9,008 research outputs found
Ontology-based Document Spanning Systems for Information Extraction
Information Extraction (IE) is the task of automatically organizing in a structured form data extracted from free text documents. In several contexts, it is often desirable that extracted data are then organized according to an ontology, which provides a formal and conceptual representation of the domain of interest. Ontologies allow for a better data interpretation, as well as for their semantic integration with other information, as in Ontology-based Data Access (OBDA), a popular declarative framework for data management where an ontology is connected to a data layer through mappings. However, the data layer considered so far in OBDA has consisted essentially of relational databases, and how to declaratively couple an ontology with unstructured data sources is still unexplored.
By leveraging the recent study on document spanners for rule-based IE by Fagin et al., in this paper we propose a new framework that allows to map text documents to ontologies, in the spirit of OBDA. We investigate the problem of answering conjunctive queries in this framework. For ontologies specified in the Description Logics DL-LiteR and DL-LiteF , we show that the problem is polynomial in the size of the underlying documents. We also provide algorithms to solve query answering by rewriting the input query on the basis of the ontology and its mapping towards the source documents. Through these techniques we pursue a virtual approach, similar to that typically adopted in OBDA, which allows us to answer a query without having to first populate the entire ontology. Interestingly, for DL-LiteR both the spanners used in the mapping and the one computed by the rewriting algorithm belong to the same class of expressiveness. This holds also for DL-LiteF , modulo some limitations on the form of the mapping. These results say that in these cases our framework can be easily implemented by decoupling ontology management and document access, which can be delegated to an external IE system able to compute the extraction rules we use in the mapping
Exploring The Value Of Folksonomies For Creating Semantic Metadata
Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources
Some Reflections on the Task of Content Determination in the Context of Multi-Document Summarization of Evolving Events
Despite its importance, the task of summarizing evolving events has received
small attention by researchers in the field of multi-document summariztion. In
a previous paper (Afantenos et al. 2007) we have presented a methodology for
the automatic summarization of documents, emitted by multiple sources, which
describe the evolution of an event. At the heart of this methodology lies the
identification of similarities and differences between the various documents,
in two axes: the synchronic and the diachronic. This is achieved by the
introduction of the notion of Synchronic and Diachronic Relations. Those
relations connect the messages that are found in the documents, resulting thus
in a graph which we call grid. Although the creation of the grid completes the
Document Planning phase of a typical NLG architecture, it can be the case that
the number of messages contained in a grid is very large, exceeding thus the
required compression rate. In this paper we provide some initial thoughts on a
probabilistic model which can be applied at the Content Determination stage,
and which tries to alleviate this problem.Comment: 5 pages, 2 figure
Thematic Annotation: extracting concepts out of documents
Contrarily to standard approaches to topic annotation, the technique used in
this work does not centrally rely on some sort of -- possibly statistical --
keyword extraction. In fact, the proposed annotation algorithm uses a large
scale semantic database -- the EDR Electronic Dictionary -- that provides a
concept hierarchy based on hyponym and hypernym relations. This concept
hierarchy is used to generate a synthetic representation of the document by
aggregating the words present in topically homogeneous document segments into a
set of concepts best preserving the document's content.
This new extraction technique uses an unexplored approach to topic selection.
Instead of using semantic similarity measures based on a semantic resource, the
later is processed to extract the part of the conceptual hierarchy relevant to
the document content. Then this conceptual hierarchy is searched to extract the
most relevant set of concepts to represent the topics discussed in the
document. Notice that this algorithm is able to extract generic concepts that
are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure
Automatic annotation of bioinformatics workflows with biomedical ontologies
Legacy scientific workflows, and the services within them, often present
scarce and unstructured (i.e. textual) descriptions. This makes it difficult to
find, share and reuse them, thus dramatically reducing their value to the
community. This paper presents an approach to annotating workflows and their
subcomponents with ontology terms, in an attempt to describe these artifacts in
a structured way. Despite a dearth of even textual descriptions, we
automatically annotated 530 myExperiment bioinformatics-related workflows,
including more than 2600 workflow-associated services, with relevant
ontological terms. Quantitative evaluation of the Information Content of these
terms suggests that, in cases where annotation was possible at all, the
annotation quality was comparable to manually curated bioinformatics resources.Comment: 6th International Symposium on Leveraging Applications (ISoLA 2014
conference), 15 pages, 4 figure
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