following the idea of
adaptation as revision, we firstly extend the logical basis for describing
cases from propositional logic to the DL , and present a
formalism for adaptation based on . Then we present an
adaptation algorithm for this formalism and demonstrate that our algorithm is
syntax-independent and fine-grained. Our work provides a logical basis for
adaptation in CBR systems where cases and domain knowledge are described by the
tractable DL .Comment: 21 pages. ICCBR 201
Beyond representing orthology relations by trees
Reconstructing the evolutionary past of a family of genes is an important aspect of many genomic studies. To help with this, simple relations on a set of sequences called orthology relations may be employed. In addition to being interesting from a practical point of view they are also attractive from a theoretical perspective in that e.\,g.\,a characterization is known for when such a relation is representable by a certain type of phylogenetic tree. For an orthology relation inferred from real biological data it is however generally too much to hope for that it satisfies that characterization. Rather than trying to correct the data in some way or another which has its own drawbacks, as an alternative, we propose to represent an orthology relation in terms of a structure more general than a phylogenetic tree called a phylogenetic network. To compute such a network in the form of a level-1 representation for , we formalize an orthology relation in terms of the novel concept of a symbolic 3- dissimilarity which is motivated by the biological concept of a ``cluster of orthologous groups'', or COG for short. For such maps which assign symbols rather that real values to elements, we introduce the novel {\sc Network-Popping} algorithm which has several attractive properties. In addition, we characterize an orthology relation on some set that has a level-1 representation in terms of eight natural properties for as well as in terms of level-1 representations of orthology relations on certain subsets of
Clustering Multi-Represented Objects with Noise
Traditional clustering algorithms are based on one representation space, usually a vector space. However, in a variety of modern applications, multiple representations exist for each object. Molecules for example are characterized by an amino acid sequence, a secondary structure and a 3D representation. In this paper, we present an e#cient density-based approach to cluster such multi-represented data, taking all available representations into account. We propose two di#erent techniques to combine the information of all available representations dependent on the application. The evaluation part shows that our approach is superior to existing techniques
Lysosomal Proteome and Transcriptome
LĂĽbke T, Landgrebe J. Lysosomal Proteome and Transcriptome. In: Saftig P, ed. Lysosomes. Georgetown, Texas, USA: Landes Bioscience; 2005: 130-143