KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor

Ohsawa, Yukio; Benson, Nels; Yachida, Masahiko

doi:10.1109/adl.1998.670375

Cited by 276 publications

(156 citation statements)

References 10 publications

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“…Tf-idf for term t in document d is then the product tf-idf(t, d) = tf(t, d) · idf(t). Tf-idf and other methods to extract keywords (e.g., Keygraph [13]) have been highly successful in that task. However, they do not attempt to highlight associations between terms.…”

Section: Related Workmentioning

confidence: 99%

Discovery of Novel Term Associations in a Document Collection

Hynönen

Mahler

Toivonen

2012

Bisociative Knowledge Discovery

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Abstract. We propose a method to mine novel, document-specific associations between terms in a collection of unstructured documents. We believe that documents are often best described by the relationships they establish. This is also evidenced by the popularity of conceptual maps, mind maps, and other similar methodologies to organize and summarize information. Our goal is to discover term relationships that can be used to construct conceptual maps or so called BisoNets.The model we propose, tpf-idf-tpu, looks for pairs of terms that are associated in an individual document. It considers three aspects, two of which have been generalized from tf-idf to term pairs: term pair frequency (tpf; importance for the document), inverse document frequency (idf; uniqueness in the collection), and term pair uncorrelation (tpu; independence of the terms). The last component is needed to filter out statistically dependent pairs that are not likely to be considered novel or interesting by the user.We present experimental results on two collections of documents: one extracted from Wikipedia, and one containing text mining articles with manually assigned term associations. The results indicate that the tpfidf-tpu method can discover novel associations, that they are different from just taking pairs of tf-idf keywords, and that they match better the subjective associations of a reader.

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Section: Related Workmentioning

confidence: 99%

Discovery of Novel Term Associations in a Document Collection

Hynönen

Mahler

Toivonen

2012

Bisociative Knowledge Discovery

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“…is defined as (Ohsawa, Benson, & Yachida 1999). In addition, we also consider the following factors:…”

Section: The Algorithm Of Paimentioning

confidence: 99%

“…In reading a document, the author's main point is interpreted by activating ( ¤ £ 2 ¥ § in turn. We construct the association between terms in each segment by calculating the co-occurrence of the terms proposed in (Ohsawa, Benson, & Yachida 1999). The algorithm is based on the assumption that associated terms tend to occur within the same sentence.…”

Section: Activation Networkmentioning

confidence: 99%

“…However, in order to retrieve documents matching users' specific and unique interests, the traditional methods of approach mentioned above are insufficient in that they often disregard the author's specific and original point (Ohsawa, Benson, & Yachida 1999). KeyGraph (Ohsawa, Benson, & Yachida 1999) focuses on extracting keywords representing the asserted main point in a document. The strategy is that the author's main point is based on the fundamental concepts represented by the cooccurrence between frequent terms in a document.…”

Section: Introductionmentioning

confidence: 99%

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PAI: Automatic indexing for extracting asserted keywords from a document

Matsumura

Ohsawa

Ishizuka

2003

NGCO

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“…Focusing on WWW, Matsumura et al [8] discovered promising new topics on the web by visualizing new combinations of communities sharing common topics. Ohsawa et al [11] proposed KeyGraph, which is an algorithm for extracting assertions based on co-occurrence graph of terms from textual data. KeyGraph visualizes the relations between assertions and foundations to help us understand potential needs or demands.…”

Section: Discovery Of Future Directionsmentioning

confidence: 99%

Future Directions of Communities on the Web

Matsumura

Ohsawa

Ishizuka

2001

New Frontiers in Artificial Intelligence

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Abstract.Discovering new topics which cover new items, problems, and ideas (e.g., mobile phone, global warming, human genome project, etc) is truly profitable, important, and interesting for us. For instance, 1. Companies producing 'mobile phones' have made large profits by the great sales, 2. The awareness of 'global warming' has improved the environment of the earth by regulating exhaust emissions, 3. Fatal illnesses might be conquered by the human genome project. However, since we cannot completely decode the world surrounding us, we cannot know the topics and their mechanisms in advance. Considering this situation, these phenomena could be a big chance for our activities. In this paper, we describe our approach for discovering the future directions of communities on the web to detect chances.

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KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor

Cited by 276 publications

References 10 publications

Discovery of Novel Term Associations in a Document Collection

Discovery of Novel Term Associations in a Document Collection

PAI: Automatic indexing for extracting asserted keywords from a document

Future Directions of Communities on the Web

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