263 research outputs found
Ontology Matching: OM-2018: Proceedings of the ISWC Workshop
International audienceno abstrac
Making AI meaningful again
Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s, but this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy
Making AI meaningful again
Artificial intelligence (AI) research enjoyed an initial period of enthusiasm
in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of
frustration when genuinely useful AI applications failed to be forthcoming.
Today, we are experiencing once again a period of enthusiasm, fired above all
by the successes of the technology of deep neural networks or deep machine
learning. In this paper we draw attention to what we take to be serious
problems underlying current views of artificial intelligence encouraged by
these successes, especially in the domain of language processing. We then show
an alternative approach to language-centric AI, in which we identify a role for
philosophy.Comment: 23 pages, 1 Tabl
Semantic Enrichment of Ontology Mappings
Schema and ontology matching play an important part in the field of data integration and semantic web. Given two heterogeneous data sources, meta data matching usually constitutes the first step in the data integration workflow, which refers to the analysis and comparison of two input resources like schemas or ontologies. The result is a list of correspondences between the two schemas or ontologies, which is often called mapping or alignment. Many tools and research approaches have been proposed to automatically determine those correspondences. However, most match tools do not provide any information about the relation type that holds between matching concepts, for the simple but important reason that most common match strategies are too simple and heuristic to allow any sophisticated relation type determination.
Knowing the specific type holding between two concepts, e.g., whether they are in an equality, subsumption (is-a) or part-of relation, is very important for advanced data integration tasks, such as ontology merging or ontology evolution. It is also very important for mappings in the biological or biomedical domain, where is-a and part-of relations may exceed the number of equality correspondences by far. Such more expressive mappings allow much better integration results and have scarcely been in the focus of research so far.
In this doctoral thesis, the determination of the correspondence types in a given mapping is the focus of interest, which is referred to as semantic mapping enrichment. We introduce and present the mapping enrichment tool STROMA, which obtains a pre-calculated schema or ontology mapping and for each correspondence determines a semantic relation type. In contrast to previous approaches, we will strongly focus on linguistic laws and linguistic insights. By and large, linguistics is the key for precise matching and for the determination of relation types. We will introduce various strategies that make use of these linguistic laws and are able to calculate the semantic type between two matching concepts. The observations and insights gained from this research go far beyond the field of mapping enrichment and can be also applied to schema and ontology matching in general.
Since generic strategies have certain limits and may not be able to determine the relation type between more complex concepts, like a laptop and a personal computer, background knowledge plays an important role in this research as well. For example, a thesaurus can help to recognize that these two concepts are in an is-a relation. We will show how background knowledge can be effectively used in this instance, how it is possible to draw conclusions even if a concept is not contained in it, how the relation types in complex paths can be resolved and how time complexity can be reduced by a so-called bidirectional search. The developed techniques go far beyond the background knowledge exploitation of previous approaches, and are now part of the semantic repository SemRep, a flexible and extendable system that combines different lexicographic resources.
Further on, we will show how additional lexicographic resources can be developed automatically by parsing Wikipedia articles. The proposed Wikipedia relation extraction approach yields some millions of additional relations, which constitute significant additional knowledge for mapping enrichment. The extracted relations were also added to SemRep, which thus became a comprehensive background knowledge resource. To augment the quality of the repository, different techniques were used to discover and delete irrelevant semantic relations.
We could show in several experiments that STROMA obtains very good results w.r.t. relation type detection. In a comparative evaluation, it was able to achieve considerably better results than related applications. This corroborates the overall usefulness and strengths of the implemented strategies, which were developed with particular emphasis on the principles and laws of linguistics
Proceedings of The Tenth International Workshop on Ontology Matching (OM-2015)
shvaiko2016aInternational audienceno abstrac
Domain-aware ontology matching
During the last years, technological advances have created new ways of
communication, which have motivated governments, companies and institutions
to digitalise the data they have in order to make it accessible and transferable to
other people. Despite the millions of digital resources that are currently available,
their diversity and heterogeneous knowledge representation make complex the
process of exchanging information automatically. Nowadays, the way of tackling
this heterogeneity is by applying ontology matching techniques with the aim of
finding correspondences between the elements represented in different resources.
These approaches work well in some cases, but in scenarios when there are
resources from many different areas of expertise (e.g. emergency response) or
when the knowledge represented is very specialised (e.g. medical domain), their
performance drops because matchers cannot find correspondences or find incorrect
ones.
In our research, we have focused on tackling these problems by allowing
matchers to take advantage of domain-knowledge. Firstly, we present an
innovative perspective for dealing with domain-knowledge by considering three
different dimensions (specificity - degree of specialisation -, linguistic structure -
the role of lexicon and grammar -, and type of knowledge resource - regarding
generation methodologies). Secondly, domain-resources are classified according
to the combination of these three dimensions. Finally, there are proposed several
approaches that exploit each dimension of domain-knowledge for enhancing
matchers’ performance. The proposals have been evaluated by matching two
of the most used classifications of diseases (ICD-10 and DSM-5), and the results
show that matchers considerably improve their performance in terms of f-measure.
The research detailed in this thesis can be used as a starting point to delve into
the area of domain-knowledge matching. For this reason, we have also included
several research lines that can be followed in the future to enhance the proposed
approaches
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