117458795
Semantic Smoothing of Document Models for Agglomerative Clustering
In this paper, we argue that the agglomerative clustering with vector cosine similarity measure performs poorly due to two reasons. First, the nearest neighbors of a document belong to different classes in many cases since any pair of documents shares lots of "general" words. Second, the sparsity of class-specific "core" words leads to grouping documents with the same class labels into different clusters. Both problems can be resolved by suitable smoothing of document model and using Kullback-Leibler divergence of two smoothed models as pairwise document distances. Inspired by the recent work in information retrieval, we propose a novel context-sensitive semantic smoothing method that can automatically identifies multiword phrases in a document and then statistically map phrases to individual document terms. We evaluate the new model-based similarity measure on three datasets using complete linkage criterion for agglomerative clustering and find out it significantly improves the clustering quality over the traditional vector cosine measure.
14856827
Relation-Based Document Retrieval for Biomedical IR
In this paper, we explore the use of term relations in information retrieval for precision-focused biomedical literature search. A relation is defined as a pair of two terms which are semantically and syntactically related to each other. Unlike the traditional “bag-of-word” model for documents, our model represents a document by a set of sense-disambiguated terms and their binary relations. Since document level co-occurrence of two terms, in many cases, does not mean this document addresses their relationships, the direct use of relation may improve the precision of very specific search, e.g. searching documents that mention genes regulated by Smad4. For this purpose, we develop a generic ontology-based approach to extract terms and their relations, and present a betweenness centrality based approach to rank retrieved documents. A prototyped IR system supporting relation-based search is then built for Medline abstract search. We use this novel IR system to improve the retrieval result of all official runs in TREC-2004 Genomics Track. The experiment shows promising performance of relation-based IR. The average P@100 (the precision of top 100 documents) for 50 topics is significantly raised from 26.37 %( the P@100 of the best run is 42.10%) to 53.69% while the MAP (mean average precision) is kept at an above-average level of 26.59%. The experiment also shows the expressiveness of relations for the representation of information needs, especially in the area of biomedical literature full of various biological relations.
5785584
Semantic Smoothing for Model-based Document Clustering
A document is often full of class-independent "general" words and short of class-specific "core " words, which leads to the difficulty of document clustering. We argue that both problems will be relieved after suitable smoothing of document models in agglomerative approaches and of cluster models in partitional approaches, and hence improve clustering quality. To the best of our knowledge, most model-based clustering approaches use Laplacian smoothing to prevent zero probability while most similarity-based approaches employ the heuristic TF*IDF scheme to discount the effect of "general" words. Inspired by a series of statistical translation language model for text retrieval, we propose in this paper a novel smoothing method referred to as context-sensitive semantic smoothing for document clustering purpose. The comparative experiment on three datasets shows that model-based clustering approaches with semantic smoothing is effective in improving cluster quality.
14024342
A Semantic Approach for Mining Hidden Links from Complementary and Non-interactive Biomedical Literature
Two complementary and non-interactive literature sets of articles, when they are considered together, can reveal useful information of scientific interest not apparent in either of the two sets alone. Swanson called the existence of such hidden links as undiscovered public knowledge (UPK). The novel connection between Raynaud disease and fish oils was uncovered from complementary and non-interactive biomedical literature by Swanson in 1986. Since then, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections. This paper proposes a semantic-based mining model for undiscovered public knowledge using the biomedical literature. Our method replaces manual ad-hoc pruning by using semantic knowledge from the biomedical ontologies. Using the semantic types and semantic relationships of the biomedical concepts, our prototype system can identify the relevant concepts collected from Medline and generate the novel hypothesis between these concepts. The system successfully replicates Swanson’s two famous discoveries: Raynaud disease/fish oils and migraine/magnesium. Compared with previous approaches such as LSI-based and traditional association rule-based methods, our method generates much fewer but more relevant novel hypotheses, and requires much less human intervention in the discovery procedure
9003534
A Language Modeling Text Mining Approach to the Annotation of Protein Community
This paper discusses an ontology based language modeling text mining approach to the annotation of protein community. Communities appear to play an important role in the functional properties of complex networks. Being able to annotate the identified the community structure in a biological network can help us to understand better the structure and dynamics of biological systems. Traditional method such as gene ontology (GO) provides information about the functionality of gene products, but they are not enough to annotate community as for only limited number of proteins in the database, limited protein properties available for annotation and the inability to annotate a group of gene products as a whole. Thus, we present an ontology based mixture language model approach to annotate protein community. Compared to traditional method, we have the following three advantages. First, biomedical literature mining brings much richer information than existed gene databases. Second, the mixture language model can help "purify" the document by eliminating some background noise. Third, using domain ontology, we extract biological concept and concept pairs from abstracts. Biological concept is more meaningful than word or multi-word phrases. Moreover, using concept pairs can deliver much more information and serve as evidence of annotation results. We test our approach on four communities SAGA-SRB, CCR-NOT, RFC and ARP2/3, detected from dataset of interactions for Saccharomyces cerevisae from the general repository for interaction datasets (GRID). Annotation results provide a very coherent indication of functionality of each community
12113641
Neighbor number, valley seeking and clustering
This paper proposes a novel nonparametric clustering algorithm capable of identifying shape-free clusters. This algorithm is based on a nonparametric estimation of the normalized density derivative (NDD) and the local convexity of the density distribution function, both of which are represented in a very concise form in terms of neighbor numbers. We use NDD to measure the dissimilarity between each pair of observations in a local neighborhood and to build a connectivity graph. Combined with the local convexity, this similarity measure can detect observations in local minima (valleys) of the density function, which separate observations in different major clusters. We demonstrate that this algorithm has a close relationship with the single-linkage hierarchical clustering and can be viewed as its extension. The performance of the algorithm is tested with both synthetic and real datasets. An example of color image segmentation is also given. Comparisons with several representative existing algorithms show that the proposed method can robustly identify major clusters even when there are complex configurations and/or large overlaps.
11479391
Virtual Workspaces in the Grid
Despite significant progress in the development of Grid infrastructure, the provisioning of a customized and controllable remote execution environment remains an open issue. This paper introduces the concept of a virtual workspace, a configurable execution environment that can be created and managed as a first-class entity to reflect client requirements. Such workspaces can be dynamically deployed on a variety of resources decoupling the notion of environment and resource. We show how virtual workspaces fit into the Grid architecture, present an example implementation using virtual machines, and discuss our initial experiences using this system in practice and with applications.
3575990
Pruning dynamic slices with confidence
Given an incorrect value produced during a failed program run (e.g., a wrong output value or a value that causes the program to crash), the backward dynamic slice of the value very frequently captures the faulty code responsible for producing the incorrect value. Although the dynamic slice often contains only a small percentage of the statements executed during the failed program run, the dynamic slice can still be large and thus considerable effort may be required by the programmer to locate the faulty code.In this paper we develop a strategy for pruning the dynamic slice to identify a subset of statements in the dynamic slice that are likely responsible for producing the incorrect value. We observe that some of the statements used in computing the incorrect value may also have been involved in computing correct values (e.g., a value produced by a statement in the dynamic slice of the incorrect value may also have been used in computing a correct output value prior to the incorrect value). For each such executed statement in the dynamic slice, using the value profiles of the executed statements, we compute a confidence value ranging from 0 to 1 - a higher confidence value corresponds to greater likelihood that the execution of the statement produced a correct value. Given a failed run involving execution of a single error, we demonstrate that the pruning of a dynamic slice by excluding only the statements with the confidence value of 1 is highly effective in reducing the size of the dynamic slice while retaining the faulty code in the slice. Our experiments show that the number of distinct statements in a pruned dynamic slice are 1.79 to 190.57 times less than the full dynamic slice. Confidence values also prioritize the statements in the dynamic slice according to the likelihood of them being faulty. We show that examining the statements in the order of increasing confidence values is an effective strategy for reducing the effort of fault location.
620811
Using data mining techniques to learn layouts of flat-file biological datasets
One of the major problems in biological data integration is that many data sources are stored as atlasses, with a variety of different layouts. Integrating data from such sources can be an extremely time-consuming task. We have been developing data mining techniques to help learn the layout of a dataset in a semi-automatic way. In this paper, we focus on the problem of identifying delimiters for optional fields. Since these fields do not occur in every record, frequency based methods are not able to identify the corresponding delimiters. We present a method which uses contrast analysis on the frequency of sequences to identify such delimiters and help complete the layout descriptions. We demonstrate the effectiveness of this technique using three atlasses biological datasets.
30931074
A Distributed Object Database's Basic Design Issues