Search (5 results, page 1 of 1)

Did you mean:
lcsh's%3a%22Statistics %2f graphic methods%22 5
lcsh's%3a%22Statistics %2f graphics methods%22 5
lcshs%3a%22Statistics %2f graphic methods%22 5
lcsh's%3a%22Statistics %2f graphik methods%22 5
lcshs%3a%22Statistics %2f graphics methods%22 5

Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.14

0.14355062 = product of:
  0.28710124 = sum of:
    0.07177531 = product of:
      0.21532592 = sum of:
        0.21532592 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
          0.21532592 = score(doc=862,freq=2.0), product of:
            0.38312992 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.045191016 = queryNorm
            0.56201804 = fieldWeight in 862, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=862)
      0.33333334 = coord(1/3)
    0.21532592 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
      0.21532592 = score(doc=862,freq=2.0), product of:
        0.38312992 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.045191016 = queryNorm
        0.56201804 = fieldWeight in 862, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=862)
  0.5 = coord(2/4)

Source: https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.02
```
0.01774153 = product of:
  0.07096612 = sum of:
    0.07096612 = sum of:
      0.040352322 = weight(_text_:methods in 1171) [ClassicSimilarity], result of:
        0.040352322 = score(doc=1171,freq=2.0), product of:
          0.18168657 = queryWeight, product of:
            4.0204134 = idf(docFreq=2156, maxDocs=44218)
            0.045191016 = queryNorm
          0.22209854 = fieldWeight in 1171, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.0204134 = idf(docFreq=2156, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1171)
      0.030613795 = weight(_text_:22 in 1171) [ClassicSimilarity], result of:
        0.030613795 = score(doc=1171,freq=2.0), product of:
          0.15825124 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.045191016 = queryNorm
          0.19345059 = fieldWeight in 1171, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1171)
  0.25 = coord(1/4)
```
Abstract

Logical rules are essential for uncovering the logical connections between relations, which could improve the reasoning performance and provide interpretable results on knowledge graphs (KGs). Although there have been many efforts to mine meaningful logical rules over KGs, existing methods suffer from the computationally intensive searches over the rule space and a lack of scalability for large-scale KGs. Besides, they often ignore the semantics of relations which is crucial for uncovering logical connections. Recently, large language models (LLMs) have shown impressive performance in the field of natural language processing and various applications, owing to their emergent ability and generalizability. In this paper, we propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs to prompt LLMs to generate logical rules. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs. Last, a rule validator harnesses the reasoning ability of LLMs to validate the logical correctness of ranked rules through chain-of-thought reasoning. ChatRule is evaluated on four large-scale KGs, w.r.t. different rule quality metrics and downstream tasks, showing the effectiveness and scalability of our method.

Date

23.11.2023 19:07:22

Großjohann, K.: Gathering-, Harvesting-, Suchmaschinen (1996) 0.01

0.012988334 = product of:
  0.051953334 = sum of:
    0.051953334 = product of:
      0.10390667 = sum of:
        0.10390667 = weight(_text_:22 in 3227) [ClassicSimilarity], result of:
          0.10390667 = score(doc=3227,freq=4.0), product of:
            0.15825124 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045191016 = queryNorm
            0.6565931 = fieldWeight in 3227, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=3227)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date: 7. 2.1996 22:38:41
Pages: 22 S

Robertson, S.E.: OKAPI at TREC-3 (1995) 0.01
```
0.009986691 = product of:
  0.039946765 = sum of:
    0.039946765 = product of:
      0.07989353 = sum of:
        0.07989353 = weight(_text_:methods in 5694) [ClassicSimilarity], result of:
          0.07989353 = score(doc=5694,freq=4.0), product of:
            0.18168657 = queryWeight, product of:
              4.0204134 = idf(docFreq=2156, maxDocs=44218)
              0.045191016 = queryNorm
            0.43973273 = fieldWeight in 5694, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.0204134 = idf(docFreq=2156, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5694)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
```
Abstract

Reports text information retrieval experiments performed as part of the 3 rd round of Text Retrieval Conferences (TREC) using the Okapi online catalogue system at City University, UK. The emphasis in TREC-3 was: further refinement of term weighting functions; an investigation of run time passage determination and searching; expansion of ad hoc queries by terms extracted from the top documents retrieved by a trial search; new methods for choosing query expansion terms after relevance feedback, now split into methods of ranking terms prior to selection and subsequent selection procedures; and the development of a user interface procedure within the new TREC interactive search framework

Wätjen, H.-J.: Mensch oder Maschine? : Auswahl und Erschließung vonm Informationsressourcen im Internet (1996) 0.01

0.0076534487 = product of:
  0.030613795 = sum of:
    0.030613795 = product of:
      0.06122759 = sum of:
        0.06122759 = weight(_text_:22 in 3161) [ClassicSimilarity], result of:
          0.06122759 = score(doc=3161,freq=2.0), product of:
            0.15825124 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045191016 = queryNorm
            0.38690117 = fieldWeight in 3161, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3161)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date: 2. 2.1996 15:40:22

Search (5 results, page 1 of 1)

Authors

Years

Languages

Themes